performance analysis of ieee 802.15.4 based wireless

PERFORMANCE ANALYSIS OF IEEE 802.15.4

BASED WIRELESS SENSOR NETWORKS

Sumudu Wijetunge

A thesis submitted for the degree of

Doctor of Philosophy in Engineering

SCHOOL OF COMPUTING, ENGINEERING AND MATHEMATICS

UNIVERSITY OF WESTERN SYDNEY

AUSTRALIA

November 2013

c©Sumudu Wijetunge, 2013

To my Parents and my beloved Wife

DECLARATION

Date: November 2013

Author: Sumudu Wijetunge

Title: PERFORMANCE ANALYSIS OF IEEE 802.15.4 BASED

WIRELESS SENSOR NETWORKS

Degree: Ph.D.

I certify that the work presented in this thesis is, to the best of my knowledgeand belief, original, except as acknowledged in the text, and that the material has notbeen submitted, either in full or in part, for a degree at this or any other institution.

I certify that I have complied with the rules, requirements, procedures and policy

relating to my higher degree research award of the University of Western Sydney.

Author's Signature

ACKNOWLEDGEMENTS

It is with great pleasure that I express my deepest gratitude to my princi-

pal supervisor, Dr Ranjith Liyanapathirana, for his continuous guidance, advice,

encouragement and support. Without his excellent supervision, this dissertation

would not have been completed.

I extend my sincere gratitude to my co-supervisor, Dr. Upul Gunawardana,

who has been a source of generosity, insight and inspiration for all my eorts

during the candidature. I owe my research achievements to his expert guidance.

I would like to thank to my co-supervisor, Dr. Qi Cheng for his support

and valuable advice. My sincere thanks also go to Dr. Xinqun Zhu for giving

insight about structural health monitoring systems and to Dr. Ravi Ranasinghe

for helping to set up the computer simulation platform.

I gratefully acknowledge the University of Western Sydney for granting me the

UWS International Postgraduate Research Scholarship, which was the primary

source of funding for this research. I also appreciate the travel support given

by the School of Computing, Engineering and Mathematics for my attending

national and international conferences.

I am thankful to all technical, administrative and academic sta of School of

Computing, Engineering and Mathematics who directly or indirectly helped me

during my candidature. My gratitude also goes to all my research colleagues for

their support, encouragement and friendship.

I am always grateful to my beloved parents and sisters for their love and

constant support throughout my life. Finally, I cannot express my thanks enough

to my loving wife, Dr. Pushpika Wijesinghe, who has been the reason for all my

success during last six years. Her understanding throughout these years has

meant more than I could ever imagine.

ABSTRACT

This thesis investigates performance of the IEEE 802.15.4 standard in the

context of wireless sensor networks (WSNs) deployed in pervasive monitoring.

IEEE 802.15.4 has been widely acknowledged as the standard physical (PHY)

and medium access control (MAC) layer specications for WSNs due to its simple

protocol stack and low power operation. Mathematical models and computer

simulations were devised to analyse the IEEE 802.15.4 MAC protocol and to

improve its performance under dierent applications and operational conditions.

First, the beacon-enabled mode of the protocol with acknowledgments (ACKs)

was analysed using a discrete time Markov chain (DTMC). The proposed model

provides a generalised platform to evaluate the impact of dierent network and

MAC layer parameters and erroneous channel conditions on the performance of

the protocol. Second, performance of the non-beacon-enabled mode of the pro-

tocol was investigated both with and without ACKs. The outcomes of these

analyses were then used to compare and contrast the performance of two opera-

tional modes. Third, impact of the presence of hidden nodes on the performance

of IEEE 802.15.4 based networks was examined. Using a DTMC model and net-

work simulations, it was shown that the throughput of the protocol is severely

reduced due to increased transmission failures caused by hidden nodes. Finally,

a new hybrid MAC mechanism was proposed to improve the performance of the

IEEE 802.15.4 standard in the context of recently emerged hybrid monitoring ap-

plications. Simulation based experiments show that the proposed hybrid protocol

not only outperforms the standard protocol, but also provides a reliable, energy

ecient and delay limited transmission mechanism for hybrid monitoring WSNs.

This thesis has provided a platform for further studies on the performance

analysis and application specic tailoring of the IEEE 802.15.4 standard in the

context of WSNs.

Contents

Acknowledgement iii

Abstract iv

Contents v

Abbreviations xi

Notation xiv

List of Figures xvii

List of Tables xxi

1 Introduction 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2 Major Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4 Thesis Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 MAC Protocols for WSNs 12

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2 Wireless MAC Protocols . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.1 Fixed Assignment Mechanism . . . . . . . . . . . . . . . . 13

2.2.2 Demand Assignment Mechanism . . . . . . . . . . . . . . . 14

CONTENTS vi

2.2.3 Random Access Mechanism . . . . . . . . . . . . . . . . . 14

2.3 MAC Protocols for WSNs . . . . . . . . . . . . . . . . . . . . . . 16

2.3.1 Energy Wastage in MAC Layer . . . . . . . . . . . . . . . 16

2.3.2 Schedule based Protocols . . . . . . . . . . . . . . . . . . . 17

2.3.3 Contention based Protocols . . . . . . . . . . . . . . . . . 18

2.4 Overview of IEEE 802.15.4 Standard . . . . . . . . . . . . . . . . 19

2.4.1 Device Types and Network Topologies . . . . . . . . . . . 20

2.4.2 PHY Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.4.3 MAC Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.4.3.1 Beacon-Enabled Mode . . . . . . . . . . . . . . . 24

2.4.3.2 Non-beacon-Enabled Mode . . . . . . . . . . . . 28

2.5 Performance Evaluation of MAC Protocols . . . . . . . . . . . . . 30

2.5.1 Analytical Models . . . . . . . . . . . . . . . . . . . . . . . 30

2.5.2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3 Analysis of Beacon-enabled IEEE 802.15.4 MAC Protocol with

ACK Transmission 35

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.2.1 Approximations . . . . . . . . . . . . . . . . . . . . . . . . 39

3.3 Analytical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.3.1 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 40

3.3.2 Channel State Model . . . . . . . . . . . . . . . . . . . . . 44

3.4 Simplied Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.4.1 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 48


3.5 Extended Model for Networks with Erroneous Channels . . . . . . 50

3.5.1 Channel Error Model . . . . . . . . . . . . . . . . . . . . . 51

CONTENTS vii

3.5.2 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 52

3.5.3 Modied Channel State Model . . . . . . . . . . . . . . . . 52

3.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . 54

3.6.1 Aggregate Network Throughput . . . . . . . . . . . . . . . 54

3.6.2 Average Power Consumption . . . . . . . . . . . . . . . . . 55

3.6.3 Data Transmission Reliability . . . . . . . . . . . . . . . . 57

3.7 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 59

3.7.1 Validation of Analysis . . . . . . . . . . . . . . . . . . . . 59

3.7.2 Eects of Network Parameters . . . . . . . . . . . . . . . . 62

3.7.3 Eects of MAC-Layer Parameters . . . . . . . . . . . . . . 65

3.7.4 Eects of Channel Errors . . . . . . . . . . . . . . . . . . . 70

3.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4 Analysis of Non-beacon-enabled IEEE 802.15.4 MAC Protocol 73

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.2 Collision of Transmissions . . . . . . . . . . . . . . . . . . . . . . 75

4.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.3.1 Approximations . . . . . . . . . . . . . . . . . . . . . . . . 79

4.4 Analytical Model without ACKs . . . . . . . . . . . . . . . . . . . 81

4.4.1 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 81


4.5 Analytical Model with ACKs . . . . . . . . . . . . . . . . . . . . 88

4.5.1 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 88


4.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . 94

4.6.1 Aggregate Network Throughput . . . . . . . . . . . . . . . 95

4.6.2 Average Power Consumption . . . . . . . . . . . . . . . . . 96

4.6.3 Data Transmission Reliability . . . . . . . . . . . . . . . . 97


CONTENTS viii

4.7.1 Validation of Analysis . . . . . . . . . . . . . . . . . . . . 99

4.7.2 Impact of Network and MAC-layer Parameters . . . . . . . 105

4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

5 Throughput Analysis of IEEE 802.15.4 MAC Protocol in the

Presence of Hidden Nodes 111

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

5.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

5.2.1 Node Grouping . . . . . . . . . . . . . . . . . . . . . . . . 115

5.3 Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

5.3.1 Analysis of Individual Groups . . . . . . . . . . . . . . . . 116

5.3.2 Analysis of the Common Channel . . . . . . . . . . . . . . 118

5.4 Simplied Analysis for Networks with Uniform Groups . . . . . . 124

5.5 Throughput Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 124


5.6.1 Validation of Analytical Results . . . . . . . . . . . . . . . 126

5.6.2 Impact of Dierent Network Parameters on Throughput . 127

5.6.3 Approximating Throughput of Generic Networks . . . . . 131

5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

6 IEEE 802.15.4 based MAC Protocol for Hybrid MonitoringWSNs139

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

6.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

6.2.1 QoS Requirements of Hybrid Monitoring Application . . . 143

6.3 New MAC Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . 144

6.3.1 MAC Mechanism for LTPM Data Transmission . . . . . . 144

6.3.2 MAC Mechanism for ED Data Transmission . . . . . . . . 147

6.3.2.1 Improving Data Transmission Reliability of net-

works with synchronised trac . . . . . . . . . . 148

6.4 Hybrid Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

CONTENTS ix

6.4.1 Initial Phase . . . . . . . . . . . . . . . . . . . . . . . . . . 152

6.4.2 Steady Phase . . . . . . . . . . . . . . . . . . . . . . . . . 157


6.5.1 Experiment 1 - Performance Evaluation of the DTS Mech-

anism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

6.5.2 Experiment 2 - Performance Evaluation of the Randomly-

delayed CSMA/CA Mechanism . . . . . . . . . . . . . . . 164

6.5.3 Experiment 3 - Performance Evaluation of the Hybrid Pro-

tocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

6.5.4 Experiment 4 - Network Scalability of the Hybrid Protocol 173

6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176

7 Conclusion 178

7.1 Summary and Conclusion . . . . . . . . . . . . . . . . . . . . . . 178

7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

References 182

Appendices 204

A Steady State Transition Equations of Node Model DTMCs 204

A.1 Beacon-enabled IEEE 802.15.4 - Analytical Model . . . . . . . . . 204

A.2 Beacon-enabled IEEE 802.15.4 - Simplied Model . . . . . . . . . 206

A.3 Non-beacon-enabled IEEE 802.15.4 with ACK Transmission . . . 207

B Fractions of Time Spent by a Node in Dierent Transceiver Ac-

tivities 208

B.1 Analysis of Beacon-enabled IEEE 802.15.4 . . . . . . . . . . . . . 208

B.2 Analysis of Non-beacon-enabled IEEE 802.15.4 . . . . . . . . . . . 209

C Modications to ns-2.34 Simulator 211

CONTENTS x

C.1 Modications to CCA Procedure . . . . . . . . . . . . . . . . . . 211

C.2 Implementation of Hybrid Protocol . . . . . . . . . . . . . . . . . 213

D Supportive Calculations and Algorithm Descriptions 217

D.1 Quantifying δ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

D.2 Algorithms for Networks Deployed Only in LTPM Applications . 220

D.3 Computation of Initialising-Parameters . . . . . . . . . . . . . . . 221

Abbreviations

ns-2 network simulator - 2

ACK acknowledgment

B-MAC Berkeley MAC

BPSK binary phase-shift keying

CAP contention access period

CBR continuous bit rate

CCA clear channel assessment

CDMA code division multiple access

CFP contention free period

COTS commercial-o-the-shelf

CSMA carrier sense multiple access

CSMA/CA carrier sense multiple access/collision avoidance

CSMA/CD carrier sense multiple access/collision detection

CTS clear to send

DCF distributed coordination function

DEE-MAC dynamic energy ecient MAC

DLL data link layer

DSSS direct sequence spread spectrum

DTMC discrete time Markov chain

ABBREVIATIONS xii

DTS dedicated time slots

ED event detection

EPA equilibrium point analysis

FDMA frequency division multiple access

FFD full-function device

GTS guaranteed time slot

HUA hybrid unied-slot access

IFS interframe spacing

LEACH low energy adaptive clustering hierarchy

LLC logical link control

LQI link quality indication

LTPM long-term periodic monitoring

MAC medium access control

MACA multiple access with collision avoidance

MANET mobile ad-hoc network

MEMS micro electromechanical sensors

MH-MAC mobility adaptive hybrid MAC

O-QPSK oest quadrature phase-shift keying

OSI open systems interconnection

PAMAS power aware multi access with signalling

PHY physical

PMAC pattern MAC

PSSS parallel sequence spread spectrum

QoS quality of service

RF radio frequency

RFD reduced-function device

ABBREVIATIONS xiii

RTS request to send

RX-to-TX receive to transmit

S-MAC sensor-MAC

SHM structural health monitoring

SMACS self organising MAC for sensor networks

SSCS service specic convergence sublayer

T-MAC timeout MAC

TDMA time division multiple access

TRAMA trac adaptive medium access

TUA tagged user analysis

WBAN wireless body area network

WLAN wireless local area network

WPAN wireless personal area network

WSN wireless sensor network

Z-MAC zebra MAC

Notation

BEy backo exponent of the yth backo stage

BI beacon interval

BImin−nwk minimum BI value the network can operate

BInwk beacon interval of the network

BO beacon order

Dcsma maximum delay caused by CSMA/CA mechanism

Dmax upper bound of the delay in ED data transmission

Drnd maximum value of the random delay

EDrate event detection rate

K number of non-overlapping groups in the network

L data frame length in backo slots

Lack ACK frame length in backo slots

Led ED data frame length in backo slots

Lltpm LTPM data frame length in backo slots

N number of nodes in the network

Nmax−ltpm maximum number of nodes allowed to associate with the network when

only LTPM application exists

Nmax maximum number of nodes allowed to associate with the network

Pdiscard probability of frame discarding

Perr−data probability of having channel errors during data transmission

NOTATION xv

R reliability of data transmission (or reliability factor)

S aggregate network throughput

SD superframe duration

SO superframe order

Tm monitoring period for a given LTPM instance

Tcycle periodicity of LTPM instances

Tdts DTS length

Tinit initial value of the countdown timer

Trpt reporting cycle of LTPM data

Yav average power consumption of a node

α probability of none of the node begin data transmission

β probability of only one node begins data transmission

δ proportional constant of Drnd

η frame delivery ratio

γ probability of none of the remaining nodes begin data transmission

S fraction of time the channel spent in successful data transmission

λ frame arrival rate

N natural numbers

π(statei) long term proportion of transitions into statei

ρ frame discard ratio

σ probability of more than one node begin data transmission

x maximum number of transmission attempts

y maximum number of backing o stages

d delay constraint in ED data transmission

havg average number of hidden-nodes-per-node of the network

m number of LTPM data frames generated in a node during Tm period

NOTATION xvi

nj number of nodes in Group j

ndts number of DTSs required for a node to transmit LTPM data during Trpt

nltpm number of LTPM data frames transmitted within the DTS

p frame arrival probability

pci|i conditional probability of the channel being idle in the next backo slot

given that it is idle in the current backo slot

pny geomatircal parameter that represents the number of backo slots a node

dwells in the yth backo stage

pnt|ii conditional probability of any node begins transmission given that the

channel has been idle for two consecutive backo slots

pci steady state probability of channel idleness

pnt steady state probability of a node begins transmission

pncca steady state probability of a node begins CCA

q probability of receiving ACK after a data transmission

sdack starting delay of ACK transmission

tack waiting time for a ACK frame

tbcn beacon duration

tltpm transmission duration of a single LTPM data frame (including IFS)

vb transition probability from bad channel state to good channel state

vg transition probability from good channel state to bad channel state

List of Figures

2.1 IEEE 802.15.4 standard within OSI seven-layer model. . . . . . . 20

2.2 IEEE 802.15.4 network topologies: (a) Star (b) Peer-to-peer (c)

Cluster-tree, a complex network based on peer-to-peer topology. . 21

2.3 IEEE 802.15.4 MAC protocol: Channel access mechanisms [62]. . 23

2.4 IEEE 802.15.4 superframe structure. . . . . . . . . . . . . . . . . 25

2.5 IEEE 802.15.4 slotted CSMA/CA mechanism. . . . . . . . . . . . 26

2.6 IEEE 802.15.4 unslotted CSMA/CA mechanism. . . . . . . . . . . 29

3.1 3D-DTMC model of node. Probability pci and pci|i are denoted by

u and v, respectively. . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.2 Discrete-time Markov chain model of channel. . . . . . . . . . . . 45

3.3 Discrete-time Markov chain for node in simplied model. . . . . . 49

3.4 Gilbert-Elliot channel error model. . . . . . . . . . . . . . . . . . 51

3.5 DTMC model for burst error channel. . . . . . . . . . . . . . . . . 53

3.6 Energy states and transitions of CC2420 transceiver [126][149]. . . 56

3.7 Performance of beacon-enabled IEEE 802.15.4 networks with and

without ACK transmission (N = 10 and L = 10 backo slots). . . 60

3.8 Eects of frame length L on the performance of beacon-enabled

IEEE 802.15.4 networks (when N = 10). . . . . . . . . . . . . . . 63

3.9 Eects of number of nodesN on the performance of beacon-enabled

IEEE 802.15.4 networks (when L = 10 backo slots). . . . . . . . 64

LIST OF FIGURES xviii

3.10 Eects of macMaxFrameRetries on the performance of beacon-

enabled IEEE 802.15.4 networks. . . . . . . . . . . . . . . . . . . 66

3.11 Eects of macMaxCSMABackos on the performance of beacon-


3.12 Eects of the backo window length on the performance of beacon-


3.13 Eects of channel errors on the performance of beacon-enabled

IEEE 802.15.4 networks (N = 10 and L = 10 backo slots). . . . . 71

4.1 Collision of transmissions: slotted CSMA/CA vs. unslotted CSMA/CA. 76

4.2 Timing of starting the same event in dierent protocols. . . . . . 80

4.3 DTMC model for node without ACKs. The steady state probabil-

ity pci is denoted by a . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.4 DTMC model for channel without ACKs. . . . . . . . . . . . . . . 85

4.5 DTMC model for node with ACKs. The steady state probability

pci is denoted by a . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.6 DTMC model for channel with ACKs. . . . . . . . . . . . . . . . 91

4.7 Behaviour of basic model probabilities (N = 10 and L = 10 backos).100

4.8 Performance of IEEE 802.15.4 based networks (N = 10 and L =

10): (a) Aggregate network throughput S (b) Average power con-

sumption per node Yav. . . . . . . . . . . . . . . . . . . . . . . . . 102

4.9 Performance of IEEE 802.15.4 based networks (N = 10 and L =

10): (a) Frame discard ratio ρ (b) Frame delivery ratio η. . . . . . 103

4.10 Number of collisions in slotted and unslotted protocols. . . . . . . 104

4.11 Eects of frame length L on the performance of non-beacon-enabled

IEEE 802.15.4 networks without ACKs (N = 10). . . . . . . . . . 106

4.12 Eects of number of nodes N on the performance of non-beacon-

enabled IEEE 802.15.4 networks without ACKs (L = 10 backo

slots). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

LIST OF FIGURES xix

4.13 Eects ofmacMaxFrameRetries on the performance of non-beacon-

enabled IEEE 802.15.4 networks with ACKs. . . . . . . . . . . . . 108

4.14 Eects ofmacMaxCSMABackos on the performance of non-beacon-

enabled IEEE 802.15.4 networks with ACKs. . . . . . . . . . . . . 109

4.15 Eects of the backo window length on the performance of non-

beacon-enabled IEEE 802.15.4 networks with ACKs. . . . . . . . 109

5.1 Hidden node problem in a single-hop star-topology network. . . . 112

5.2 Example network [4,6,8] with node grouping (K = 3). . . . . . . . 115

5.3 DTMC model for the common channel. . . . . . . . . . . . . . . . 119

5.4 Transition from SUCCL to SUCCL−1 state. . . . . . . . . . . . . . 120

5.5 Normalised aggregate network throughput S of dierent networks

with hidden nodes. . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5.6 Normalised aggregate network throughput S for varying network

parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

5.7 Normalised aggregate network throughput S for [4,8,12] network

when dierent groups generate frames at dierent rates. . . . . . . 130

5.8 [8,8] network and its relaxed node grouping congurations. . . . . 132

5.9 [8,8,8] network and its relaxed node grouping congurations. . . . 133

5.10 Normalised aggregate network throughput S of networks with re-

laxed node grouping. . . . . . . . . . . . . . . . . . . . . . . . . . 134

5.11 Dierent congurations of 16-node-network (N = 16) with dier-

ent average number of hidden nodes havg. . . . . . . . . . . . . . . 135

5.12 Dierent congurations of 24-node-network (N = 24) with dier-

ent average number of hidden nodes havg. . . . . . . . . . . . . . . 136

5.13 S of dierent network congurations with varying havg: (a) 16-

node-network (N = 16) and (b) 24-node-network (N = 24). . . . . 136

5.14 Validation of proposed technique. . . . . . . . . . . . . . . . . . . 137

6.1 Hybrid monitoring scenario. . . . . . . . . . . . . . . . . . . . . . 143

LIST OF FIGURES xx

6.2 DTS mechanism. . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

6.3 Hybrid MAC mechanism. . . . . . . . . . . . . . . . . . . . . . . . 151

6.4 SuperBeacon payload. . . . . . . . . . . . . . . . . . . . . . . . . 156

6.5 Reliability in LTPM data transmission. . . . . . . . . . . . . . . . 163

6.6 Power consumption in LTPM data transmission. . . . . . . . . . . 164

6.7 Performance of the randomly-delayed CSMA/CA mechanism in

ED data transmission: (a) reliability R (b) maximum delay. . . . 166

6.8 Data transmission reliability of hybrid protocol: (a) LTPM data

transmission with varying EDrate (b) ED data transmission with

varying m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

6.9 Power consumption of hybrid protocol (m = 2400). . . . . . . . . 171

6.10 Maximum delay in ED data transmission. . . . . . . . . . . . . . 172

6.11 Network scalability of hybrid protocol (in terms of maximum num-

ber of nodes allowed to form the network Nmax). . . . . . . . . . . 175

C.1 Timing of CCA procedure in dierent scenarios. . . . . . . . . . . 212

D.1 Normalised dierence in data transmission reliability ∆R with

varying δ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

List of Tables

2.1 IEEE 802.15.4 PHY layer: Frequency bands and data rates. . . . 22

2.2 IEEE 802.15.4 MAC-layer parameters. . . . . . . . . . . . . . . . 27

3.1 Relationships between model variables and MAC-layer parameters. 43

3.2 States of the CC2420 transceiver: Beacon-enabled mode. . . . . . 56

3.3 MAC-layer parameter values for dierent investigations. . . . . . . 66

4.1 Node states and their dwell times. . . . . . . . . . . . . . . . . . . 82

4.2 States of the CC2420 transceiver: Non-beacon-enabled mode. . . . 96

5.1 Probabilities αj, βj, A, B, C and E for the network shown in

Figure 5.2 when L = 10. . . . . . . . . . . . . . . . . . . . . . . . 118

6.1 Simulation parameters. . . . . . . . . . . . . . . . . . . . . . . . . 161

6.2 Sensor measurements and monitoring application requirements. . 167

6.3 DTS-scheduling parameters Tdts, ndts, and BInwk (when Trpt = 30

min.). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

B.1 Fractions of time spent by a node in dierent transceiver activities

in beacon-enabled networks. . . . . . . . . . . . . . . . . . . . . 209

B.2 Fractions of time spent by a node in dierent transceiver activities

in non-beacon enabled networks. . . . . . . . . . . . . . . . . . . 209

C.1 Impelemetation of hybrid protocol in ns-2 . . . . . . . . . . . . . 214

Chapter 1

Introduction

Sensing, observing and controlling the neighbouring environment has been con-

sidered as one of the key requirements of humans from the beginning of mankind.

Ever increasing demand of this requirement has led to the development of ad-

vanced sensor-actuators to sense and control our surroundings. However, the ex-

istence of complex monitoring applications that cannot be addressed by a single

sophisticated sensor-actuator (e.g., battle eld monitoring, environmental moni-

toring and structural health monitoring) demands a new paradigm for automated

sensing and controlling. With recent advances in electronic and computer engi-

neering, a technology known as Sensor Networks has emerged to form networks

of sensors to fulll the requirements posed by complex monitoring applications

[1].

A sensor network is a group of transducers that monitor and record conditions

of various physical phenomena such as temperature, pressure, wind speed, and

chemical concentration in a given environment. The basic building block of a

sensor network is the sensor node, which comprises of several micro electrome-

chanical sensors (MEMS), a micro computer and a transceiver [2]-[4]. To perform

as a network, sensor nodes should be able to communicate in addition to their

primary tasks of sensing and computing. Currently, most of the sensor networks

utilise existing wired technologies to form the communication network. However,

1. INTRODUCTION 2

manipulating a wired network is cumbersome and inherently associated with the

hassles of installation and maintenance apart from the cost of cabling. On the

other hand, wireless networks can be easily deployed and maintained regardless

of the scale of the network and are cost eective compared with wired networks

[4][5]. These advantages along with the recent technological advances and ever

growing list of potential applications [6]-[9] have paved the way to build sensor

networks that communicate wirelessly.

Wireless operation, despite being the prime strength of wireless sensor net-

works (WSNs), poses new challenges: Latency and security of communication,

fair access to the medium, eective routing and scalability of the network are a

few among others. Moreover, wireless sensor nodes have to rely completely on

built-in energy sources as they cannot be fed externally due to their untethered

nature. Making the situation worse, replacing or renewing the self contained

energy sources in sensor nodes has been generally considered impractical or too

costly [2]-[4]. Therefore, one of the major challenges in WSNs is the scarcity of

energy used for sensing, processing (computation) and communication.

Among these three major functions in wireless sensor nodes, communication

has been considered the largest energy consumer [10]-[13]. According to Karl and

Willig [14] the ratio of energy consumption for communication to computation of

a single bit is about 190. Therefore, energy ecient communication is essential

to achieve the overall energy eciency in WSNs. The obvious solution to achieve

the energy eciency in communication is improving the energy characteristics of

the underlying radio. Apart from that, introducing ecient data transmission

and reception mechanisms to a given radio may also enhance the energy per-

formance of a wireless sensor node signicantly [15]. Thus, the medium access

control (MAC) protocol which manages the data transceiver operation is a vital

contributor for energy ecient operation in WSNs.

In general, the MAC protocols in traditional wireless networks such as wire-

less local area networks (WLANs) and mobile ad-hoc networks (MANETs) focus

1. INTRODUCTION 3

on delivering high channel throughput (i.e., channel utilisation), low latency in

transmissions and fairness in medium access; however, have little or no consider-

ation for energy conservation [10]. In contrast, WSNs require protocols that can

provide the best performance with the minimum energy consumption [11]-[17].

Due to this fundamental dierence and the unique characteristics of WSNs includ-

ing scalability of network and limited resources, the MAC protocols designed for

traditional wireless networks are inapt in the context of WSNs [18]-[21]. There-

fore, there exists a requirement for developing an improved set of MAC protocols

in order to harness the full potential of WSN technology.

Following the introduction of the well-known sensor-MAC (S-MAC) [19], a

wide range of MAC protocols that meet the unique requirements of WSNs has

been developed; WiseMAC [22], DMAC[23], Sift [21] and trac adaptive medium

access (TRAMA) [12] are a few to name. Each of these MAC protocols is spe-

cially tailored for a certain set of WSN applications to deliver their optimum

performance. The application-specic nature of these protocols hinders the inter-

operability among them, and hence creates a huge barrier to the successful com-

mercial launching of WSN technology [24][25]. This raises the necessity of a

standard for the MAC protocols used in WSNs [26].

Due to its simplicity and low power operation, the IEEE 802.15.4 standard

[27], which denes the physical (PHY) and MAC layer specications for low

power, low-data-rate wireless personal area networks (WPANs), has been widely

acknowledged as the state-of-the-art standard for MAC protocols in WSNs. This

standard has already been implemented in most of the commercial sensor nodes

including MicaZ [28], IMote2 [29] and TelosB [30]. It also serves as the basis for

almost all commercial WSNs standards including Zigbee [31], ISA 100.11a [32]

and WirelessHART[33], acclaiming itself as the standard for the PHY/MAC layer

protocols in WSNs.

The IEEE 802.15.4 MAC protocol operates in two dierent modes: the beacon-

enabled mode and the non-beacon-enabled mode. The beacon-enabled mode is

1. INTRODUCTION 4

utilised to deploy synchronised sensor networks, while the non-beacon-enabled

mode is used in non-synchronised sensor networks. Whether it is synchronised or

non-synchronised, performance of a sensor network highly depends on the per-

formance of the underlying MAC protocol. Performance of the IEEE 802.15.4

MAC protocol is generally evaluated using mathematical modelling, computer

simulations and real world experiments. Outcomes of such analyses aid devel-

opers to predict the behaviour of a particular WSN before the deployment, and

hence to minimise time and cost associated with the design and development

process. Furthermore, the insight gained from these investigations would provide

design guidelines for researchers and developers to deploy better WSNs in future.

The aim of this thesis is to develop a platform for such analyses (modelling and

computer simulations) on the performance of the IEEE 802.15.4 MAC protocol.

1.1 Motivation

Performance of the IEEE 802.15.4 MAC protocol has been evaluated by analyt-

ical methods, simulations and experiments. The reliability in data transmission

has not been considered as a critical measure in most of the existing analyses,

and therefore, the protocol has been analysed by overlooking the MAC level ac-

knowledgment (ACK) and frame retransmission. However, existence of reliability

critical WSN applications such as military survivance [7] and chemical agent de-

tection [34] questions the applicability of the existing analyses that overlook ACK

frame transmission. Thus, the lack of knowledge of the performance of the IEEE

802.15.4 MAC protocol with ACK frame transmission under both operational

modes (i.e., the beacon-enabled and non-beacon-enabled) provides a strong mo-

tivation for the subject of this thesis.

Due to its interesting features such as the superframe structure and guaran-

teed time slots (GTSs), the beacon-enabled mode has attracted the attention of

the research community over the non-beacon-enabled mode. Consequently, most

1. INTRODUCTION 5

of the existing analyses are focused on the beacon-enabled mode of the protocol.

However, the non-beacon-enabled mode is equally deployed in practical WSNs

as its counterpart with beacons, and it is the only mode that can facilitate the

decentralised communication occurring in many event monitoring WSNs. Hence,

analysing the non-beacon-enabled mode is an important contribution to the com-

plete performance evaluation of the IEEE 802.15.4 MAC protocol.

Generally, WSNs operate under harsh environments and over a considerably

large geographical area [1][3]. Thus, highly error-prone channels and the presence

of hidden nodes are familiar scenarios for practical WSN deployments. However,

most of the existing analyses appear to overlook these important physical and

system level considerations associated with WSNs and derive performance of

the protocol by assuming ideal conditions. Therefore, lack of studies on the

performance of the IEEE 802.15.4 MAC protocol under non-ideal operational

conditions strongly motivates to carry out this study.

WSNs are deployed mainly in two dierent monitoring scenarios: periodic

monitoring and event detection [7]. In general, a given WSN serves only for one

monitoring scenario but not for both; however, emerging applications with hybrid

monitoring (e.g., structural health monitoring (SHM) [35]) demand WSNs that

can support both monitoring scenarios simultaneously. Given that the IEEE

802.15.4 MAC protocol in many current WSNs has been adjusted for the un-

derlying monitoring application [36]-[38], it would be interesting to modify the

protocol to meet the unique data transmission requirements of emerging hybrid

monitoring WSNs.

Thus, the main research objective of this thesis is to investigate the per-

formance of the IEEE 802.15.4 MAC protocol deployed in dierent WSNs that

operate under real-world conditions and applications.

1. INTRODUCTION 6

1.2 Major Contributions

The work reported in this thesis has resulted in several contributions to the eld

of analysis and design of MAC protocols in the context of WSNs.

The major contributions are as follows:

• A comprehensive review of existing MAC protocols for WSNs and per-

formance evaluation techniques for communications protocols is presented.

MAC layer specications of the IEEE 802.15.4 standard is identied as the

state-of-the-art MAC protocol for WSNs. Markov Chain based analytical

models and network simulator - 2 (ns-2) based simulations are recognised

as potential performance evaluation techniques for the MAC protocols used

in WSNs.

• Performance of the beacon-enabled mode of the IEEE 802.15.4 MAC with

ACK frame transmission is evaluated using a discrete time Markov chain

(DTMC) based model. An extension to the proposed model is presented

to analyse the protocol under non-ideal channel conditions. Generality of

the proposed model is exploited to investigate the impact of dierent net-

work and MAC-layer parameters on the performance of the protocol. The

proposed models are validated using extensive ns-2 simulations.

• Performance of the non-beacon-enabled mode of the IEEE 802.15.4 MAC

protocol is investigated. A Markov chain based analysis is presented to anal-

yse the non-beacon-enabled IEEE 802.15.4 protocol without ACK frame

transmissions. The analysis is then extended to evaluate the performance

of the protocol with ACK frame transmission. Analytical results obtained

from these models and the models proposed for the beacon-enabled mode

are used to compare and contrast the performance of two operation modes

of the IEEE 802.15.4 MAC protocol.

1. INTRODUCTION 7

• Impact of the presence of hidden nodes on the performance of the beacon-

enabled mode of the IEEE 802.15.4 MAC protocol is investigated. A DTMC

based analytical model is proposed to model the wireless channel seen by the

common receiver, and consequently, to derive the aggregate throughput of

the network. Analytical results and simulations reveal a signicant impact

of average number of hidden nodes on the throughput performance of a

given network.

• IEEE 802.15.4 compliant two new MAC mechanisms are proposed to meet

the quality of service (QoS) requirements of dierent monitoring scenarios.

By carefully merging these mechanisms, a hybrid MAC protocol is devel-

oped to transmit data eciently in hybrid monitoring WSNs. The new

hybrid protocol is implemented on ns-2 simulation platform. Simulation

results show that the proposed hybrid protocol outperforms the standard

IEEE 802.15.4 MAC in terms of reliability and energy eciency, while sat-

isfying the stringent delay requirements in data transmission.

1.3 Publications

The following collection of papers, which has been published in, accepted by or

submitted to peer-reviewed journals or conferences, presents the contribution of

this thesis.

1. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Performance

Analysis of IEEE 802.15.4 MAC Protocol with ACK Frame Transmission',

Accepted for publication in the Wireless Personal Communications, ISSN:

1572-834X (electronic version).

2. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Throughput Anal-

ysis of IEEE 802.15.4 MAC Protocol in the Presence of Hidden Nodes',

1. INTRODUCTION 8

Accepted for publication in Wireless Networks, ISSN: 1572-8196 (electronic

version).


Analysis of Non-Beacon-Enabled IEEE 802.15.4 MAC Protocol', Submitted

to Wireless Personal Communications, ISSN: 1572-834X (electronic ver-

sion).

4. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, Àn IEEE 802.15.4

based MAC Protocol for WSNs deployed in Hybrid Monitoring Applica-

tions', Submitted to International Journal of Wireless Information Net-

works, ISSN: 1572-8129 (electronic version).

5. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, Àn IEEE 802.15.4

based hybrid MAC Protocol for Hybrid Monitoring WSNs', in Proceedings

of the 38th IEEE Conference on Local Computer Networks (LCN), Oct.

2013, Sydney, Australia.


ysis of Non-Beacon-Enabled IEEE 802.15.4 Networks with Unsaturated

Trac ', in Proceedings of the 12th International Symposium on Communi-

cations and Information Technologies (ISCIT) 2012, Gold Coast, Australia.

7. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, Ìnvestigation of

Data Transmission Reliability of IEEE 802.15.4 based Wireless Sensor Net-

works with Synchronised Periodic Data', in Proceedings of the International

Conference on Computer and Information Sciences (ICCIS) 2012, Kuala

Lumpur, Malaysia, pp.619-624.

8. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, Ìmpact of MAC

parameters on the performance of IEEE 802.15.4 MAC protocol with ACK

Frame Transmission', in Proceedings of the Australian Telecommunication

1. INTRODUCTION 9

Networks and Applications Conference (ATNAC), Nov. 2011, Melbourne,

Australia. (Won one of the competitive travel grants)


Analysis of IEEE 802.15.4 MAC protocol for WSNs in Burst Error Chan-

nels', in Proceedings of the 11th International Symposium on Communica-

tions and Information Technologies (ISCIT), Oct. 2011, Hangzhou, China,

pp.286-291. (Received the ISCIT2011 Best Paper Award)


ysis of IEEE 802.15.4 MAC Protocol in the Presence of Hidden Nodes', in

Proceedings of the 11th International Symposium on Communications and

Information Technologies (ISCIT), Oct. 2011, Hangzhou, China, pp.303-

308.

11. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, 'Performance

Analysis of IEEE 802.15.4 MAC protocol for WSNs with ACK Frame Trans-

mission under Unsaturated Trac Conditions', in Proceedings of the Sixth

International Conference on Intelligent Sensors, Sensor Networks and In-

formation Processing (ISSNIP), Dec. 2010 , Brisbane, Australia, pp.55-60.

12. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Wireless Sensor

Networks for Structural Health Monitoring: Considerations for communica-

tion protocol design', in Proceedings of the IEEE 17th International Confer-

ence on Telecommunications (ICT), Apr. 2010, Doha, Qatar, pp.694-699.

1.4 Thesis Organisation

The remainder of the thesis is organised as follows:

In Chapter 2, the requirement of having energy ecient MAC protocols for

WSNs is elaborated by discussing the potential sources of energy wastage in

1. INTRODUCTION 10

communications. A comprehensive review that leads to a classication of existing

MAC protocols is presented. The IEEE 802.15.4 standard, which species the

PHY and MAC layer specications for low-power, low-data-rate applications,

is identied as the state-of-the-art MAC protocol for WSNs. An overview of

the IEEE 802.15.4 MAC protocol is presented by specifying its two dierent

operational modes: beacon-enabled mode and non-beacon-enabled mode. The

literature of modelling wireless MAC protocols is reviewed, and the Markov chain

analysis is recognised as one of the potential analytical technique to model the

IEEE 802.15.4 MAC protocol. Further, a comprehensive summary of existing

computer simulators used for MAC protocol simulations in the context of WSNs

is provided.

Chapter 3 presents a DTMC based analysis to model the beacon-enabled

mode of the IEEE 802.15.4 MAC protocol with ACK frame transmissions. Per-

formance of the protocol is evaluated in terms of the network throughput, power

consumption, and reliability in data transmission. By using few approximations,

the proposed analysis is simplied to a mathematically less complex model that

can predict the performance of the protocol with an acceptable accuracy. Impact

of dierent network parameters including number of nodes and frame length, and

the MAC layer parameters including number of frame retries and backo window

length are investigated using the proposed models. The analysis is then extended

to evaluate performance of the protocol under erroneous channel conditions. Pre-

dictions of all the proposed analyses are validated using ns-2 simulations.

In Chapter 4, a Markov chain based analysis is presented to model the non-

beacon-enabled mode of the IEEE 802.15.4 MAC protocol. After illustrating the

possible contention scenarios associated with the non-beacon-enabled mode, a

mathematical model is developed for the protocol without ACK frames. Then,

by carefully integrating the ACK frame transmission and the frame retransmis-

sion mechanism, the proposed model is extended to evaluate performance of the

protocol with ACK frames. Similar to Chapter 3, generality of the proposed anal-

1. INTRODUCTION 11

yses is exploited to investigate the impact of dierent network and MAC-layer

parameters on the performance of the protocol. Validity of the proposed analyses

are proven using extensive ns-2 simulations.

Chapter 5 develops an analytical model to investigate the impact of the pres-

ence of hidden nodes on the performance of the IEEE 802.15.4 MAC protocol. In

a network with hidden nodes, the common channel seen by the receiver is mod-

eled using a DTMC based on the node grouping assumption. The DTMC is then

solved, and the aggregate throughput of the network is obtained. Consequently,

the impact of dierent network parameters including number of groups, nodes per

group, frame arrival rate and frame length on the performance of the protocol

is discussed. Experimental results show that the aggregate network throughput

of a given network depends on the average value of the number of hidden nodes

per node of that network. Based on this nding, a mechanism is proposed to ap-

proximate the throughput performance of networks that do not satisfy the node

grouping condition.

In Chapter 6, a new hybrid MAC mechanism is presented to improve the

performance of IEEE 802.15.4 based WSNs deployed in hybrid monitoring ap-

plications. The new protocol combines two dierent medium access techniques

without altering the basic architecture of the standard IEEE 802.15.4 MAC proto-

col. The performance of the proposed protocol is evaluated via ns-2 simulations.

A WSN deployed in a SHM system is considered as a case study to discuss the

improvements achieved by the proposed protocol in terms of reliability, delay, and

energy eciency in data transmission.

Chapter 7 concludes the thesis by presenting a summary of the investigations,

research outcomes, and recommendations for future research.

Chapter 2

MAC Protocols for WSNs

2.1 Introduction

This chapter reviews MAC protocols and their performance evaluation techniques

in the context of WSNs. An overview of basic wireless medium access mechanisms

is followed by a discussion on possible energy losses at the MAC layer in WSNs.

Based on how MAC protocols address the energy wastage, a broader classica-

tion of MAC protocols for WSNs is presented. The IEEE 802.15.4 standard is

identied as the state-of-the-art MAC protocol for WSNs and, it is summarised

by elaborating its dierent medium access mechanisms. An overview of the an-

alytical techniques that have been used to evaluate the performance of MAC

protocols is presented with a special attention to Markov chain based analyses.

Finally, the simulation platforms used to model MAC protocols in WSNs are dis-

cussed by comparing their ability to simulate the IEEE 802.15.4 MAC protocol.

2.2 Wireless MAC Protocols

Communication in wireless networks is generally achieved using a `common trans-

mission medium1', which is shared by all network nodes. The shared medium1Also referred to as the channel.

2. MAC PROTOCOLS FOR WSNS 13

access in wireless networks has to be regulated properly while satisfying the per-

formance requirements of underlying applications. This responsibility is carried

out by the MAC layer protocol of wireless nodes. The MAC layer represents the

lower half of the data link layer (DLL) of the open systems interconnection (OSI)

model. It operates directly on top of the PHY layer, thereby having full control

over the physical medium. The main functions of the MAC layer protocols are

to decide when a node accesses the shared medium and to resolve any potential

medium access conicts between competing nodes.

The medium access conicts, which are widely known as `collisions of transmis-

sions', can be minimised by exchanging some amount of controlling information

among the nodes. Exchanging these overhead information generally occurs over

the same common channel reducing the available channel resources for useful data

transmission. On the other hand, reducing overhead information would increase

collisions in data transmissions, and consequently it wastes the available channel

resources. The trade-o between the maximum utilisation of the available chan-

nel resources and the overheads required to achieve it has been at the basis of

most of the Wireless MAC protocols [7]. To address this trade-o, wireless MAC

protocols may follow one of the three fundamental channel access mechanisms :

xed assignment, demand assignment, and random access [39].

2.2.1 Fixed Assignment Mechanism

In the xed assignment mechanism, each node is allocated a predetermined xed

amount of channel resources with the cost of large overheads. Each node uses its

allocated resources exclusively without the risk of collisions. In general, a central-

node should exist with this mechanism to coordinate the resource allocation to

other nodes. The protocols based on the xed assignment are more ecient un-

der evenly distributed trac conditions (i.e., when each node has same amount

of data to transmit). However, they waste channel resources in uneven trac


conditions by allocating resources to the idle nodes too (i.e., nodes without data

to transmit). Some of the classic protocols that comply with the xed assignment

mechanism are time division multiple access (TDMA), frequency division mul-

tiple access (FDMA), and code division multiple access (CDMA), which assign

channel resources in the form of time slots, frequency bands and channel codes,

respectively.

2.2.2 Demand Assignment Mechanism

In the demand assignment mechanism, channel resources are assigned temporar-

ily to nodes on their demand. Thus, this mechanism provides better performance

in networks with uneven trac conditions by dynamically assigning channel re-

sources only to active nodes. However, the dynamic resource allocation generates

more overheads, and hence reduces available channel resources further. The pro-

tocols based on polling schemes [40] and token passing [41] pursue the demand

assignment mechanism.

2.2.3 Random Access Mechanism

In contrast to the aforementioned mechanisms, the random access mechanism

does not particularly assign channel resources to nodes. Thus, all nodes must

contend to access the common medium in a random manner. Therefore, the

nodes in this mechanism are uncoordinated, and hence the channel access is fully

distributed. Compared to the other two mechanisms, the random access mech-

anism utilises little or no overheads to regulate the channel access. Therefore,

it inevitably causes collisions in data transmission. However, various techniques

have been proposed with this mechanism to reduce the number of collisions and

to recover from such collisions. For example, ALOHA protocol [42][43], which is

one of the rst protocols complied with random access, resolves collisions by de-

ploying a positive acknowledgement technique along with an exponential backing


o retransmission mechanism.

The carrier sense multiple access (CSMA) protocol [44] enhances the collision

mitigation techniques proposed in ALOHA by sensing the shared medium (i.e.,

verifying the absence of other trac) before transmitting. Sensing the shared

medium - widely known as `carrier sensing' - reduces the possibility of simultane-

ous transmissions, and hence minimises the number of collisions in the medium.

Carrier sensing in CSMA is implemented in two versions: non-persistent and

p-persistent. In non-persistent CSMA, a node is allowed to transmit data imme-

diately if the medium is sensed idle. Otherwise, the node backs o (i.e., delays

the transmission) randomly. At the end of the back o period, the node senses

the medium again and repeats the same mechanism. On the other hand, in p-

persistent CSMA1 a node senses the medium continuously. When the medium

becomes idle the node either transmits the data with a probability p or delays

the transmission with a probability (1− p).

An extended version of CSMA known as carrier sense multiple access/collision

avoidance (CSMA/CA) adds further measures to limit the number of collisions oc-

curred in wireless networks, in which collision detection is not possible. CSMA/CA

protocol may optionally utilise request to send (RTS) and clear to send (CTS)

control messages to inform neighbouring nodes about the oncoming data trans-

mission, and thereby, it can reserve the wireless medium for each data transmis-

sion. Further improvements to the CSMA and CSMA/CA protocols have been

proposed in the IEEE 802.11 MAC protocol [45], multiple access with collision

avoidance (MACA) protocol [46] and its variants [47][48].

These three fundamental mechanisms (i.e., xed assignment, demand assign-

ment and random access) form the basis for MAC protocols designed for all most

all wireless networks including WSNs.1When p = 1, the protocol is specically categorised as 1-persistent CSMA.


2.3 MAC Protocols for WSNs

In general, a WSN represents a network of battery powered sensor nodes that

are deployed in a large physical environment to monitor a certain phenomenon

cooperatively. The data collected at each sensor node are forwarded wirelessly

to a central server to generate useful information [2]-[4]. This challenging nature

poses several unique requirements for designing MAC protocols to WSNs. First,

these protocols should be able to adapt to network changes - in terms of topology,

size and density - without incurring signicant overheads. Next, the scalability of

MAC protocol is vital for many WSNs as most of them contain tens to hundreds

of sensor nodes. Then, dierent trac patterns (e.g., periodic trac, spontaneous

trac) generated by underlying WSN applications should be supported by these

protocols without undergoing signicant modications. Furthermore, these MAC

protocols should be simple enough to be implemented using limited memory and

computing capabilities of sensor nodes. Above all of these requirements, the

energy eciency in operation should be achieved by these protocols to conserve

limited energy resources in sensor nodes [9][10][14][49].

2.3.1 Energy Wastage in MAC Layer

To achieve the energy ecient communication in WSNs, sensor nodes should min-

imise the energy wasted at the MAC layer due to collisions, overhearing, protocol

overheads and idle listening [11][19]. Collision of transmissions inicts corrupted

receptions at the receiver-node, and hence it wastes energy at both transmitter

and receiver nodes. Energy consumption may extend further upon subsequent

retransmissions of the collided frames. The broadcast nature of wireless medium

creates the overhearing problem by making wireless nodes to pick up frames that

are not destined to them. Overhearing unnecessary trac can be a dominant

factor of the energy wastage when node density is high and trac load is heavy

[19]. Protocol overheads are induced by exchanging control and synchronisation


information among competing nodes. Excessive transmitting/receiving of over-

head information may result in signicant energy consumption [11]. Nodes waste

energy on idle listening when they are listening to receive possible trac (data or

control messages) that is not sent. Even though idle listening consumes less en-

ergy than data transmission or reception, it is the main source of energy wastage

when nodes operate over a long period [19].

Based on how they eliminate/minimise energy losses and full other design

requirements, the MAC protocols for WSNs can be categorised in to two major

groups: schedule based protocols and contention based protocols.

2.3.2 Schedule based Protocols

In schedule based protocols, all sensor nodes follow a common schedule to access

the channel to avoid the energy losses occurred mainly due to collisions. There-

fore, these protocols are inherently based on the xed or demand assignment

mechanisms. TDMA scheme is the preferred mechanism for most of these proto-

cols, since limited resources at sensor nodes may not permit employing complex

radio transceivers required for FDMA or CDMA systems. In TDMA scheme, a

node transmits/receives only during its allocated time slot. It turns the radio

transceivers o at all the other times, and thereby avoids the idle listening and

overhearing implicitly. Therefore, schedule based protocols successfully eliminate

most of the energy losses in wireless communications and increase the life time

of the network.

However, the xed/demand assignment nature of these protocols creates some

shortcomings. First and foremost, a signicant amount of protocol overheads is

required in schedule based protocols to establish and maintain the common sched-

ule. Then, these protocols have to maintain a network-wide strict time synchro-

nisation, which is again achieved by additional signalling overheads, to deliver

a collision-free communication. Poor scalability is another issue with schedule


based protocols as it is inecient or impractical to deploy a common schedule

in large networks. Moreover, schedule based protocols do not easily adapt to

network changes, since such adaptations demand complex rearrangements to the

common schedule.

TRAMA [12], low energy adaptive clustering hierarchy (LEACH) [50], dy-

namic energy ecient MAC (DEE-MAC) [51], pattern MAC (PMAC) [52], self

organising MAC for sensor networks (SMACS) [53] and EMACS [54] are some of

the prominent schedule based MAC protocols proposed for WSNs.

2.3.3 Contention based Protocols

In contention based protocols, the channel access is regulated not by a common

schedule but the contention that each node has with its neighbouring nodes to

access the common channel. Thus, these protocols are essentially based on the

random access mechanism. Most of the contention based protocols prefer CSMA

scheme over other random access techniques due to its simplicity. The absence

of a common schedule in these protocols minimises the energy wasted due to

protocol overheads. However, their random nature of channel access leads to

an excessive amount of energy losses in the form of collisions, idle listening and

overhearing. To mitigate these energy losses, many contention based protocols

provide various techniques. For example, random backing o and channel sensing

are generally performed to reduce collisions in transmission, while low duty cycles

with periodic listening [19] and preamble sampling [22] are employed to minimise

the idle listening and overhearing.

Despite their comparatively high energy consumption, the contention based

protocols show better adaptability for network changes and trac variations.

Furthermore, these protocols do not require a strict time synchronisation and

are highly scalable for large sensor networks due to their decentralised operation.

S-MAC [19], timeout MAC (T-MAC) [20], Berkeley MAC (B-MAC) [55], power


aware multi access with signalling (PAMAS) [56] and WiseMAC [22] are a few

representative contention based MAC protocols for WSNs.

By combining the strengths and eliminating the weaknesses of both schedule

based and contention based categories, a new class of MAC protocols known as

hybrid protocols has been emerged recently. The IEEE 802.15.4 MAC protocol

[27] is one of these hybrid protocols1 that has a contention based component to

achieve a exible and low complex operation and a schedule based component to

provide a higher quality of service (QoS). More Importantly, the introduction of

the IEEE 802.15.4 protocol has accelerated the commercial deployment of WSNs

by setting up a standard for WSNs MAC protocols [60].

2.4 Overview of IEEE 802.15.4 Standard

The IEEE 802.15.4 standard [27] denes the PHY and MAC layer specica-

tions for Low-Rate WPANs, which are generally low cost, low power, short range

communication networks. The PHY layer of an IEEE 802.15.4 complied device

contains a radio frequency (RF) transceiver along with its low-level control mech-

anism. On the other hand, the MAC sublayer of the device provides a regulating

mechanism to access the physical layer for all communication. Figure 2.1 illus-

trates these layers within the OSI seven-layer model.

The MAC sublayer, which represents the lower half of the data link layer

(DLL), also provides services to the logical link control (LLC) sublayer2 (i.e., the

upper half of DLL) via a service specic convergence sublayer (SSCS) as shown

in Figure 2.1. The LLC sublayer oers a direct interface to upper layer protocols

by shielding them form the underlying PHY and MAC protocols. Functionalities

of the LLC sublayer and other upper layers, which range from the network layer

to application layer, are beyond the scope of the IEEE 802.15.4 standard.1Zebra MAC (Z-MAC) (Z-MAC) [57], Funneling MAC [58] and mobility adaptive hybrid

MAC (MH-MAC) [59] are a few other hybrid MAC protocols proposed for WSNs.2An IEEE 802.2 Type 1 LLC is intended to use with IEEE 802.15.4 devices.


Figure 2.1: IEEE 802.15.4 standard within OSI seven-layer model.

Even though the IEEE 802.15.4 standard was originally proposed for WPANs,

the close similarities between the characteristics of WPANs and WSNs make it

a perfect candidate for the PHY/MAC layer protocols in WSNs1. Therefore,

following sections present the most relevant characteristics of the IEEE 802.15.4

standard in the context of WSNs: device types and network topologies, PHY

layer, and MAC layer.

2.4.1 Device Types and Network Topologies

The IEEE 802.15.4 standard denes two classes of physical devices: full-function

devices (FFDs) and reduced-function devices (RFDs). A FFD is equipped with

adequate resources to handle all the functionalities specied by the standard.

Conversely, a RFD has less resources and carries only a reduced set of protocol

functionalities. Based on these physical device types, the standard denes three

logical device types as follows:

• network coordinator: an FFD responsible for network establishment and

control,1Thus, the `LR-WPAN devices' and `wireless sensor nodes' will be used interchangeably

hereafter.


Figure 2.2: IEEE 802.15.4 network topologies: (a) Star (b) Peer-to-peer (c)Cluster-tree, a complex network based on peer-to-peer topology.

• router node: an FFD that relays messages to other nodes,

• end node: an RFD or FFD that communicates only with its parent node,

i.e., the network coordinator or a router node.

Depending on application requirements and logical types of network nodes,

an IEEE 802.15.4 network may operate either of two network topologies: star

topology or peer-to-peer topology (Figure 2.2). In both of these topologies, the

network coordinator initialises the network and administers the association of

other nodes to the network. Communication in star topology networks is con-

trolled centrally by the network coordinator i.e., all nodes communicate directly

with the network coordinator, and the communication among nodes occurs only

through the network coordinator. Conversely, peer-to-peer topology networks ex-

hibit a decentralised communication, in which any node may communicate with

any other nodes in its radio range. In contrast to the star topology, the network

coordinator in the peer-to-peer topology is signicant only at network initialisa-

tion and association procedures; and it does not play a key role in handling the

data ows. Therefore, complex network structures with multi-hop communica-

tion such as cluster-tree topology (Figure 2.2 (c)) can be developed by extending

the peer-to-peer topology.


2.4.2 PHY Layer

The physical layer is primarily responsible for data transmission and reception

using a certain physical radio channel according to a specic modulation and

spreading technique. For this purpose, the IEEE 802.15.4 standard adopts a wide

band physical layer with direct sequence spread spectrum (DSSS) technique. The

standard species three dierent frequency bands: the 868 MHz band - available

in Europe, the 915 MHz band - available in North America, and the 2400 MHz

ISM band - available worldwide. The channelisation of these frequency bands

are as follows: a single channel (Channel 0) operates in the 868 MHz band, 10

channels (Channels 1-10) are dened in the 915 MHz band with a channel spacing

of 2 MHz, and 16 channels (Channels 11-26) operate in the 2.4 GHz band with

a channel spacing of 5 MHz. The major features of each frequency band are

summarised in Table 2.11.

Apart from the data transmission and reception, the IEEE 802.15.4 PHY

layer is responsible for the activation and deactivation of the radio transceiver,

channel frequency selection, energy detection within the current channel, link

quality indication (LQI) for received frames, and clear channel assessment (CCA).

Among these tasks, CCA is particularly important to the MAC layer to acquire

the knowledge of the current state of the medium: busy or idle. CCA reports a

Table 2.1: IEEE 802.15.4 PHY layer: Frequency bands and data rates.

PHY Frequncey Data Parameterslayer band Modulation Bit rate Symbol rate Symbols(MHz) (MHz) (kb/s) (ksymbol/s)

868 868868.6 BPSK† 20 20 Binary915 902928 BPSK 40 40 Binary2450 24002483.5 O-QPSK‡ 250 62.5 16-ary† binary phase-shift keying (BPSK)‡ oest quadrature phase-shift keying (O-QPSK)

1It is worth mentioning that the 2006 revision of the standard [61] has proposed new mod-ulation schemes along with the parallel sequence spread spectrum (PSSS) technique for the 868MHz and 915 MHz bands allowing them to achieve maximum data rate of 250 kbps.


busy medium to the MAC layer by detecting either any energy above the energy

detection threshold or a signal with the modulation and spreading characteristics

of the IEEE 802.15.4 standard. Otherwise, it reports an idle medium to the MAC

layer.

2.4.3 MAC Layer

The MAC layer is mainly responsible for beacon management, network associ-

ation and disassociation, channel access via CSMA/CA mechanism, guaranteed

time slot (GTS) management, ACK frame delivery, frame validation, and se-

cure communication. Among all of them, this thesis concentrates only on the

CSMA/CA mechanism, GTSs, and ACK frame delivery, which relate directly to

the channel access procedures. In terms of channel access, the IEEE 802.15.4

MAC protocol can operate in two dierent modes: beacon-enabled mode and

non-beacon-enabled mode. In the beacon-enabled mode, special control mes-

sages known as beacons are periodically transmitted by the network coordinator

for synchronisation and association purposes. On the other hand, the periodic

beacons do not exist in the non-beacon-enabled mode; however the network coor-

dinator may transmit unicast beacon messages upon nodes' requests. Figure 2.3

Figure 2.3: IEEE 802.15.4 MAC protocol: Channel access mechanisms [62].


illustrates these two operational modes along with their associated channel ac-

cess mechanisms1: slotted CSMA/CA, slotted CSMA/CA + GTSs, and unslotted

CSMA/CA; and provides the outline for the following discussion.

2.4.3.1 Beacon-Enabled Mode

In the beacon-enabled mode, all nodes follow a common superframe structure

initiated by the network coordinator. The superframe, which is bounded by two

consecutive beacons, consists of an active period and an optional inactive period

as shown in Figure 2.4. While all communications occur during the active period,

nodes' transceivers are allowed to sleep in the inactive period. The length of the

superframe (beacon interval, BI) and the length of its active period (superframe

duration, SD) are dened as

BI = aBaseSuperframeDuration × 2BO

SD = aBaseSuperframeDuration × 2SO,(2.1)

where aBaseSuperframeDuration = 960 symbols. The parameters BO and SO

represent the beacon order and superframe order respectively, and they are in

the range 0 6 SO 6 BO 6 14. When SO = BO ⇒ SD = BI, and then

the superframe is always active. The active period of the superframe is divided

into 16 equal superframe slots as depicted in Figure 2.4. Each superframe slot is

further divided into several smaller slots that are equal to aUnitBackoPeriods

(= 20 symbols) in length. These smaller slots form the basic time period of the

CSMA mechanism, and they are simply referred to as the backo slots throughout

this dissertation. The active period consists of a beacon, a contention access

period (CAP), and an optional contention free period (CFP). Nodes that want to

communicate during the CAP compete with other nodes by following the slotted1Each of these access mechanism supports an optional positive ACK scheme. In this ACK

scheme, if the destination node has not received a given data frame successfully, that frame isnot acknowledged. If the originating node has not received the corresponding ACK after a datatransmission, it assumes an unsuccessful transmission and retransmits the data frame.


Figure 2.4: IEEE 802.15.4 superframe structure.

CSMA/CA mechanism1, while in the CFP nodes access the channel using GTSs.

Slotted CSMA/CA Mechanism: The primary channel access mechanism of

the beacon-enabled mode is the slotted CSMA/CA. In this mechanism, all nodes

are time synchronised with the network coordinator by aligning respective back-

o slot boundaries, in particular to the start of the beacon transmission. Fur-

thermore, all channel access procedures (e.g., backing o, channel sensing and

transmitting) should begin at the boundaries of backo slots.

When the MAC layer of a node receives a data frame to transmit, the IEEE

802.15.4 slotted CSMA/CA mechanism (Figure 2.5) begins to operate as follows:

First, the variables NRT, NB, BE and CW, which represent the retransmission

counter, backo stage counter, backo exponent and contention window length,

are initiated to their respective default values (0, 0, macMinBE (default value 3)

and 2). Then, the node locates the next backo slot boundary and waits for a ran-

dom number of backo slots chosen uniformly between 0 and 2BE−1 before sens-

ing the channel. This random backing o reduces the possibility of collision with

transmissions of other contending nodes. After the backo phase, the node senses

the channel (i.e., performs a CCA) for CW times consecutively. If the channel

1Except the channel access for beacons and ACK frame transmissions.


Figure 2.5: IEEE 802.15.4 slotted CSMA/CA mechanism.

has been found idle, the channel access is considered a success and transmission

begins. Otherwise, the node increases NB and BE by one and moves to the next

random backo stage. Once BE reaches the maximum value of backo exponent

(macMaxBE ; default value 5), it is frozen. The above process repeats until NB

reaches the maximum number of backo stages (macMaxCSMABackos ; default

value 4) where an access failure is declared to the upper layer.

If the ACK scheme is not enable, the slotted CSMA/CA mechanism ter-

minates after transmitting the data frame. Otherwise, the slotted CSMA/CA


Table 2.2: IEEE 802.15.4 MAC-layer parameters.

MAC layer Range Default Related MACparameter value layer variables

macMaxBE 3 8 5 BEmacMinBE 0 macMaxBE 3 BEmacMaxCSMABackos 0 5 4 NBmacMaxFrameRetries 0 7 3 NRT

mechanism continues and the node waits for the corresponding ACK frame to

be received. If the node does not receive the corresponding ACK frame within

a macAckWaitDuration1 period, it assumes a frame transmission failure. Then,

it increases NRT by one and starts to retransmit the data frame. During the re-

transmission phase, the node retries the aforementioned channel access procedure

up to the maximum value of retransmission attempts (macMaxFrameRetries ; de-

fault value 3). After macMaxFrameRetries transmission failures, the node drops

the data frame after declaring a transmission failure. Possible range and default

values of the MAC-layer parameters described in this section are listed in Table

2.2.

Channel Access using GTSs: In the beacon-enabled mode, a node that re-

quires a guaranteed QoS can access the channel during the optional CFP, which

(if it presents) locates at the end of the CAP as shown in Figure 2.4. The CFP is

comprised of GTSs allocated by the network coordinator to the requested nodes.

A GTS is a portion of the superframe that is dedicated exclusively to a given

node. The network coordinator can allocate up to seven GTSs at a time and

each GTS may occupy more than one superframe slot. Communication within a

GTS, which is exclusively limited to its allocated node, starts immediately after1This is the maximum waiting time specied in the standard. It comprised of the starting

delay sdack of ACK transmission and the length of the ACK frame Lack. The former variesbetween aTurnaroundTime (i.e., 12 symbols) and aUnitBackoPeriod + aTurnaroundTime

(i.e., 32 symbols) depending on the time at which the last symbol of the data frame receivedat the receiver-node. The length of ACK frames is 22 symbols in 2.4GHz PHY layer.


the GTS boundary without following the CSMA/CA mechanism. Thus, the GTS

mechanism creates a scheduled based access scheme within the superframe using

a TDMA like mechanism.

2.4.3.2 Non-beacon-Enabled Mode

IEEE 802.15.4 networks that do not comply with the superframe structure operate

in the non-beacon-enabled mode by setting BO = SO = 15. Due to the absence

of periodic beacons, nodes are not synchronised in non-beacon-enabled networks,

and hence all transmissions except ACKs follow the unslotted CSMA/CA mech-

anism to access the channel. Moreover, GTSs are not permitted in this mode of

the protocol.

Unslotted CSMA/CA Mechanism: The IEEE 802.15.4 unslotted CSMA/CA

can be considered as a simpler version of the slotted CSMA/CA mechanism.

Apart from the absence of periodic beacons and its consequences (i.e., absence of

network wide synchronisation, superframe structure), the unslotted CSMA/CA

diers from its slotted counterpart in the following aspects:

• The evolution of time in the slotted mechanism is discrete where the discrete

time unit is equal to a single backo slot (= 20 symbol duration); i.e., events

such as back o, CCA, and frame transmission occur only at the boundaries

of backo slots. On the other hand, the evolution of time in the unslotted

mechanism is continuous; i.e., an event may occur immediately after the

previous one.

• In the slotted CSMA/CA, a node performs two consecutive CCAs (at the

boundaries of back-to-back backo slots) to assess the channel idleness,

while in the unslotted CSMA/CA, only a single CCA is performed.

Except these dierences the operation of the the IEEE 802.15.4 unslotted CSMA/CA


Figure 2.6: IEEE 802.15.4 unslotted CSMA/CA mechanism.

mechanism is almost similar to the slotted mechanism, and it is shown in Fig-

ure 2.61.

Next, the performance evaluation of wireless MAC protocols will be discussed,

with special emphasis on the two operational (i.e., beacon-enabled and non-

beacon-enabled) modes of the IEEE 802.15.4 MAC protocol.

1The MAC layer variables and parameters in Figure 2.6 have the same meanings and valuesof those in the slotted mechanism.


2.5 Performance Evaluation of MAC Protocols

Performance of a MAC protocol is a central issue in its design, deployment and

conguration. Therefore, the performance evaluation is a major requirement

for a MAC protocol to measure its capability and competency of accomplishing

the required tasks. Furthermore, performance evaluation studies may be useful in

optimising MAC protocols for a given set of system specications. There are three

basic methods to evaluate the performance of a protocol: laboratory experiments,

computer simulations and analytical models. Among them, analytical models and

computer simulations are the focus of this dissertation as laboratory experiments

are exceedingly time consuming and costly1.

2.5.1 Analytical Models

In the context of MAC protocols, analytical modelling is considered as a useful

tool to model the probabilistic behaviour of the protocols. Thus, it has been

widely applied to analyse the MAC protocols with random access mechanisms.

To this end, several techniques have been proposed in the literature [66][67].

A technique known as the S-G analysis was introduced by Abramson [68] to

evaluate the throughput performance S of the ALOHA protocol in terms of the

oered channel trac rate G. This analysis was then extended by Kleinrock and

Tobagi [44] to evaluate the throughput and delay performance of the persistent

and non-persistent CSMA protocols. More recently, the maximum throughput

and channel busyness of CSMA/CA protocols deployed in the IEEE 802.11 and

IEEE 802.15.4 standards have been computed using a generalised S-G analysis

[69]. The S-G analysis usually models the frame transmission characteristics on

the common channel and does not analyse the behaviour of individual nodes in1For instance, a prototype WSN deployment at Golden Gate Bridge in San Francisco USA

has taken approximately 6 months and US$38, 000 to be implemented (i.e., the WSN wascomprised of 64 sensor nodes that cost US$600 each) [63]. Similar prototype WSNs deployed inglacial environment monitoring [64] and heritage building monitoring [65] have been cost £177and US$120 for each sensor node, respectively.


detail. Therefore, it generally fails to evaluate the energy performance of a node,

which is vital in the context of WSNs.

On the other hand, the tagged user analysis (TUA) [70] models the behaviour

of an individual node of a given network by using the classical queuing theory.

It evaluates the performance of MAC protocols by considering the interactions

among individual nodes on accessing the common channel. To enable this ap-

proach the TUA assumes that all nodes in the network exhibit equivalent sta-

tistical behaviours. Based on this fundamental assumption, TUAs have been

developed to analyse the throughput and delay performance of many random ac-

cess protocols including slotted ALOHA [70][71], CSMA [72], and IEEE 802.11

CSMA/CA [73] protocols.

A uid-type approximation analysis known as the equilibrium point analysis

(EPA) was proposed by Tasaka [67] to analyze MAC protocols in steady state.

In EPA, it is assumed that the system always works at its equilibrium point so

that the number of nodes in any mode is remained xed. For its analysis, EPA

requires to solve a set of nonlinear equations to obtain the equilibrium point of

the system. Using the solution of equilibrium point, various system performance

metrics are calculated. The EPA was rst applied to analyse the carrier sense

multiple access/collision detection (CSMA/CD) protocol [74]. Recently, it has

been used in evaluating the throughput performance of CSMA/CA in the IEEE

802.11 distributed coordination function (DCF) protocol [75][76].

In contrast to the aforementioned techniques, the Markov chain analysis [77][78]

provides a generalised frame work to analyse the performance of MAC protocols.

Neither an equilibrium point in operation nor an equivalent statistical behaviours

of nodes is necessary to develop a Markov chain. In addition, Markov chains can

comprehensively model the behaviour of an individual node in a given network.

Moreover, the Markov chain analysis has been at the foundation of many other

evaluation techniques including EPA and classical queuing theory [77]. In this an-

alytical approach, a Markovian model [78] is formulated to capture the dynamic


behaviour of the system of interest, and then the stationary state probability

distribution of the model is computed. At the end, the system performance is

evaluated using the stationary state probabilities.

Early work on Markov chain analysis of wireless MAC protocols can be found

in [79][80], where the performance of slotted ALOHA and slotted non-persistent

CSMA is evaluated, respectively. Bianchi [81] has developed a two-dimensional

Markov chain to model the CSMA/CA mechanism in the IEEE 802.11 DCF pro-

tocol, and consequently performed a saturated throughput analysis. Following

Bianchi's approach, many Markov chain models have been developed to analyse

the IEEE 802.11 DCF under dierent conditions (e.g., unsaturated trac [82]

and imperfect channel conditions [83]). Pollin et al. [84] have extended Bianchi's

model to evaluate the throughput and energy consumption of IEEE 802.15.4

based star topology networks under both saturated and unsaturated trac con-

ditions. An advanced Markov chain has been presented in [85] by including the

superframe structure of the IEEE 802.15.4 MAC protocol to evaluate the unsatu-

rated throughput of the protocol. Some of the other notable Markov chain based

analyses on the IEEE 802.15.4 MAC protocol can be found in [86]-[89] and more

related work will be presented in Chapter 3, 4 and 5.

2.5.2 Simulations

Simulation based analyses have been used as an alternative to analytical tech-

niques in many large and complex systems, since they can model and analyse such

systems without making restrictive assumptions. In the context of WSNs, various

computer simulators are being employed to model the MAC layer protocols and

to evaluate their performance.

OPNET Modeler [90], which has an object-oriented approach and a hierar-

chical modelling environment, is a well known commercial simulation tool for

network protocol modelling. It provides a wireless suite to model and simulate


various aspects of a wireless network including MAC protocols. Several WSNs'

MAC protocols such as PMAC [52], OD-MAC[91], and A-MAC [92] have been

already simulated and validated using OPNET Modeler. The IEEE 802.15.4

MAC protocol is readily implemented in OPNET, and this implementation has

been used in [93] to study the performance of slotted CSMA/CA mechanism of

the protocol. However, the IEEE 802.15.4 implementation in OPNET does not

contain energy models [94], and hence it fails to simulate the energy performance

of the protocol. QualNet [95] is another commercial simulator1 that has a par-

allel discrete-event simulation approach to model wired and wireless networks.

Similar to OPNET Modeler, QualNet includes an IEEE 802.15.4 MAC protocol

implementation [96] and also supports simulating of many other MAC protocols

in WSNs [97][98].

Compared with OPNET and QualNet simulators, OMNeT++ [99] provides

a free, object-oriented, discrete network simulation framework that can be used

to model and evaluate MAC protocols in WSNs [100]-[102]. Because of its open

platform, OMNeT++ has been extensively exploited to develop many simulators

for WSNs. Among them, MiXiM [103] supports the non-beacon-enabled mode

of the IEEE 802.15.4 protocol, while INET framework [104] includes the beacon-

enabled mode. However, none of the OMNeT++ based simulators contain a

unied implementation of the IEEE 802.15.4 MAC protocol that includes both

operational modes.

In contrast, the IEEE 802.15.4 implementation in ns-2 [105][106] supports

both operational modes of the protocol and also includes simulation models

for energy consumption. ns-2 provides an open simulation platform for object-

oriented discrete event simulations, and hence it is widely used in academia to

model, validate and evaluate communication protocols. The IEEE 802.15.4 MAC

protocol was rst implemented in ns-2 by Zheng and Lee [107][108] to study

the performance of the protocol comprehensively. A similar simulation based1Both OPNET and QualNet are freely available for academic research with less features.


study is presented in [109], where ns-2 simulations were used to investigate some

throughput-energy-delay tradeos of the protocol. Subsequent improvements to

the initial ns-2 implementation of the IEEE 802.15.4 protocol [107] are proposed

in [110][111]. Apart from the IEEE 802.15.4, many other WSN's MAC protocols

have been modelled and evaluated in ns-2 successfully [19][91][112].

Comprehensive reviews on simulation environments for WSNs can be found

in [113]-[115].

2.6 Conclusion

All wireless MAC protocols are based on one of three basic access mechanisms:

xed assignment, demand assignment and random access. In WSNs, these mech-

anisms are used in dierent ways to minimise the energy wasted at the MAC

layer. The IEEE 802.15.4 MAC protocol provides a better solution by combining

the features of demand assignment and random access mechanisms. Performance

of MAC protocols, which critically aects the overall performance of a given net-

work, can be evaluated by either analytical models or simulations. The Markov

chain analysis is preferred to evaluate the IEEE 802.15.4 MAC protocol analyt-

ically, while ns-2 can simulate the protocol comprehensively. As a whole, this

chapter provides a foundation for the concepts and techniques presented in the

subsequent chapters.

Chapter 3

Analysis of Beacon-enabled IEEE

802.15.4 MAC Protocol with ACK

Transmission

3.1 Introduction

With the introduction of wireless sensor networks (WSNs), there has been an in-

creasing demand for low-power, low-data-rate wireless communication standards.

The IEEE 802.15.4 standard [61] meets that demand by dening the PHY and

MAC layer specications for such communications. As mentioned in Chapter 2,

the IEEE 802.15.4 MAC protocol can operate in two dierent modes: beacon-

enabled mode and non-beacon-enabled mode. Out of these modes, the beacon-

enabled mode can be utilised to maintain the network-wide coordination required

for synchronised monitoring WSNs [116][117]. The beacon-enabled mode also fa-

cilitates several power saving features including BatteryLifeExtension and duty

cycling [61][108], which are vital in achieving overall energy eciency. Further-

more, the IEEE 802.15.4 standard includes a MAC-level retransmission mecha-

nism with acknowledgements (ACKs) to improve data transmission reliability in

3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 36

reliability-critical-WSN applications such as military surveillance, wildre detec-

tion and explosive agents tracking [7][34]. Moreover, most of the existing IEEE

802.15.4 based networks operate under unsaturated trac conditions as the stan-

dard has been specically proposed for low-data-rate applications. Therefore,

to understand the performance of real-world WSNs, it is necessary to analyse

the beacon-enabled IEEE 802.15.4 MAC protocol with ACK transmission under

unsaturated trac conditions.

The performance of the beacon-enabled IEEE 802.15.4 MAC protocol has been

already evaluated by means of experiments [118]-[120], simulations [107][109][121]

and analytical models [87]-[135]. Most of the earlier analytical models [87][122],

which were inspired by the seminal work of Bianchi [81], have described the

beacon-enabled IEEE 802.15.4 protocol under saturated trac, despite the fact

that such trac conditions are rarely seen in WSNs. Among these saturated

analyses, only a few have considered ACK transmission and frame retransmission

[86][123]-[125]. Recently, dierent approaches to Bianchi's model have been taken

to analyse the protocol with unsaturated trac [88][126]-[135] ; however, most of

them have overlooked ACK transmission [88][126]-[130].

One of the early attempts to analyse the protocol with acknowledgements

and retransmissions under unsaturated trac conditions can be found in [133].

However, the analytical model presented in [133] has not been validated, and later

it has been shown that it does not match the simulation results [87]. Taking a

dierent approach, Lee et al. [132] have proposed a renewal theory based model to

analyse the protocol with ACK transmission under unsaturated trac. Yet, they

have not derived energy/power related performance metrics, which are vital in

the context of WSNs. Furthermore, they have assumed saturated trac to derive

the throughput performance. The enhanced Markov chain presented in [131]

accurately models the protocol with ACK and unsaturated trac. However, it too

has overlooked the energy/power related performance metrics of the protocol. On

the other hand, Park et al. [134] have derived the average power consumption of


a sensor node considering a network with ACK frames under unsaturated trac.

But, they have not discussed the throughput performance of the protocol. The

analysis proposed by Sahoo and Sheu [135] has derived both the throughput and

the energy consumption related performance metrics. However, their analysis has

been validated only for a single network setup. Later, it has been shown that the

analysis in [135] weakly matches with simulations [134].

The majority of the existing analyses of the beacon-enabled IEEE 802.15.4

MAC protocol assume default MAC parameters values, and hence they present

value-specic models. However, the default MAC layer conguration does not

yield the desirable performance for all situations [136][137]. In fact, the litera-

ture shows that there is a signicant impact of MAC-layer parameters includ-

ing macMinBE, macMaxBE, macMaxCSMABackos and macMaxFrameRetries

on system performance [134]-[136, and references there in]. Most of the ex-

isting analytical models that consider the eects of MAC-layer parameters on

system performance only take macMinBE or macMaxCSMABackos or both

[125][129][132][138] into account. On the other hand, the analyses in [131] and

[135] only consider the macMaxFrameRetries parameter. The models that con-

sider all of the above MAC parameters [134] do not derive a comprehensive set of

performance metrics, which covers both throughput and energy/power aspects of

the protocol. Therefore, none of the existing analyses provide a complete picture

of the performance of beacon-enabled IEEE 802.15.4 MAC protocol with ACK

transmission under unsaturated trac conditions.

The key assumption of `ideal (i.e., error free) channel condition' made in all

the aforementioned analyses deters their applicability in practical WSNs, in which

sensor nodes generally communicate through highly error prone wireless channels.

Although the impact of channel errors on the performance of well-known IEEE

802.11 based networks has been thoroughly investigated in the literature [139]

[140], that of the IEEE 802.15.4 based WSNs is still open to be studied. More

recently, Gao et al. [141] have eliminated the ideal channel assumption and anal-


ysed the IEEE 802.15.4 MAC protocol under burst error channels. However, that

analysis does not take ACK transmission into account. Furthermore, it overlooks

some performance metrics including power consumption and frame delivery ratio,

which are important in reliability critical WSNs. Thus, there is a need to analyse

the protocol with ACK transmission under erroneous real-life channel conditions.

This chapter presents a generalised analytical model based on a three-dimensional

discrete-time Markov chain (3D-DTMC) to analyse the beacon-enabled IEEE

802.15.4 MAC protocol with ACK transmission under unsaturated trac condi-

tions. The proposed analytical model is a comprehensive extension of the analysis

done by Ramachandaran et al. [126] for the same protocol without ACK frames

and retransmissions. In additon, a simplied version of the proposed model is

presented to reduce the complexity of the analysis. Finally, the proposed analyt-

ical model is extended to analyse IEEE 802.15.4 based networks operating under

erroneous channel conditions.

3.2 System Model

The system model considered in this chapter consists of an IEEE 802.15.4 beacon-

enabled, single-hop, star topology network of N sensor nodes and a coordinator

node. The latter acts as a common receiver for all sensor nodes, which lie within

the carrier sensing range of each other. Therefore, all nodes see the same trans-

mission channel with regard to MAC-layer functionalities. The system remains

active throughout the entire beacon interval1, which is lled with CAP. Data

transmission within the system is assumed to be only in the uplink direction

(i.e., from sensor nodes to coordinator). Each successful data transmission will

be followed by an acknowledgement (ACK) from the common receiver. All data

frames are of equal length, and the transmission of a data frame lasts for xed-L1i.e., SO = BO. However, only the coordinator node remains active always. For energy

conservation, sensor nodes power-o their radio unless they are transmitting data frames orreceiving acknowledgements and beacons.


backo slots. Data frames arrive at the nodes according to a Poisson distribution

with an arrival rate of λ frames per frame duration. Thus, the frame arrival prob-

ability per backo slot can be derived as p = λ/L. Buering at the nodes is not

considered. Consequently, a node that already holds a frame will discard further

frame arrivals. Moreover, the capture eect [142][143] is not implemented for

collided frames, i.e., all collided frames are dropped regardless of their received

signal strengths at the common receiver. Unless mentioned otherwise, ideal chan-

nel conditions are assumed. Hence, transmitted data frames can be lost only due

to frame collisions.

Performance of the system of interest (e.g., throughput, power consumption)

largely depends on the amount of data transmitted over a given period of time.

On other hand, transmission of data depends on sensing the channel to be idle.

Therefore, modelling of this system is based on two basic probabilities:

• The probability that the channel is sensed idle in a given backo slot,

• The probability that a node begins data transmission in a given backo

slot.

Since exact computation of these probabilities is analytically complex, some ap-

proximations1 are considered to simplify the analysis.

3.2.1 Approximations

The probability that the channel is sensed idle in a given backo slot is approx-

imated by the steady state probability pci that the channel is idle. Accordingly,

after a random backo each node senses the channel to be idle in the rst2 of the

two channel-sensing backo slots with probability pci . Similarly, the probability

that any node begins data transmission in a given backo slot is approximated

by the steady state probability pnt that a node begins its data transmission.1Similar approximations have been made in [126] and [144].2The channel idleness is not considered to be independent from one sensing backo slot to

the next.


According to the standard, a node should defer any transmission that cannot

be completed within the current beacon interval to the next. It is assumed that

this condition has a negligible eect on the contention process of data transmis-

sion. Therefore, the beacon-enabled IEEE 802.15.4 MAC protocol is approxi-

mated by the slotted CSMA protocol described in Section 2.4.3.1.

3.3 Analytical Model

Based on the system model and approximations above, this section formulates two

DTMCs to model the behaviour of an individual node and the common channel

in the system considered. The two DTMCs are coupled and solved numerically

for the basic model probabilities pnt and pci , which can be then used to analyse

the protocol in a given network.

3.3.1 Node State Model

Consider a node that has a data frame to transmit. In the standpoint of MAC

protocol, this node can be in dierent states depending on its current trans-

mission attempt, backo stage and backo counter value. Values of these pa-

rameters in a given instant depend on the basic model probabilities, and hence

they are not deterministic. Therefore, the transmission attempt, backo stage

and backo counter of a given node are modelled using three stochastic pro-

cesses represented by the random variables x, y and z, respectively, where x ∈

1, 2, ...,macMaxFrameRetries+1, y ∈ 1, 2, ...,macMaxCSMABackos+1 and

z ∈ 1, 2, ..., wy − 11. Based on these stochastic processes, a three-dimensional

DTMC is developed to model the behaviour of an individual node as shown in

Figure 3.1.

A node resides in the IDLE state if it does not have a frame to transmit.

When this node receives a frame to transmit (with probability p), it moves from

1wy − 1 represents the maximum backo window length of the yth backo stage, and it isequal to 2BEy − 1, where BEy denotes the corresponding backo exponent.


Figure 3.1: 3D-DTMC model of node. Probability pci and pci|i are denoted by u

and v, respectively.


the IDLE state to the rst backo stage. A backo stage consists of several

backing o slots and two channel sensing states; each of one backo slot1 in

duration. The number of back o slots that the node resides in the yth backo

stage is uniformly distributed between 0 and wy − 1 (note: if the chosen number

is zero, the node immediately starts to sense the channel without backing o).

However, during the rst backo stage of the rst transmission attempt, the node

has to wait at least three backo slots before sensing the channel due to the node's

transceiver Sleep-to-Idle transition time [126]. The back o slots of all backo

stages are modelled by BOxyz states with suitable values for the variables x, y

and z.

When the rst backing o expires, the node moves to CS110 state. The nota-

tions CSxy0 and CSxy(−1) are used to represent the rst and second channel sensing

states of the yth backo stage in the xth transmission attempt, respectively. If

the channel is found to be idle during the rst sensing, which happens with prob-

ability pci , the node moves to CS11(−1) state. If the node nds the channel to be

idle again, it accesses the channel to transmit the data frame. This happens with

probability pci|i, which is the conditional probability of the channel being idle in

the next backo slot given that it is idle in the current backo slot. On the other

hand, if the channel had been found busy when the node was in either CS110 or

CS11(−1) state, the node moves to the next backo stage and chooses the number

of back o slots randomly. This mechanism is repeated up to y backo stages,

where y = macMaxCSMABackos+1 . The node moves back to IDLE state from

CS1y0 or CS1y(−1) by declaring a channel access failure, if the channel is found to

be busy.

On the contrary, if the node gets access to the channel successfully, it resides

in TX1 state for L backo slots to transmit the data frame. At the end of data

transmission, the node moves to ACK1 state with probability 1 and awaits the1This is the basic time step of the protocol and equals to 20 symbols in duration. In the

2.4 GHz physical layer, this equals 320 µs.


Table 3.1: Relationships between model variables and MAC-layer parameters.

Model variable MAC-layer parameters

x macMaxFrameRetries + 1y macMaxCSMABackos + 1wy 2min((y−1)+macMinBE ,macmaxBE)

corresponding ACK frame for a period of (Lack + sdack) backo slots, where Lack

and sdack denote the ACK frame length and the starting delay of ACK trans-

mission, respectively. If the node has received the ACK frame (with probability

q, which will be determined in Section 3.3.2), it moves to IDLE state. Con-

versely, if the node has not received the ACK frame, it moves to the rst backo

stage of the next transmission attempt. This process repeats until the node

receives the corresponding ACK frame or tries x transmission attempts, where

x = macMaxFrameRetries + 1. If the node has not received the corresponding

ACK frame after x transmission attempts, it terminates the transmission process

and moves back to IDLE state by declaring a transmission failure. The relation-

ships between the model variables x, y and wy, and their respective MAC-layer

parameters are summarised in Table 3.1.

Based on the node state transitions described above, steady state probabilities

of the node model DTMC are derived in Appendix A.1. They are then used to

derive the steady state probability pnt that a node begins data transmission as

follows.

pnt =

steady state probability of node

residing in any CSxy(−1) state

probability that the node transits

from CSxy(−1) to TXx states

=

( ∑xx=1

∑yy=1 π(csxy(−1))

π(idle) + Φ + Ψ + L∑x

x=1 π(txx) + (Lack + sdack)∑x

x=1 π(ackx)

)pci|i,

(3.1)

where Φ =∑x

x=1

∑yy=1

∑wy−1z=1 π(boxyz) and Ψ =

∑xx=1

∑yy=1

∑0z=(−1) π(csxyz). In

(3.1), the steady state probability of node residing in any of the CSxy(−1) states


is obtained by summing long-term proportions of time that the node resides in

each CSxy(−1) state. Then, the transition probability pci|i can be related to the

other basic model probability - the steady state probability pci that the channel is

idle - as

pci = pci|ipci + pci|b(1− pci), (3.2)

where pci|b is the conditional probability that the channel is idle in the next backo

slot given that it is busy in the current backo slot. The probability pci|b equals2

L+Lack, where L and Lack represent the data frame length and ACK frame length

in backo slots, respectively. Therefore, (3.2) can be rearranged to obtain pci|i as

pci|i =pci − pci|b(1− pci)

pci= 1− 2(1− pci)

(L+ Lack)pci. (3.3)

To nd the basic probability pci in (3.3), the channel state model is developed

next.

3.3.2 Channel State Model

The behaviour of the channel can be modelled using a DTMC as shown in Fig-

ure 3.2. Assume that the channel is idle for two consecutive backo slots, which

is represented by IDLE,IDLE state. The channel remains in this state given

that none of the nodes begin transmission. This happens with probability α

= (1 − pnt|ii)N , where N and pnt|ii are the number of contending nodes and the

probability that any node begins transmission given that the channel has been

idle for two consecutive backo slots, respectively. The conditional probability

pnt|ii can be computed as

pnt|ii =pntpcii

=pntpci|ip

ci

=(L+ Lack)p

nt

(L+ Lack)pci − 2(1− pci). (3.4)

On the other hand, when exactly one node begins transmission and the oth-


Figure 3.2: Discrete-time Markov chain model of channel.

ers refrain from transmission, the channel moves from IDLE,IDLE to SUCCESS

state. This happens with probability β = Npnt|ii(1 − pnt|ii)N−1. After dwelling

L backo slots in SUCCESS state, the channel progresses to ACK-IDLE state

with probability 1. ACK-IDLE state represents the channel idleness between

data transmission and the following ACK transmission. The channel dwells sdack

backo slots in this state and moves to ACK-TX state, which corresponds to

the subsequent ACK transmission. After residing Lack backo slots in this state,

the channel returns to IDLE,IDLE state through an intermediate IDLE state. If

more than one node begin transmission simultaneously, the channel moves from

IDLE,IDLE to FAILURE state with probability σ = 1 − α − β. Due to the ab-

sence of a collision detection mechanism, the channel dwells in FAILURE state

for the entire frame length (i.e., L backo slots) before returning to IDLE,IDLE

state via the intermediate IDLE state. Moreover, a node that has just transmit-

ted a data frame will receive the corresponding ACK frame if none of the other

nodes have transmitted data simultaneously. This happens with probability q

= (1− pnt|ii)N−1, and it is one of the key probabilities that the node state model

was built upon in Section 3.3.1.

According to the channel behaviour described above, the steady state equa-


tions and the normalised condition of the channel model DTMC can be expressed

as

πcidle,idle = πcidle + απcidle,idle

πcsuccess = βπcidle,idle

πcack−tx = πcack−idle = πcsuccess (3.5)

πcfailure = (1− α− β)πcidle,idle

πcidle = πcfailure + πcack−tx

πcidle,idle + πcsuccess + πcfailure + πcack−idle + πcack−tx + πcidle = 1. (3.6)

By rearranging the equations above, the long-term proportion of transitions into

each state in the channel model DTMC are computed as

πcidle,idle =1

Ω, πcidle =

(1− α)

Ω, πcfailure =

(1− α− β)

Ω

πcsuccess = πcack−idle = πcack−tx = β/Ω, (3.7)

where Ω = 3 + 2(β − α). Then, the fraction of time spent in each state can be

obtained by taking the actual time spent in each state into account. Therefore,

the probability that the channel remains idle for two consecutive backo slots pcii

is given as

pcii =πcidle,idle

πcidle,idle + Lπcsuccess + Lπcfailure + sdackπcidle−ack + Lackπcack−tx + πcidle

=1

1 + (L+ 1)(1− α) + (Lack + sdack)β. (3.8)

Using (3.3), (3.8) and the expression pci = pcii/pci|i, the basic probability p

ci can be

obtained as

pci =1

L+ Lack + 2

[(L+ Lack)

1 + (L+ 1)(1− α) + (Lack + v)β+ 2

]. (3.9)


In (3.9), the approximated probability that the channel is sensed idle in a

given backo slot pci is expressed as a function of the approximated probability

that a node begins transmission in a given backo slot pnt through α and β. On

the other hand, in (3.1) pnt is a function of pci through pci|i. Moreover, all steady

state probabilities related to both node state model and channel state model

can be expressed as functions of these two basic model probabilities. Therefore,

(3.1)(3.4), (3.8), (3.9), steady state probabilities of the node model, and the

expressions for α, β and q formulate a consistent system of equations that can

be solved numerically. Numerical solution of this analytical model will be used

to investigate the system performance in Section 3.7.

3.4 Simplied Model

The proposed analysis in Section 3.3 has developed a large state space (specially

in the node state model) to exactly model the protocol's behaviour. However,

the presence of a large number of states complicates the computations associ-

ated with the model. To reduce the computational complexity of the analysis,

a simplied version of the proposed model is developed after making some ad-

ditional approximations. In the simplied model, the uniform distribution that

determines the backo counter value is replaced by a geometric distribution of

the same mean [126]. Therefore, all backing o slots that a node dwells in a

given backo stage can be represented by a single state. Furthermore, the limit

for maximum number of retransmission attempts is ignored, and hence it is as-

sumed that nodes keep retransmitting until they receive the corresponding ACK

frames. This eliminates the 3rd dimension (i.e., dierent transmission attempts)

thus simplifying the model. Finally, the simplied model does not consider the

Sleep-to-Idle transition of node's transceiver to avoid erroneous escalations of

backing os in frame retransmissions in the absence of the 3rd dimension. Apart

from the above additional approximations, all other assumptions and approxima-


tions drawn in Section 3.3 remain unchanged in the simplied model. Modied

node and channel state models of the simplied analysis will be described next

in detail.


Because of the simplifying approximations made in Section 3.4 above, the be-

haviour of an individual node can be modelled using a more simple DTMC as

shown in Figure 3.3. The node is in IDLE state if it does not have a frame to trans-

mit. When the node receives a frame (with probability p) it starts backing o.

BOy states represent the backo stages of node, where y ∈ 1, 2, ...,macMaxCSMA-

Backos+1. The number of back o slots that the node dwells in the yth backo

stage is geometrically distributed with parameter pny , which also determines the

transition from that backo stage to the corresponding rst carrier sensing stage

[126]. pny can be computed as

1− pnypny

=1

wy

wy−1∑z=0

z (3.10)

by considering its same mean property with the equivalent uniform distribution1.

In Figure 3.3, CSy1, CSy2, TX and ACK represent the `rst and second channel

sensing' (of the yth backo stage), `frame transmitting' and `ACK frame await-

ing/receiving' node states, respectively. Parameters p, q and y have the same

denotations those of the detailed analytical model presented in Section 3.3. The

transition probabilities of the DTMC are also shown in Figure 3.3, and the steady

state equations are derived in Appendix A.2. While the expressions for pci and pci|i

in the simplied model remain unchanged with those of the detailed analytical

1 wy is related to the maximum backo window length of the yth backo stage, and it hasbeen quantied in Table 3.1.


Figure 3.3: Discrete-time Markov chain for node in simplied model.

model, the expression for pnt is changed as follows

pnt =pci|i∑y

y=1 π(csy2)

π(idle) + Lπ(tx) +∑y

y=1 π(boy) +∑y

y=1

∑2z=1 π(csyz) + (Lack + sdack)π(ack)

(3.11)

due to the node model simplications. The denominator of (3.11) can be simpli-

ed to 1 + (L− 1)π(tx) + (Lack + sdack − 1)π(ack), considering the normalisation

condition of the Markov chain.


Since the additional approximations made in Section 3.4 do not aect the channel

model, the same channel state model depicted in Figure 3.2 is used with the

simplied model. Thus, the expressions for pnt|ii, α, β, q, pcii and pci are the

same as those of Section 3.3.2; however, their values are dierent in the simplied

model due to the changes in the steady state equations of the node model and the

expression for pnt . Similar to the detailed analytical model, a system of equations


(comprised of (3.3), (3.4), (3.8)(3.11), steady state equations for the node model

and the expressions for α, β and q) can be used to solve the simplied model

numerically.

3.5 Extended Model for Networks with Erroneous

Channels

This section extends the analytical model presented in Section 3.3 to analyse

IEEE 802.15.4 networks operating under burst channel errors. From the stand-

point of MAC protocol, the key dierence between an ideal (i.e., error free)

channel and an erroneous channel is their reasons behind discarding frame trans-

missions. In ideal channel conditions, transmitted data frames are discarded

only due to collisions. If a data frame is transmitted without being collided, the

intended receiver-node successfully receives it, and subsequently the transmitter-

node will receive the corresponding ACK.

On the other hand, in networks experiencing erroneous channel conditions, the

transmitted data frames are discarded either due to collisions or due to channel

errors. Although a data frame is received at the intended receiver-node suc-

cessfully, the corresponding transmitter-node may not receive the ACK due to

channel errors. In such situations, the transmitter-node assumes a failure in data

transmission (link timeout), and consequently it retransmits the data frame until

receiving the corresponding ACK or reaching the maximum possible retransmis-

sion attempts. On the other end of the channel, if the receiver-node receives the

retransmitted frame, it would be recognised as a duplicate frame. Each duplicate

frame will be discarded at the receiver node after resending the corresponding

ACK.

Taking the aforementioned new behaviour into account, an extension to the

analysis in Section 3.3 is proposed. The extended analysis is developed for the


same system model presented in Section 3.2 excluding the ideal common channel.

Instead, a channel with burst errors is considered as described next.

3.5.1 Channel Error Model

The erroneous channel is modelled using the Gilbert-Elliot error model [141][145].

In contrast to the classicmemoryless binary symmetric channel model, the Gilbert-

Elliot model relates current state of the channel to previous channel conditions.

This enables it to describe burst error patterns in transmission channels.

As depicted in Figure 3.4, the Gilbert-Elliot model represents the channel

using two states: good (G) and bad (B)1. The `good' state is free from errors while

Figure 3.4: Gilbert-Elliot channel error model.

the `bad' state represents the channel with errors. The transition probabilities

from good state to bad state and vice versa are given by vg and vb. Then, the

durations that the channel dwells in the good and bad states can be considered as

exponential random variables with mean v−1g and v−1b , respectively [141]. Thus,

the steady-state probabilities of the channel residing in the good and bad states

- πg and πb - can be given as

πg =vb

vg + vb, πb =

vgvg + vb

.

These steady state probabilities will be used in Section 3.5.3 to derive new prob-

abilities of channel state transitions.1Similar two state Markov models have been used to model the bursty characteristics of

channel errors in [146]-[148].


Based on this channel error model, node state and channel state models of

the extended analysis are developed next.


Erroneous channel conditions do not aect the behaviour of a transmitter-node

except causing it to retransmit more frames to compensate frames dropped due

to channel errors. In the node state model, those additional retransmissions can

be taken into consideration by recomputing the transition probability q (i.e., the

probability that a node receives the corresponding ACK after a data transmission)

as illustrated in Section 3.5.3. Therefore, the node state model presented in

Section 3.3.1 is used with recomputed q values to model the behaviour of a node

in the extended analysis. In contrast, the presence of channel errors signicantly

aects the behaviour of the common channel, and hence major modications are

required to the channel state model presented in Section 3.3.2. This leads to a

modied channel state model.

3.5.3 Modied Channel State Model

The behaviour of common channel under erroneous conditions is modelled using

a modied DTMC as shown in Figure 3.5. Suppose the channel is in IDLE,IDLE

state at the beginning. If a node begins transmission, the channel moves from

IDLE,IDLE state to SUCCESS state given that no collision has occurred and

the channel is error free for the entire duration of data frame transmission. The

no-collision condition occurs with probability β, while the error free condition is

satised with probability (1−Perr−data) = πge−vgL [141]. If any of these conditions

is not satised, the channel moves to FAILURE state with probability σ = 1 −

α− (1−Perr−data)β. The probabilities α and β have their usual meanings and are

expressed as α = (1 − pnt|ii)N and β = Npnt|ii(1 − pnt|ii)N−1. The rest of the state

transitions of the modied DTMC is similar to those presented in Section 3.3.2.


Figure 3.5: DTMC model for burst error channel.

After transmitting a data frame, a node receives the corresponding ACK frame

if there were no collisions and the channel was error free for the entire duration of

transmission (including both data and ACK frames). This occurs with probability

q = (1− Perr)(1− pnt|ii)N−1, where (1− Perr) = πge−vg(L+v+Lack). This modied q

value should be applied to the node state model in the extended analysis.

In the modied channel state model, the probability that the channel remains

idle for two consecutive backo slots pcii is computed by solving the steady state

equations of the DTMC and by computing the fraction of time spent in each chan-

nel state (similar to the channel model in Section 3.3.2). Thus, the probability

pcii of the extended analysis is given as

pcii =1

1 + (L+ 1)(1− α) + (1− Perr−data)(Lack + sdack)β. (3.12)

Considering (3.3), (3.12) and the expression pci = pcii/pci|i, the probability that

the channel is idle at any given backo slot pci can be obtained as

pci =1

L+ Lack + 2

[(L+ Lack)

1 + (L+ 1)(1− α) + (1− Perr−data)(Lack + sdack)β+ 2

]. (3.13)

In the extended analysis, the expressions for pci|i, pnt and pnt|ii remain same as

those in Section 3.3. However, their values have been changed due to modi-

cations made to pci . Once pci|i, pnt and pnt|ii have been computed, the extended


analysis formulates a consistent system of equations (which includes (3.1)(3.4),

(3.12), (3.13), steady state equations for the node model, and the expressions for

α, β and q) that can be solved numerically.

3.6 Performance Evaluation

The system performance considered in this chapter are the aggregate network

throughput, average power consumption per node, frame discard ratio and frame

delivery ratio per node. Based on the proposed analytical models, they are derived

as follows.

3.6.1 Aggregate Network Throughput

The aggregate network throughput S can be derived considering the fraction of

time the channel spent in successful data transmission S, which is given as

S =Lπcsuccess

πcidle,idle + Lπcsuccess + Lπcfailure + sdackπcack−idle + Lackπcack−tx + πcidle(3.14)

using the steady state probabilities and the dwell times of each state of channel

models.

For systems with ideal channel conditions, the aggregate network throughput

equals S as there are no duplicate data frame transmissions in the channel. There-

fore, the aggregate network throughput of systems with ideal channel conditions

can be given as

Sideal−channel =Lβ

1 + (L+ 1)(1− α) + (Lack + sdack)β(3.15)

by simplifying (4.38).

On the other hand, for the systems with channel errors, S represents the

fraction of time the channel spent in both original and duplicate data frame


transmissions. Duplicate frames, which are transmitted due to errors in ACK

frame reception, do not contribute to the aggregate network throughput as they

are rejected by the receiving node's MAC layer. Thus, the aggregate network

throughput of systems with channel errors is computed as

Serror−channel =(1− Perr)Lβ

1 + (L+ 1)(1− α) + (1− Perr−data)Lackβ

=πge−vg(L+Lack)Lβ

1 + (L+ 1)(1− α) + (πge−vgL)Lackβ(3.16)

by eliminating duplicate frames' contribution to S.

In both systems, the aggregate network throughput represents a normalised

value and is dimensionless.

3.6.2 Average Power Consumption

The average power consumption of a node is derived by considering the Chip-

con CC2420 802.15.4 RF transceiver1. Energy characteristics of the CC2420

transceiver are illustrated in Figure 3.6 [126][149], while the relationships be-

tween transceiver states and node's activities in each analytical model are listed

in Table 4.2.

The eect of beacon reception may be ignored during throughput calculations,

since beacons occupy a very small fraction of the time. However, neglecting

beacon receptions may not be justied for power calculations as nodes consume

a considerable amount of energy on beacon reception. Therefore, the beacon

reception time is deducted from the node's dwelling time in IDLE state, and

the power consumption budget is adjusted accordingly. Similar adjustments are1Chipcon CC2420 transceiver, which is widely used in many WSN products including Cross-

bow technology's Micaz motes, is selected as a representative of commercial IEEE 802.15.4 RFtransceivers. However, the proposed analytical model does not depend on this selection. Powerconsumption of any other IEEE 802.15.4 transceiver can be predicted by applying the energycharacteristics (i.e., power consumption at each states and transition power and time betweendierent states) of that transceiver to the analysis appropriately.


Figure 3.6: Energy states and transitions of CC2420 transceiver [126][149].

Table 3.2: States of the CC2420 transceiver: Beacon-enabled mode.

State Description Related model statesAnalytical/Extended† Simplied

Sleep Idling IDLE IDLEIdle Backing o BOxyz BOy

Receive Carrier Sensing CSxyz CSyzReceiving ACK ACKx ACK

Transmit Transmitting TXx TX† Since the analytical model presented in Section 3.3 and the extended modelpresented in Section 3.5 share the same node state model, they are placedunder the same column.

made to the transitions among the transceiver states. Furthermore, the radio

ramp-down times are assumed to be negligible. Based on above assumptions, the

average power consumption of a node Yav can be expressed as

Yav = (pni − pnbcn − pnsi)YSleep + (pnbo − pnir + pnsi)YIdle

+(pncs + pnbcn + pnack + pnir)YRx + pntxYTx.(3.17)

In (3.17), YSleep, YIdle, YRx and YTx are the power expenditures correspond-

ing to the transceiver's Sleep, Idle, Receive and Transmit states (which are

explicitly depicted in Figure 3.6) ; pnbcn denotes the fraction of the time spent in

receiving beacons; pnsi and pnir denote those of transceiver's Sleep to Idle transi-

tion and Idle to Receive transition; pni , pnbo, p

ncs, p

nack and p

ntx represent the fraction

of time spent by a node in idling, backing o, carrier sensing, receiving ACK and


transmitting, respectively. These parameters are calculated in Appendix B.1 for

each analytical model.

3.6.3 Data Transmission Reliability

The reliability of data transmission is evaluated using two performance metrics:

frame discard ratio ρ and frame delivery ratio η.

Frame discard ratio ρ: This is the ratio between the number of discarded

frames at the MAC layer and the number of frames arrived at the MAC layer to

transmit. Hence, it can be expressed as in (3.18) for a given period of time.

ρ =number of discarded frames

number of successful transmissions + number of discarded frames(3.18)

In the analytical model presented in Section 3.3 and the extended analytical

model presented in Section 3.5, frames are discarded either due to y consecutive

channel access failures or to x consecutive transmission failures. Considering node

state DTMCs of these models, the probability of frame discarding due to channel

access failures Pdiscard−CCA−fails and the probability of frame discarding due to

transmission failures1 Pdiscard−TX−fails at a node in a given backo slot can be

derived as in (3.19) and (3.20), respectively.

Pdiscard−CCA−fails =

∑xx=1

[(1− pci)π(csxy0) + (1− pci|i)π(csxy(−1))

]∑x

x=1 [1 + (L− 1)π(txx) + (Lack + sdack − 1)π(ackx)](3.19)

Pdiscard−TX−fails =(1− q)(Lack + sdack)π(ackx)∑x

x=1 [1 + (L− 1)π(txx) + (Lack + sdack − 1)π(ackx)](3.20)

Pdiscard−Simplified =

[(1− pci)π(csy1) + (1− pci|i)π(csy2)

][1 + (L− 1)π(tx) + (Lack + sdack − 1)π(ack)]

(3.21)

Thus, in these two models, the `total probability of frame discarding' Pdiscard1In the analytical model, transmission failures only occur due to frame collisions (owing to

the ideal channel assumption). In contrast, transmission failures in the extended model occureither due to frame collisions or to channel errors.


at a node in a given backo slot can be given as Pdiscard = Pdiscard−CCA−fails +

Pdiscard−TX−fails.

On the other hand, in the simplied model presented in Section 3.4, frames

are discarded only due to channel access failures as the model assumes limitless

retransmission attempts. Therefore, in this model, the probability of frame dis-

carding at a node in a given backo slot Pdiscard−Simplified can be computed as in

(3.21).

Then, by applying (3.18) for a unit backo slot, ρ can be quantied as

ρ =PFrame−discard

(S/NL) + PFrame−discard, (3.22)

where S, N and L represent the aggregate network throughput, number of nodes

in the network and frame length, respectively. PFrame−discard should be substi-

tuted by either Pdiscard or Pdiscard−Simplified depending on the analytical model

considered.

Frame delivery ratio η: This is the ratio between the number of successful

frame transmissions and the total number of frame transmissions. The total

number of frame transmissions can be obtained by adding the number of frames

transmitted successfully and the number of frames discarded due to transmission

failures (Note: the frames discarded due to channel access failures have not been

considered as they were not transmitted). Therefore, by considering a unit backo

period, η can be given as

η =S/NL

(S/NL) + Pdiscard−TX−fails, (3.23)

where S, N and L have their respective meanings as above. In the simplied

model, always η = 1 as there are no transmission failures. Therefore, only the

analytical models presented in Sections 3.3 and 3.5 will be considered to compute

η.


3.7 Results and Discussion

This section presents numerical results obtained from each analytical model to in-

vestigate the performance of beacon-enabled IEEE 802.15.4 networks under the

inuence of dierent network parameters, MAC-layer parameters and channel

conditions. First, the proposed analysis is validated using a system with ideal

channel conditions. Then, the same system is used to investigate the eects

of network parameters (frame arrival rate λ, number of sensor nodes N , frame

length L) and MAC-layer parameters (macMaxFrameRetries, macCSMABack-

os, macMinBE, and macMaxBE ) on the performance of system. Finally, a sys-

tem with channel errors is considered to discuss the eects of erroneous channel

conditions on system performance.

3.7.1 Validation of Analysis

In this section, the analytical models proposed in Sections 3.3 and 3.41 are vali-

dated using a two-fold process. First, the analytical results are veried using ns-2

simulations. Second, they are compared and contrasted with the results of an ex-

isting analysis [126], which models the same system without ACK transmission.

For the validation of analysis, a beacon-enabled (BO = 6), star topology net-

work of 10 sensor nodes was considered. Each node of this network generated

frames of length 10 backo slots based on a Poisson arrival rate of λ frames

per frame duration. MAC-layer parameters were assumed to have their default

values as specied in the standard [61] (i.e., macMinBE =3, macMaxBE =5,

macMaxCSMABackos =4 and macMaxFrameRetries =3). In analytical mod-

els, the beacon duration tbcn, ACK frame length Lack and starting delay sdack

of ACK transmission were set to 2, 2 and 1 backo slots, respectively (assum-

ing 2.4-GHz PHY layer). For all ns-2 simulations, IEEE 2.4-GHz PHY layer

1The extended analytical model presented in Section 3.5 will be validated while investigatingthe eects of erroneous channel conditions in Section 3.7.4.


was used along with the two-ray ground propagation model. A simulation trial

ran until each node completes 20, 000 frame transmissions. Each simulation data

point was averaged over 20 independent simulation trials using dierent random

seeds. Approximations used for the analytical models were not considered for

simulations.

(a) S (b) Yav

(c) ρ (d) η

Figure 3.7: Performance of beacon-enabled IEEE 802.15.4 networks with andwithout ACK transmission (N = 10 and L = 10 backo slots).

Performance of the protocol (in terms of the aggregate network throughput S,

power consumption per node Yav, frame discard ratio ρ and frame delivery ratio


η) obtained from both analytical models and simulations are compared in Figure

3.7. The close agreement between simulations and analytical results demonstrates

the validity of the proposed models. Results obtained for an equivalent network-

without-ACK [126] are also included in Figure 3.7 for comparison.

For both network scenarios (i.e., with and without ACK), the aggregate

throughput S remains almost the same at low λ values where data frames collide

rarely. On the other hand, a network-with-ACK shows a less aggregate through-

put than the network-without-ACK at high λ values. This is because nodes in the

network-with-ACK have to share the common channel not only to transmit data

frames but also to receive ACK frames and to retransmit collided data frames.

Despite having dierent face values, the aggregate throughput of both networks

follow a similar trend. They maximise at λ ≈ 0.2, and then decrease slowly

towards the saturation region (i.e., λ ≥ 1) as shown in Figure 3.7(a).

In contrast to the throughput results, the average power consumption per

node Yav in the network-with-ACK is greater than that of its counterpart (Fig-

ure 3.7(b)). This is caused by the excess energy consumed while receiving ACK

frames and retransmitting collided data frames. The frame discard ratio ρ of both

network scenarios have a similar behaviour as shown in Figure 3.7(c). In both

networks, ρ is insignicant in the low λ region; however, it escalates rapidly with

increasing λ. Although the face values of ρ in the two networks dier marginally,

their compositions vary largely from each other. While the network-without-ACK

discards a large amount of data frames due to collisions1, in the network-with-

ACK data frames are dropped mainly due to channel access failures. It appears

that the retransmission mechanism forces a large amount of frames to be discarded

due to lack of buering. Nevertheless, the retransmission mechanism assures suc-

cessful delivery of transmitted frames as depicted in Figure 3.7(d). On the other

hand, the network-without-ACK, which has approximately similar ρ values with1In systems with ideal channel conditions, transmission failures occur only due to frame

collisions. Therefore, `the frames discarded, due to transmission failures' are denoted as `theframe discarded due to collisions' throughout this section.


its counterpart, performs poorly in frame delivery due to the absence of a frame

retransmission mechanism.

Figure 3.7 also explains the implications of the simplications proposed in Sec-

tion 3.4. Because of the approximation of endless retransmissions, the simplied

mode does not drop frames due to collisions. In contrast, dropped frames due to

collisions1 can be seen in the full analytical model where retransmission is limited

(Figure 3.7(c)). Therefore, the simplied model always yields 100% frame deliv-

ery as shown in Figure 3.7(d). Moreover, due to approximated backo counter

(based on geometric distribution), nodes in the simplied model back o slightly

more than nodes in the full analysis. This increased backing o time and extra

retransmissions may cause a marginal reduction in average network throughput

(Figure 3.7(a)) and a marginal increase in power consumption (Figure 3.7(b)).

The above implications of the simplied model can also be observed in further

results presented in coming sections.

3.7.2 Eects of Network Parameters

This section investigates the eects of network parameters - in particular the

frame length and number of nodes - on system performance. Except the values

of frame length L and number of nodes N , the same system parameter values

mentioned in Section 3.7.1 were applied for this investigation.

Figure 3.8 illustrates the system performance obtained for three dierent

frame lengths: 5, 8, and 12 backo slots. The aggregate throughput of networks

with dierent frame lengths are similar in the low λ region. This is because net-

works with short frame length compensate their short channel occupancy (per

transmitted frame) by transmitting more frames than their counterparts in a

given period of time2. However, when λ increases, more transmissions cause1Even though, frame dropped due to collision is negligible compared with that due to

channel congestion2For a given λ, short frame lengths yield more frame arrivals, since λ is expressed per frame

length.


(a) S (b) Yav

(c) ρ (d) ρ and its composition

Figure 3.8: Eects of frame length L on the performance of beacon-enabled IEEE802.15.4 networks (when N = 10).

more frame collisions which in turn result more frame retransmissions. There-

fore, in high λ region, networks with short frames exhibit reduced throughput

than their counterparts with long frames (Figure 3.8(a)). The higher the number

of frames to transmit the higher the energy used for backing o, channel sensing,

transmitting and ACK receiving. This explains the high power consumption per

node observed in networks with shorter frames throughout the entire range of λ.

Similar to throughput results, the frame discard ratio ρ shows almost the same

behaviour in networks with dierent frame lengths at low frame arrival rates.


However, at high λ values, networks with long frames discard more frames due

to channel access failures as the `probability of channel is sensed busy' in such

networks escalates with increased λ.

(a) S (b) Yav

(c) ρ (d) ρ and its composition

Figure 3.9: Eects of number of nodes N on the performance of beacon-enabledIEEE 802.15.4 networks (when L = 10 backo slots).

Eects of the number of nodes N on system performance is depicted in Fig-

ure 3.9. At low frame arrival rates, the aggregate throughput increases with the

number of nodes while the power consumption per node remains unchanged (Fig-

ures 3.9(a) and (b)). This behaviour can be mainly contributed to two reasons.

First, each nodes of all considered networks generates a similar number of frames


for a given λ value. Second, all most all frames are successfully transmitted in the

low λ region irrespective of the number of nodes in the network (Figures 3.9(c)

and (d)).

The larger the number of nodes, the sooner the network reaches the peak in

aggregate throughput. After reaching its peak, the throughput of larger networks

start decreasing towards saturation. However, in this region of λ, the aggregate

throughput of small networks keep on increasing towards their respective peaks,

which means the number of frames transmitted by a node in small networks could

be greater than that of large networks in this region. This explains the higher

power consumption per node observed in small networks in the same region.

For all networks considered, the number of collisions and channel occupancy

increase with λ yielding a rapid escalation in the frame discard ratio. However,

the situation is worst in larger networks as more nodes lead to more collisions

and higher channel occupancy as shown in Figures 3.9(c) and (d).

3.7.3 Eects of MAC-Layer Parameters

In this section, the proposed analytical models are used to investigate the eects

of dierent MAC-layer parameters including the number of frame retransmission

attempts (macMaxFrameRetries), number of backo stages (macCSMABackos)

and length of backo window (macMinBE, and macMaxBE ) on system perfor-

mance. The system parameters mentioned in Section 3.7.1 were applied for

these investigations with necessary modications to MAC parameters mac-

MaxFrameRetries, macCSMABackos, macMinBE, and macMaxBE as sum-

marised in Table 3.3. Furthermore, number of nodes N and frame length L were

set to 10 and 10 backo slots, respectively. Both analytical and simulation results

were obtained for ve dierent λ values that represent the entire range of frame

arrival rates.

Figures 3.10(a) 3.10(d) show the behaviour of system performance for three


Table 3.3: MAC-layer parameter values for dierent investigations.

Investigation Investigation Investigationon frame on number on length

MAC Parameter retransmission of backo of backoattempts stages window

macMaxFrameRetries (1,3,5) 3 3macMaxCSMABackos 4 (1,3,5) 4macMinBE 3 3 (2,3,4)macMaxBE 5 5 (3,5,8)

(a) S (b) Yav

(c) ρ (d) η

Figure 3.10: Eects of macMaxFrameRetries on the performance of beacon-enabled IEEE 802.15.4 networks.

dierent macMaxFrameRetries values: 1, 3 and 5. For comparison, these gures

also include the system performance related to boundless retransmission attempts,


(a) S (b) Yav

(c) ρ

Figure 3.11: Eects of macMaxCSMABackos on the performance of beacon-enabled IEEE 802.15.4 networks.

which was obtained from the simplied analytical model. The number of frame

retransmission attempts appears to have no signicant impact on the aggregate

throughput and power consumption of nodes as they exhibit identical behaviour

for dierent macMaxFrameRetries. In contrast, a noticeable increase in the num-

ber of `frames discarded due to collisions' can be observed with decreasing mac-

MaxFrameRetries. This eect is further illustrated in Figure 3.10(d) in which the

lowest macMaxFrameRetries value is shown to be ineective in delivering frames

successfully (in particular at high λ values). For low macMaxFrameRetries val-

ues, this behaviour is self-explanatory as the collided frames (i.e., the frames


which were not succeeded by corresponding ACKs) are easily discarded without

getting further chance for retransmission.

Eects of the maximum number of backo stages on system performance

is depicted in Figures 3.11(a) 3.11(c)1. Clearly, the higher the macMaxCS-

MABackos value the lower the number of frame discarded due to channel access

failures (Figure 3.11(c)). Although this signicant decline in frame discard ra-

tio should lead to a rapid escalation in aggregate throughput, only a moderate

increase can be observed in throughput with increased macMaxCSMABackos

(Figure 3.11(a)). This is because networks with less number of backo stages

transmit more frames than their counterparts, since low macMaxCSMABackos

values make nodes ready to receive frames from upper layers more frequently2.

Thus, for a given period of time, nodes with less number of backo stages spend

more time on channel sensing (i.e., radio in receiving state) than nodes with

higher number of backo stages, which spend more time on backing o (i.e., ra-

dio in idle state). This explains the increased average power consumption per

node observed with decreasing macMaxCSMABackos in the high λ region (Fig-

ure 3.11(b)).

Next, the impact of the backo window length on system performance is

studied considering three dierent combinations of macMinBE and macMaxBE

values: (2,3), (3,5) and (4,8). Results of this study are shown in Figure 3.12.

Networks with shorter backo windows experience an excessive amount of frame

discarding mainly due to channel access failures as shown in Figure 3.12(c). This

is because shorter backo windows force nodes to perform channel sensing more

frequently. On the other hand, networks with longer backo windows have the

benet of delaying frame transmissions, and hence they rarely discard data frames

due to channel access failures. This explains the increased aggregate throughput1In Figures 3.11 and 3.12, the eects of the number of backo stages and backo window

length on η are not shown explicitly. η remains ≈ 1 with varying values of these two parameters,since the amount of frames discarded due to collisions is negligible as shown in Figures 3.11(c)and 3.12(c).

2By forcing nodes to discard frames instead of keeping them backing o.


(a) S (b) Yav

(c) ρ

Figure 3.12: Eects of the backo window length on the performance of beacon-enabled IEEE 802.15.4 networks.

of such networks observed particularly in the high λ region, in which the channel

is busy in most of the time. Due to frequent channel sensing, nodes in networks

with shorter backo windows consume more energy than their counterparts in

networks with longer backo windows. This increment is more signicant at

higher frame arrivals due to the escalation of oered trac.

Comparing the eects of aforementioned MAC-layer parameters (i.e., number

of frame retransmission attempts, number of backo stages and length of the

backo window) on system performance, it can be observed that all of them have

a similar impact on the aggregate network throughput (Figures 3.10, 3.11 and


3.12). On the other hand, the backo window length appears to have a higher

impact on system performance in terms of average power consumption and frame

discard ratio than the other two parameters. Therefore, it is recommended to

tune the backo window length before other two MAC parameters to achieve

better performance (related to power consumption and transmission reliability)

in a given beacon-enabled IEEE 802.15.4 network with ACK transmission.

3.7.4 Eects of Channel Errors

This section investigates the eects of channel errors on the performance of

beacon-enabled IEEE 802.15.4 protocol. In addition, it validates the extended

analysis presented in Section 3.5 using numerical and simulation results. To this

end, the same system mentioned in Section 3.7.1 was considered with three dif-

ferent non-ideal channels : channel 1 (v−1g = 100ms, v−1b = 10ms), channel 2

(v−1g = 50ms, v−1b = 10ms) and channel 3 (v−1g = 20ms, v−1b = 10ms), where v−1g

and v−1b represent the mean duration of good and bad states, respectively. Similar

channel characteristics have been used to model erroneous channels in [141].

Figure 3.13 illustrates the eects of channel errors on system performance.

Performance of an equivalent network with ideal channel conditions, obtained

from the analysis in Section 3.3, is also presented for comparison. The per-

formance of networks with channel errors exhibit similar trends to that of the

network with ideal channel. However, as expected, the system performance de-

grades with rising channel errors as shown in Figure 3.13. This performance

deterioration can be mainly attributed to the increased frame retransmissions

caused by channel errors, which in turn increase the average time that frames

dwell in transmitter-nodes. Longer average dwell times increase frame discarding

due to channel access failures while reducing oered trac due to lack of buer-

ing. Therefore, the frame discard ratio ρ increases notably with rising channel

errors causing a signicant throughput reduction. On the other hand, the average


(a) S

(b) Yav (c) ρ

Figure 3.13: Eects of channel errors on the performance of beacon-enabled IEEE802.15.4 networks (N = 10 and L = 10 backo slots).

power consumption per node Yav exhibits only a marginal increment with poor

channel conditions. This is because the additional power consumed for frame

retransmissions is neutralised by the power conserved due to reduction of oered

trac. As shown in Figure 3.13, the close agreement between analytical results

and simulations validates the extended analysis for the entire range of λ and

erroneous channel conditions considered.

Outcomes of this chapter have been published in [150]-[153].


3.8 Conclusion

ADTMC based analysis is presented to analyse the beacon-enabled IEEE 802.15.4

MAC protocol by integrating the characteristics of ACK transmission and frame

retransmissions into one of the existing models. Making some approximations,

the proposed model is simplied to a mathematically less complex model with the

cost of marginal aws in performance (≤ |5|% of deviation from original results).

Furthermore, an extended version of the proposed model is presented to analyse

the performance of IEEE 802.15.4 networks operating under non-ideal channel

conditions. The proposed analytical models can be used to derive the perfor-

mance of protocol including the aggregate network throughput, average power

consumption per node, frame discard ratio and frame delivery ratio. Numerical

results obtained from the proposed models are validated using ns-2 simulations.

Due to the generalised nature of the proposed models, they can be used to analyse

the eects of dierent network and MAC-layer parameters on the performance of

protocol.

Chapter 4

Analysis of Non-beacon-enabled

IEEE 802.15.4 MAC Protocol

4.1 Introduction

WSNs have been deployed in many event detection (ED) applications where

events occur randomly and rarely [154]-[156]. In such applications, the nodes'

transceivers wake up only in the presence of events, and remain switched o dur-

ing long inter-event durations to conserve energy. Thus, the periodic transmission

of beacons may not complement with the nature of these monitoring applications.

Therefore, the non-beacon enabled mode of the IEEE 802.15.4 protocol is pre-

ferred over the beacon enabled mode for such event monitoring applications.

In literature, the performance of non-beacon enabled IEEE 802.15.4 networks

has been evaluated using simulations [157], experiments [158] and analytical mod-

els [159]-[171]. An early attempt to determine the upper limits of the throughput

and delay performance of the non-beacon enabled mode can be found in [159], in

which a single communication link between a transmitter-receiver pair is analysed.

Recently, the performance of the non-beacon enabled mode has been evaluated

in the context of a network of nodes, by considering more than one communica-

4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 74

tion link [160]-[171]. Among these studies, the models presented in [160]-[163],

and [164] consider two successive CCAs similar to the beacon enabled mode,

and hence fail to model the non-beacon enabled protocol accurately. Kim et al.

[89] have modelled the protocol using a M/G/1 queuing system by overlooking

the collisions in data transmission. The analyses presented in [165]-[167] have

evaluated the performance of the IEEE 802.15.4 non-beacon enabled mode for

networks with specic behaviours. More specically, Buratti and Verdone [165]

analyse the protocol in a star topology network where all data transmissions are

triggered by a request from the network coordinator. Gribaudo et al. [166] carry

out a transient analysis of the protocol for sensor networks deployed in k-coverage

applications. In [167], Fischione et al. have modelled the performance of the non-

beacon enabled mode in a cluster network topology, in which the IEEE 802.15.4

protocol is used on top of a preamble sampling MAC.

The comprehensive analyses presented in [168] and [169] have assumed a single

backo slot as the basic time unit of the protocol's time evolution. Thus, they

are unable to integrate specic time constraints such as CCA duration and node's

receive to transmit (RX-to-TX) turnaround time, which have signicant impacts

on the protocol performance, into the analyses. Whilst, the analysis in [168]

overlooks the collisions between data frames and ACK frames1, the model in

[169] does not consider ACK transmission at all.

On the other hand, the protocol's continuous time evolution, and non-negligible

CCA duration and Rx-to-Tx turnaround have been taken into account in the

studies presented in [170] and [171]. In [170], only the saturated throughput per-

formance of the protocol has been evaluated using a semi-Markov process based

model. Given the number of nodes competing for channel access and their frame

generation rates, Goyal et al. [171] have analysed the frame loss probability and

transmission delay of the non-beacon-enabled IEEE 802.15.4 protocol. However,1Even though this type of collisions do not occur in the beacon enabled mode, it is a common

scenario in the non-beacon-enabled mode as discussed in Section 4.2.


neither of these analyses derives the energy performance of the protocol, which

is vital in the context of WSNs.

This chapter presents a Markov chain based analysis to model the non-beacon-

enabled IEEE 802.15.4 MAC protocol under unsaturated trac conditions. First,

the unslotted CSMA/CA protocol - the only channel access mechanism specied

for the non-beacon-enabled mode - is analysed without considering optional ACK

transmissions. Then, the proposed analysis is extended to model the advanced

behaviour of the protocol in the presence of ACK transmissions. The proposed

models meticulously follow the specications for the IEEE 802.15.4 non-beacon-

enabled mode by considering a single CCA procedure and nite time durations

involved in CCA and node's Rx-to-Tx turnaround. The performance of the proto-

col in terms of the aggregate network throughput, power consumption per node,

frame discarding ratio and frame delivery ratio is evaluated using the proposed

analysis. The validity of the analysis is demonstrated over a range of networks

with varying number of nodes and frame lengths. Numerical results are substan-

tiated through extensive ns-2 simulations.

4.2 Collision of Transmissions

One of the major drawbacks in contention based MAC protocols is the collision

of transmissions of contending nodes. Both CSMA/CA mechanisms (i.e., slotted

and unslotted) specied in the IEEE 802.15.4 standard provide counter measures

(e.g., random backos and CCAs) to minimise the collision of transmissions.

However, collisions can not be completely eliminated due to the following reasons:

• Simultaneous beginning of transmissions in two or more nodes,

• Non-negligible Rx-to-TX turnaround time,

• Presence of hidden nodes (i.e., nodes that are not within the carrier sensing

range of a given node).


Collisions due to hidden nodes may occur in any contention based wireless net-

work. Their impact on the performance of IEEE 802.15.4 based networks will

be investigated separately in Chapter 5. Thus, the discussion in this section is

conned only to the collisions occurred due to the simultaneous beginning of

transmissions or non-negligible Rx-to-Tx turnaround time. The nature of these

collisions in the slotted and unslotted CSMA/CA mechanisms is shown in Figure

4.1 considering the transmissions of two contending nodes (Node A and Node B).

(a) Collisions in slotted CSMA/CA

(b) Collisions among data frames in unslotted CSMA/CA

(c) Collisions between ACK frames and data frames in un-slotted CSMA/CA

Figure 4.1: Collision of transmissions: slotted CSMA/CA vs. unslottedCSMA/CA.


In the slotted mechanism, collisions do not occur due to the non-negligible

Rx-to-Tx turnaround time, since the discrete time unit (i.e., unit backo slot)

of that mechanism is greater than the turnaround duration1. Thus, a collision

occurs if and only if two or more nodes perform their rst CCA at the same backo

slot, which causes a simultaneous beginning of frame transmissions as shown in

Figure 4.1(a). Due to the discrete time evolution of the slotted mechanism, the

only possible time to encounter a beginning of a collision is the same backo slot

boundary that the frame transmission begins (Figure 4.1(a)).

On the other hand, collisions in the unslotted mechanism may occur due to

the simultaneous beginning of transmissions as well as the nite time of Rx-

to-Tx turnaround. However, the continuous time evolution and unsynchronised

behaviour of the mechanism reduce the possibility of beginning of simultaneous

transmissions, causing the Rx-to-Tx turnaround to be the main reason behind

the collisions. The non-negligible Rx-to-Tx turnaround time in the unslotted

mechanism may cause not only collisions among data frames but also collisions

between ACK frames and data frames2 as described below:

Collisions among data frames: Suppose Node A nishes its backo phase

before the node B at time t (Figure 4.1(b)). Then Node A performs a CCA

and Rx-to-Tx turnaround for tcca (8 symbols) and tta (aTurnaroundTime = 12

symbols) durations, respectively. Despite the Node A's activities, the channel will

remain idle until time t+ tcca+ tta at which Node A begins its frame transmission.

Meanwhile, if Node B begins its CCA at time t′, where t ≤ t′ ≤ (t + tta), it

would succeed and the subsequent frame transmission would collide with Node

A's transmission3. In contrast to the slotted mechanism, the possible time to

encounter a beginning of a collision for a given frame in the unslotted mechanism1Rx-to-Tx turnaround takes 12 symbol durations (= 0.6 unit backo slots).2In the slotted mechanism, collisions between ACK frames and data frames are avoided by

using two back-to-back CCAs.3When node B begins its CCA simultaneously with Node A at time t, the subsequent

collision occurs due to the simultaneous beginning of transmissions.


is not an instant but a window of time. For collisions among data frames, this

window is equal to tta (i.e., aTurnaroundTime symbols), and it will be denoted

as the First Collision Window throughout this chapter (Figure 4.1(b)).

Collisions between ACK frames and data frames: Suppose Node A

has completed its data frame transmission successfully at time t. Then, the

destination node needs to complete a Rx-to-Tx turnaround before sending the

corresponding ACK frame. If node B begins a CCA at time t′, where t ≤ t′ ≤

(t + tta − tcca), that CCA would succeed and a subsequent data transmission

would collide with the ACK transmission destined towards Node A as shown in

Figure 4.1(c). The possible time window to begin this type of collision equals to

(tta − tcca) symbol durations, and it will be referred to as the Second Collision

Window throughout this chapter (Figure 4.1(c)).

In an ACK-enabled IEEE 802.15.4 based network, the impact of a collision

between an ACK frame and data frames is almost same as that of a collision

between two data frames, because in both collision scenarios all associated nodes

have to retransmit their respective data frames (i.e., in the scenario depicted

in Figure 4.1(c), not only Node B but also Node A has to retransmit its data

frame, even though Node A's data frame has been received successfully at the

destination node).

Thus, the success of data transmission in non-beacon-enabled IEEE 802.15.4

networks depends not only on sensing the idle channel successfully, but also on

commencing the CCA at proper time instants. Therefore, this chapter analyses

the non-beacon-enabled IEEE 802.15.4 MAC protocol based on the following

basic probabilities:

• The probability that a node begins a CCA in a given time instant,

• The probability that the channel is sensed idle.

These two probabilities will be determined for the system model described next.


4.3 System Model

Consider a non-beacon-enabled IEEE 802.15.4 single-hop star topology network

that consists of a network coordinator and N sensor nodes. The network coor-

dinator acts as a common receiver for all sensor nodes, and all nodes are within

the carrier sensing range of each other. Data transmission within the system is

assumed to be in the uplink direction. Ideal channel conditions are assumed, so

that transmitted data frames can be lost only due to frame collisions. All data

frames are equal in length, and the transmission of a data frame lasts for xed-L

backo slots. Data frames arrive at the nodes according to a Poisson distribution

with an arrival rate of λ frames per frame duration. Buering at the nodes is

not considered; therefore, a node that already holds a frame will discard further

frame arrivals. Moreover, the capture eect [142][143] is not implemented for

collided frames, i.e., all collided frames are dropped regardless of their received

signal strengths at the common receiver.

4.3.1 Approximations

In the system considered, the computation of the basic probability that a node

begins a CCA in a given time instant is mathematically intractable due to the con-

tinuous time evolution of the unslotted IEEE 802.15.4 MAC protocol1. Therefore,

for tractability, the continuous time evolution of the protocol is approximated by

a discrete time evolution where the discrete time unit is equal to one symbol

duration. These discrete time units are referred to as mini-slots, and all nodes

are assumed to be synchronised at mini-slot boundaries. Similar approximations

have been used in [164] and [166] to model the unslotted CSMA/CA protocol.

Although the unslotted protocol with new time discretisation resembles a slot-1This is the shortened form used to refer to the `unslotted CSMA/CA mechanism of the

IEEE 802.15.4 MAC protocol' in this chapter.


Figure 4.2: Timing of starting the same event in dierent protocols.

ted protocol, the diminutive duration of a mini-slot1 ensures an analogy between

the proposed time discretisation and the timing of events in the unslotted IEEE

802.15.4 protocol (see Figure 4.2).

Because of the proposed discrete time evolution, the `probability that a node

begins a CCA in a given time instant ' can be represented by the `probability that

a node begins a CCA in a given mini-slot '. For the simplicity of the analysis

this probability is approximated by the steady state probability pncca that a node

begins a CCA. In [170] and [172], it has been shown that the other basic model

probability - the probability that the channel is sensed idle - varies depending

on the current backo stage of the channel sensing node. However, results of

the same studies demonstrate that this variation is signicant only in networks

where the number of nodes N < 10. Therefore, in this chapter, the `probability

that the channel is sensed idle' is assumed constant, and it is approximated by

the steady state probability pci that the channel is idle for 8 consecutive mini-

slots (i.e., duration of a CCA). These approximations provide the basis for the

following analyses.1= (1/20) × backo slot , where backo slot is the discrete time unit of the slotted IEEE

802.15.4 protocol.


4.4 Analytical Model without ACKs

This section analyses the unslotted IEEE 802.15.4 MAC protocol in the absence of

ACKs by formulating two DTMCs to model the behaviour of an individual node

and the common channel. The two DTMCs are coupled and solved numerically

for the basic model probabilities pncca and pci to evaluate the performance of the

protocol.


Consider a node that follows the unslotted IEEE 802.15.4 MAC protocol to ac-

cess the channel. It behaves almost similar to a node in the rst transmission

attempt of the slotted protocol described in Chapter 3 Section 3.3.1. Thus,

the node behaviour can be modelled using a DTMC as shown in Figure 4.3.

The node begins the backo mechanism when it receives a frame to transmit,

which happens with probability p (i.e., the probability of frame arrival in a given

mini-slot, p = λ/20L). Node's backing o is denoted by BOyz states, where

y ∈ 1, 2, ...,macMaxCSMABackos + 1 and z ∈ 1, 2, ..., wy−11 represent the

current backo stage and backo counter value, respectively. The node resides in

each BOyz state for a single backo slot duration (i.e., 20 mini-slots).

When the rst backing o expires, the node moves to CS1 state, which rep-

resents the 8 mini-slot long channel sensing state2 of the rst backo stage. If

the channel is found to be idle during the sensing, which occurs with probabil-

ity pci , the node enters the TA state representing node's transceiver Rx-to-Tx

turnaround. After expiring the 12 mini-slot turnaround time, the node moves to

TX and resides 20L mini-slots in there to transmit the data frame. At the end of1wy = 2BEy , where BEy is the backo exponent of the yth backo stage.)2In the slotted CSMA/CA analytical models presented in Chapter 3, the channel sensing

states (i.e., CSxy) were considered one backo slot long (=20 symbol durations), since allMAC layer events in the slotted mechanism occur at the boundaries of backo slots. Thatrepresentation can be explained as follows: For a successful channel sensing CSxy representschannel sensing + Rx-to-Tx turnaround (8+12 = 20 symbols). Otherwise CSxy representschannel sensing + remaining time to begins the next backing o (8+12 = 20 symbols).


Figure 4.3: DTMC model for node without ACKs. The steady state probabilitypci is denoted by a .

the data transmission, node moves back to the IDLE state. On the other hand, if

the channel had been found busy when the node was in the CS1 state, the node

moves to the next backo stage and continue the backo procedure. This mech-

anism is repeated up to y backo stages, where y = macMaxCSMABackos + 1.

If the channel is found busy in the CSy state, the node declares a channel access

failure and moves back to the IDLE state. Possible node states and their dwell

times are summerised in Table 4.1.

Table 4.1: Node states and their dwell times.

State Description Dwell time(in mini slots)

IDLE Idling 1BOyz Backing o 20CSy Channel sensing 8TA Rx-to-Tx turnaround 12TX Transmitting 20L


Based on the aforementioned state transitions, the steady state equations of

the node model DTMC (Figure 4.3) can be given as

π(idle) = (1− p)π(idle) + (1− pci)π(csy) + π(tx)

π(bo1z) = gpπ(idle)/w1 + hπ(bo1(z+1))

where (g, h) =

(0, 1) 1 ≤ z ≤ 2; (4, 1) z = 3

(1, 1) 4 ≤ z < w1 − 1; (1, 0) z = w1 − 1

π(cs1) = π(bo11)

π(boyz) = (1− pci)π(cs(y−1))/wy + hπ(boy(z+1))

where 2 ≤ y ≤ y; h =

1 1 ≤ z < wy − 1

0 z = wy − 1(4.1)

π(csy) = (1− pci)π(csy−1)/wy + π(boy1) where 2 ≤ y ≤ y

π(ta) = pci

y∑y=1

π(csy)

π(tx) = π(ta)

where w1 = 2macMinBE and wy = 2BEy . The notation π(state) represents the

long term proportion of transitions into STATE. The normalised condition of the

DTMC is also expressed as

π(idle) +

y∑y=1

wy−1∑z=1

π(boyz) +

y∑y=1

π(csy) + π(ta) + π(tx) = 1. (4.2)

Let θ = π(idle) and φ = (1 − pci). Then, the balanced equations in (4.1) are

rearranged to obtain

wy−1∑z=1

π(boyz) =wy − 1

2φy−1pθ where 1 ≤ y ≤ y (4.3)

π(csy) = φy−1pθ where 1 ≤ y ≤ y (4.4)


π(ta) = π(tx) = (1− φ)

y∑y=1

π(csy) = (1− φy)pθ. (4.5)

Thus, (4.2) can be simplied to

[1 +

y∑y=1

wy − 1

2φy−1p+

1− φy

1− φp+ 2(1− φ)yp

]θ = 1. (4.6)

Considering the steady state probabilities and dwell times of each DTMC state,

the steady state probability pncca that a node begins a CCA is obtained as

pncca =

∑yy=1 π(csy)

Λ, (4.7)

where Λ = θ+20∑y

y=1

∑wy−1

z=1 π(boyz)+8∑y

y=1 π(csy)+12π(ta)+20Lπ(tx). Then,

for a given network pncca can be expressed as a function of θ and φ by substituting

expressions (4.3), (4.4) and (4.5) in (4.7). Since θ is a function of pci through

the expression of φ and (4.6), the basic model probability pncca can be completely

determined by the other basic model probability pci . To compute the probability

pci , the common channel seen by all sensor nodes is analysed next.


The behaviour of the channel can be modelled using a Markov chain as shown

in Figure 4.4. Assume that the channel is in SUCC state, which represents the

channel during a successful data transmission. After a data transmission, the

channel remains idle until the beginning of the next data transmission, which

takes at least another 20 mini-slots (8 mini-slot CCA + 12 mini-slot RX-to-

TX turnaround). Therefore, the channel is certainly idle during the rst 20

mini-slots after a transmission; however, channel idleness may extend beyond

this guaranteed duration if none of the contending nodes attempt to access the

channel.


Figure 4.4: DTMC model for channel without ACKs.

The channel idleness is represented by IDLEC1 and IDLEC2 states as shown in

Figure 4.4. The IDLEC1 state represents the rst 19 mini-slots of the idle channel

after a data transmission. No data transmission begins during this state. The

remaining duration of the channel idleness is represented by the single mini-slot

long IDLEC2 state. Next transmission may or may not begin at the end of this

state depending on nodes' activities. The channel remains in the IDLEC2 state if

none of the nodes begin a transmission, which occurs with probability α. This

probability is same as the probability that none of the nodes begin a CCA 20

mini-slots ago. Hence, α = (1 − pncca)N . Conversely, if only one node begins

a transmission, the channel moves to the CW1 state with probability β where

β = Npncca(1− pncca)N−1. The CW1 state represents the rst mini-slot of the rst

collision window described in Section 4.2. On the other hand, if more than one

node begin transmissions the channel enters the FAIL state by experiencing a

collision.

After expiring the CW11 state, the channel enters to CW12 (i.e., second

mini-slot of rst collision window) given that none of the remaining nodes1 begin

transmissions. This happens with propagability γ = (1−pncca)N−1. If at least one

of the remaining node begins a transmission at the end of CW11 state, a collision1The channel being in the collision window implies that a node is already transmitting.


happens. Consequently, the channel falls back to the FAIL state. Following

similar arguments, the channel moves through the remaining mini-slots of the rst

contention window, which are denoted by the CW1i states where 1 ≤ i ≤ 12. If

there was no collision during the entire rst collision window, the channel enters

to the SUCC state. The channel resides (20L− 12) mini-slots in the SUCC state

to complete the data transmission. Conversely, the channel's dwell time in the

FAIL state varies from 20L to (20L+ 12) mini-slots depending on the time that

the collision began within the collision window. For simplicity, the dwell time

of the FAIL state is computed by taking the average of all possible values, and

hence it is given as (20L + 6) mini-slots. Respective dwell times of the channel

states are also shown in Figure 4.4.

According to the channel transition described above, the steady state equa-

tions and the normalisation condition of the channel state DTMC (Figure 4.4)

can be obtained as

π(idlec1) = π(succ) + π(fail)

π(idlec2) = απ(idlec2) + π(idlec1)

π(cw11) = βπ(idlec2)

π(cw1i) = γπ(cw1i−1) where 2 ≤ i ≤ 12 (4.8)

π(succ) = γπ(cw112)

π(fail) = (1− α− β)π(idlec2) + (1− γ)12∑i=1

π(cw1i)

π(idlec1) + π(idlec2) +12∑i=1

π(cw1i) + π(succ) + π(fail) = 1. (4.9)

By considering the dwell times of all channel states, the steady state probability

pci that the channel is idle for 8 consecutive mini-slots can be then expressed as

pci =1219

(19)π(idlec1) + π(idlec2)

Γ(4.10)


where, Γ = 19π(idlec1) + π(idlec2) +∑12

i=1 π(cw1i) + (20L − 12)π(succ) + (20L +

6)π(fail). The rst coecient (i.e., 12/19) of π(idlec1) at the numerator repre-

sents the fraction of time having 8 consecutive mini-slots during the IDLEC1 state.

In contrast, the corresponding coecient of π(idlec2) is 1, because being in IDLEC2

state guarantees the channel idleness for the previous 8 consecutive mini-slots.

The second coecient (i.e., 19) of π(idlec1) gives the dwell time of the IDLEC1

state in mini-slots.

By solving the steady state equations and normalisation condition in (4.8)

and (4.9), π(idlec2) is obtained as

π(idlec2) =1

2(1− α) + 1 + β(1−γ12

1−γ ). (4.11)

Furthermore, the steady state probabilities of the channel being in all the other

states can be expressed using π(idle2) as

π(idlec1) = (1− α)π(idlec2) (4.12)

π(cw1i) = βγi−1π(idlec2) where 1 ≤ i ≤ 12 (4.13)

π(succ) = βγ12π(idlec2) (4.14)

π(fail) = (1− α− βγ12)π(idlec2). (4.15)

Substituting (4.11) - (4.15) in (4.10), pci can be simplied to

pci =12(1− α) + 1

5(1− α)(5 + 4L) + β(1−γ12

1−γ − 18γ12) + 1. (4.16)

In (4.16), the probability pci has been expressed as a function of the other basic

model probability pncca through α, β and γ. Finally, (4.3)- (4.7) and (4.11) - (4.16)

form a system of equations that can be solved numerically. Numerical results of

this model are used in Section 4.6 to evaluate the performance of the unslotted

IEEE 802.15.4 MAC protocol in the absence of ACK transmissions.


4.5 Analytical Model with ACKs

In this section, the proposed analysis is extended to model the unslotted IEEE

802.15.4 MAC protocol with ACK transmission. To this end, the two DTMCs

presented in Section 4.4 are modied to incorporate ACK transmission and its

consequences. The modied DTMCs are then solved to nd the basic model

probabilities pncca and pci .


Consider a node in a non-beacon-enabled IEEE 802.15.4 network with ACK trans-

mission. The node has to wait for the corresponding ACK frame each time it

transmits data. If the ACK frame was not received, the node retransmits the data

frame up to macMaxFrameRetries attempts. Since receiving ACK is probabilis-

tic in nature, node's current transmission attempt is modelled using a stochastic

process with a random variable x, where x ∈ 1, 2, ...,macMaxFrameRetries +1.

New node states with the relevant transmission attempts are shown in the mod-

ied node state DTMC in Figure 4.5.

The node behaves similar to the model presented in Section 4.4.1 until end of

the data transmission in a given attempt (i.e., up to the expiring of TXx). At

the end of the data transmission, however, the node moves to the ACKx state

instead of moving back to the IDLE state. In the ACKx state the node awaits

the corresponding ACK frame for a period of (aTurnaroundTime + 20Lack) mini-

slots, where aTurnaroundTime and Lack represent the RX-to-TX turnaround

time and ACK frame length, respectively. If the node receives the ACK frame

(with probability q, which will be discussed in Section 4.5.2), it moves to the IDLE

state. Conversely, if no ACK frame is received, the node moves to the rst backo

stage of the next transmission attempt. This process repeats until the node

receives the corresponding ACK frame or tries x transmission attempts, where

x = macMaxFrameRetries + 1. If the node has not received the corresponding


Figure 4.5: DTMC model for node with ACKs. The steady state probability pciis denoted by a .


ACK frame after x transmission attempts, it terminates the frame transmission

and moves to the IDLE state after declaring a transmission failure.

The steady state equations of the node state DTMC (Figure 4.5) are derived

in Appendix A.3. Those equations can be rearranged to obtain

wy−1∑z=1

π(bo1yz) =wy − 1

2φy−1pθ 1 ≤ y ≤ y (4.17)

π(cs1y) = φy−1pθ 1 ≤ y ≤ y (4.18)

π(ack1) = (1− φ)

y∑y=1

π(cs1y) = (1− φy)pθ (4.19)

π(ackx) = (1− φy)(1− q)π(ackx−1) 1 < x ≤ x (4.20)wy−1∑z=1

π(boxyz) =wy − 1

2φy−1(1− q)π(ackx−1) 1 < x ≤ x, 1 ≤ y ≤ y (4.21)

π(csxy) = φy−1(1− q)π(ackx−1) 1 < x ≤ x 1 ≤ y ≤ y (4.22)

π(tax) = π(txx) = π(ackx) 1 ≤ x ≤ x, (4.23)

where θ = π(idle) and φ = (1−pci). By considering the steady state probabilities

and dwell times of each node state, the steady state probability pncca that a node

begins a CCA can be obtained as

pncca =

∑xx=1

∑yy=1 π(csy)

Λack

, (4.24)

where Λack = θ +∑x

x=1[20∑y

y=1

∑wy−1

z=1 π(boxyz) + 8∑y

y=1 π(csy) + 12π(tax) +

20Lπ(txx) + (12 + 20Lack)π(ackx)].

In (3.19), π(ack1) is expressed as a function of θ and φ1. Thus, π(ackx), where

1 < x ≤ x, can be determined in terms of θ, φ and q by recursively using (4.20)

with (4.19). Consequently,∑wy−1

z=1 π(boxyz), π(csxy), π(tax) and π(txx), where

1 ≤ x ≤ x and 1 ≤ y ≤ y, can be expressed using θ, φ and q according to1Note: p, the probability of frame arrival, is a known parameter for a given network.


Figure 4.6: DTMC model for channel with ACKs.

(3.21) (3.23). Thus, for a given network, the steady state probability of the

each node state can be completely determined in terms of θ, φ and q. Therefore,

pncca expressed in (4.24) also becomes a function of θ, pci (via φ) and q. Since θ

can be expressed as a function of pci and q using the normalised condition of the

node state DTMC (shown in Appendix (A.3)), the basic model probability pncca

is completely determined by the probabilities pci and q for a given network.

Next, the common channel seen by all sensor nodes is analysed to nd the

probabilities pci and q.


The behaviour of the channel in a non-beaocn-enabled network with ACK trans-

mission can be modelled using a DTMC as shown in Figure 4.6. The IDLEc1,

IDLEc2, CW1i (1 ≤ i ≤ 12), SUCC and FAIL states represent the same channel

activities that they have represented in Figure 4.4. The CW2j (1 ≤ j ≤ 4) states

denote the second collision windows of the channel, while the channel idleness in

between a success data transmission and the corresponding ACK frame is rep-

resented by IDLEc3 state, which is 12 mini-slots in duration. Finally, the ACK1


and ACK2 states denote the rst 8 mini-slot and the last (20Lack − 12) mini-slot

durations of the ACK frame transmission where a collision cannot occur.

The channel transitions from the IDLEc1 state up to the SUCC state are sim-

ilar to those of the model presented in Section 4.4.2 with the same transition

probabilities α, β and γ. However, at the end of a successful data transmission

the channel moves to the IDLEc3 state instead of the IDLEc1 state compared with

the DTMC in Figure 4.4. When the common receiver starts the ACK transmis-

sion, the channel moves from IDLEc3 to ACK1 state with probability 1. If none

of the remaining nodes1 begin transmissions at the end of the ACK1 state, where

the second collision window starts, the channel enters CW21 state. Conversely, if

at least one remaining node begins a data transmission at the end of the ACK1

state, the channel falls back to the FAIL state.

Following similar arguments, the channel moves through the remaining mini-

slots of the second contention window. If there were no collisions during the entire

second collision window, the channel enters to ACK2 state and completes the ACK

frame transmission successfully. After the ACK transmission is completed, the

channel becomes idle again. The respective dwell times of each channel state are

shown in Figure 4.6.

The steady state equations and the normailsed condition of the channel state

DTMC (Figure 4.6) can be given by

π(idlec1) = π(ack2) + π(fail) (4.25)

π(idlec2) = α1π(idlec2) + π(idlec1)

π(cw11) = βπ(idlec2)

π(cw1i) = γπ(cw1i−1) where 2 ≤ i ≤ 12

π(succ) = π(idlec3) = π(ack1) = γπ(cw112)

π(cw21) = γπ(ack1)

1The channel being in second collision window implies that a node is waiting for an ACK.


π(cw2j) = γπ(cw2j−1) where 2 ≤ j ≤ 4

π(ack2) = γπ(cw24)

π(fail) = (1− α− β)π(idlec2) + (1− γ)

[π(ack1) +

12∑i=1

π(cw1i) +4∑j=1

π(cw2j)

](4.26)

3∑u=1

π(idlecu) +2∑v=1

π(ackv) +12∑i=1

π(cw1i) +4∑j=1

π(cw2j) + π(succ) + π(fail) = 1.

(4.27)

By solving (4.25) (4.27), π(idlec2) can be obtained as

π(idlec2) =1

2(1− α) + 1 + β(1+2γ12−2γ13−γ171−γ )

. (4.28)

Moreover, the steady state probabilities of the channel being in all other states

can be given in terms of π(idle2) by rearranging (4.25) - (4.26) as follows:

π(idlec1) = (1− α)π(idlec2) (4.29)

π(cw1i) = βγi−1π(idlec2) where 1 ≤ i ≤ 12 (4.30)

π(succ) = π(idlec3) = π(ack1) = βγ12π(idlec2) (4.31)

π(cw2j) = βγ(12+j)π(idlec2) where 1 ≤ j ≤ 4 (4.32)

π(ack2) = βγ17π(idlec2) (4.33)

π(fail) = (1− α− βγ17)π(idlec2). (4.34)

Using the steady state probabilities and dwell times of the channel state

DTMC in Figure 4.6, the steady state probability pci that the channel is idle for 8

consecutive mini-slots can be given as

pci =1219

(19)π(idlec1) + π(idlec2) + 58(8)π(idlec3)

Γack(4.35)


where, Γack = 19π(idlec1) +π(idlec2) +∑12

i=1 π(cw1i) + (20L− 12)π(succ) + (20L+

6)π(fail)+12π(idlec3)+8π(ack1)+∑4

j=1 π(cw2j)+(20Lack−12)π(ack2). Substi-

tuting (4.29) (4.34) in (4.35), the basic model probability pci can be simplied

to

pci =12(1− α) + 1 + 5βγ12

5(1− α)(5 + 4L) + β[(20L+ 7)γ12 + 1−γ17

1−γ − (20(L− Lack) + 18)γ17]

+ 1.

(4.36)

In (4.36), the probability pci has been expressed as a function of pncca through α,

β and γ (Note: α = (1− pncca)N , β = Npncca(1− pncca)N−1 and γ = (1− pncca)N−1).

To determine the probability q in terms of pncca, consider a node that has just

completed a data transmission. The node will successfully receive the correspond-

ing ACK frame if and only if the following two events occur:

1. None of the remaining nodes begin a data transmission during the st col-

lision window, which happens with the probability γ12,

2. None of the remaining nodes begin a data transmission during the second

collision window, which happens with the probability γ4.

Since these two events are apart more than a duration of a data frame from each

other, they can be considered as independent. Therefore, q = γ12γ4 = γ16, which

is completely determined by pncca for a given network.

Finally, the expression for q, equations (4.17) - (4.24) and (4.28) - (4.36) form

a system of equations that can be solved numerically. Numerical solution of this

model is used to evaluate the performance of the unslotted protocol next.

4.6 Performance Evaluation

In this section, the performance of the non-beacon-enabled IEEE 802.15.4 MAC

protocol is derived using the proposed analytical models. In particular, following

performance metrics are obtained for networks with/witout ACK transmission:


the aggregate network throughput, average power consumption per node, frame

discard ratio, and frame delivery ratio.

4.6.1 Aggregate Network Throughput

The aggregate network throughput S of networks without ACKs is dened as the

fraction of time S the channel spends in successful transmission. The fraction of

time the channel spends in successful transmission is comprised of the time the

channel dwells in the SUCC state and the time it resides in each CWi (where

1 ≤ i ≤ 12) state for successful transmissions. Since a successful transmission

occurs if and only if the common channel dwells in SUCC state, the steady

state probability that the channel moves into each CWi state for a successful

transmission is equal to the steady state probability that the channel moves into

SUCC state. Therefore, the aggregate throughput S of the networks without

ACKs can be obtained as

S = S (4.37)

=(20L− 12)π(succ) +

∑12i=1 π(succ)

Γ

=20Lβγ12

5(1− α)(5 + 4L) + β(1−γ12

1−γ − 18γ12) + 1. (4.38)

by considering the steady state transition probabilities and dwell times of each

channel state in Figure 4.4.

In networks with ACKs, S is comprised of the fractions of time the channel

spent in both the original and the duplicate data frame transmissions. Duplicate

frames are transmitted due to the corrupt ACK receptions caused by the collisions

in the second collision window. Since the original data frames related to each

duplicate frames have been already received, all duplicate frames are rejected by

the receiving node's MAC layer. Therefore, they do not further contribute to the

aggregate network throughput. Thus, the aggregate network throughput Sack of


the networks with ACKs is dened as the fraction of time the channel spends in

data transmission followed by successful ACKs. Sack can be derived as

Sack =γ4((20L− 12)π(succ) +

∑12i=1 π(succ))

Γack(4.39)

by considering the steady state transition probabilities and the dwell times of

each channel state in Figure 4.6. In (4.39), γ4 represents the conditional proba-

bility of transmitting the ACK frame successfully given that the corresponding

data transmission was successful. Using the steady state probabilities and the

expression for Γack presented in Section 4.5.2, Sack can be simplied to

Sack =20Lβγ16

5(1− α)(5 + 4L) + β[(20L+ 7)γ12 + 1−γ17

1−γ − (20(L− Lack) + 18)γ17]

+ 1.

(4.40)

In both systems, the aggregate network throughput represents a normalised

value and is unitless.

4.6.2 Average Power Consumption

The average power consumption of a node is determined by considering the char-

acteristics and specications of the Chipcon CC2420 802.15.4 RF transceiver

shown in Figure 3.6. Table 4.2 links the node activities in each analytical model

to the corresponding states of the CC2420 transceiver.

Table 4.2: States of the CC2420 transceiver: Non-beacon-enabled mode.

State Description Related statesWithout ACKs With ACKs

Sleep Idling IDLE IDLEIdle Backing o BOyz BOxyz

Receive Carrier Sensing CSy CSxyReceiving ACK ACK

Transmit Transmitting TX TXx


Based on the state transition assumptions made in Chapter 3 Section 3.6.2,

the average power consumption of a node for networks without ACKs Yav and

with ACKs Yav−ack can be expressed as

Yav = (pni − pnsi)YSleep + (pnbo − pnir + pnsi)YIdle (4.41)

+(pncs − pnrt + pnir)YRx + (pntx + pnrt)YTx

Yav−ack = (pni − pnsi)YSleep + (pnbo − pnir + pnsi)YIdle (4.42)

+(pncs + pnack − pnrt + pnir)YRx + (pntx + pnrt)YTx.

In (4.42) and (4.43), YSleep, YIdle, YRx and YTx are the power expenditures cor-

responding to the transceiver's Sleep, Idle, Receive and Transmit states. The

parameter pnrt denotes the fraction of the time spent in transceiver's Receive to

Transmit transition. The other parameters pnsi, pnir, p

ni , p

nbo, p

ncs, p

nack and p

ntx have

the same meanings of their counterparts in Section 3.6.2, and their expressions

are given in Appendix B.2.

4.6.3 Data Transmission Reliability

In this section, the reliability of data transmission is evaluated using the perfor-

mance metrics: frame discard ratio ρ and frame delivery ratio η. The denitions

of these two performance metrics can be found in Chapter 3 Section 3.6.3.

Frame discard ratio ρ: In networks without ACKs, data frames are dis-

carded only due to y consecutive channel access failures at the MAC layer of

transmitting nodes. Therefore, the probability of frame discarding of a node in a

given mini-slot Pdiscard−nonBcn−noACK in networks without ACKs can be computed

as

Pdiscard−nonBcn−noACK = (1− pci)× 8π(csy)/Λ. (4.43)

On the other hand, the data frames are discarded either due to y consecutive

channel access failures or to (x−1) consecutive retransmission failures in networks

with ACKs. In such networks, the probability of frame discarding due to channel


access failures Pdiscard−nonBcnACK−CCA and the probability of frame discarding due

to retransmission failures Pdiscard−nonBcnACK−TX of a node in a given mini-slot can

be derived as

Pdiscard−nonBcnACK−CCA = (1− pci)× 8x∑x=1

π(csxy)/Λack (4.44)

Pdiscard−nonBcnACK−TX = (1− q)(Lack + 12)π(ackx)/Λack. (4.45)

Then, the `total probability of frame discarding' Pdiscard−nonBcn−ACK can be given

as Pdiscard−nonBcn−ACK = Pdiscard−nonBcnACK−CCA + Pdiscard−nonBcnACK−TX .

Considering a mini-slot period, ρ for both types of networks can be expressed

as

ρ =Pdiscard−nonBcn

(S/20NL) + Pdiscard−nonBcn(4.46)

where S, N and L represent the aggregate network throughput, number of nodes

in the network and frame length, respectively. Pdiscard−nonBcn should be substi-

tuted by either Pdiscard−nonBcn−ACK or Pdiscard−nonBcn−noACK depending on the

presence or absence of ACKs in the network considered.

Frame delivery ratio η: This can only be computed using analytical models

that include a retransmission mechanism as described in Section 3.6.3. Therefore,

η is obtained only for networks with ACKs, and it is given as

η =S/20NL

(S/20NL) + Pdiscard−nonBcnACK−TX(4.47)

where S, N and L have their respective meanings.


This section presents the numerical results of the proposed analytical models

and investigates the performance of the unslotted CSMA/CA mechanism in non-

beacon-enabled IEEE 802.15.4 WSNs. First, the analytical results of the basic


model probabilities are compared with simulations to validate the proposed mod-

els. Next, the performance of the unslotted IEEE 802.15.4 protocol is studied

using analytical and simulation results. Finally, the generality of the proposed

analyses is exploited to present the eects of the network parameters (i.e., number

of nodes and frame length) and MAC-layer parameters (i.e., macMaxFrameRe-

tries, macMaxCSMABackos, maxMaxBE, and macMinBE ) on the performance

of the unslotted IEEE 802.15.4 MAC protocol.

4.7.1 Validation of Analysis

The validation process presented in this section is two-fold. First, the numerical

results are substantiated by simulations performed using an improved version1 of

the ns-2 simulator. Then, the analytical results of the unslotted IEEE 802.15.4

protocol are compared and contrasted with those of the slotted IEEE 802.15.4

protocol obtained from the analytical models presented in Chapter 3.

Two identical non-beacon-enabled star topology networks were considered for

the validation. Both networks consisted of 10 sensor nodes, each generating

frames of length 10 backo slots based on a Poisson arrival rate of λ frames per

frame duration. Out of these two networks, one operated without ACKs, while

the other deployed ACKs and frame retransmission. The MAC-layer parameters

of both networks were assumed to have their default values as specied in the

standard [61]. For all simulations, the IEEE 2.4-GHz PHY layer and a two-ray

ground propagation model were used. A simulation trial ran until each node

completes 20, 000 frame transmissions. Each simulation data point was averaged

over 10 simulation trials using dierent random seeds. Approximations used for

the analytical models were not considered for simulations.

1Since the current ns-2 simulator (i.e., ns-2.34) detects the channel idleness incorrectly innon-beacon-enabled networks, a modication to the existing CCA implementation is proposedas shown in Appendix C.1.


(a) pci and q (b) pncca

Figure 4.7: Behaviour of basic model probabilities (N = 10 and L = 10 backos).

The behaviour of the basic model probabilities pci , pncca, and q against the frame

arrival rate λ is depicted in Figure 4.7 for both network scenarios (i.e., with and

without ACKs). Numerical results of pci and pncca closely match with simulations

for the entire range of λ; however, the analytical results of q marginally deviate

from simulations at higher frame arrivals. This discrepancy can be attributed

to various model assumptions and approximations listed in Section 4.3.1. With

increasing λ in both networks, the steady state probability of channel idleness

pci decreases, while the steady state probability of starting a CCA pncca escalates

rapidly as shown in Figure 4.7. This is because more frame arrivals lead to

more transmission attempts, which in turn increase the channel occupancy. For

a given frame arrival rate, the probability pci of the network with ACKs is less

than that of the network without ACKs due to the extra channel occupancy

caused by ACKs and retransmitted frames. In contrast, the probability pncca of

the network with ACKs is higher than that of the network without ACKs as

the retransmission mechanism generates additional frame transmission attempts.

The probability q (i.e., the probability of receiving the corresponding ACK after

a data transmission) decreases with increasing λ, since more frame arrivals cause

more collisions in transmissions. The decreasing q implies more retransmissions,


and hence more frames may be discarded due to retransmission failures at higher

frame arrival rates.

Performance of both networks, in terms of the aggregate network through-

put, power consumption per node, frame discard ratio, and frame delivery ratio,

are compared and contrasted in Figures 4.8 and 4.9. Close agreement between

simulation and analytical results demonstrates the validity of the proposed anal-

ysis. Results obtained from the analyses in [126] (for the slotted IEEE 802.15.4

protocol without ACKs) and Chapter 3 (for the slotted IEEE 802.15.4 protocol

with ACKs) are also included in Figures 4.8 and 4.9 for comparison. Hence, four

equivalent IEEE 802.15.4 networks (in which N = 10 and L = 10) with dierent

MAC mechanisms:

• unslotted protocol without ACKs,

• unslotted protocol with ACKs,

• slotted protocol (BO = SO = 3) without ACKs and

• slotted protocol (BO = SO = 3) with ACKs

are considered for the following discussion.

The aggregate network throughput S of all four networks remain almost the

same at low frame arrival rates; however, with increasing λ they dier signicantly

from each other as shown in Figure 4.8(a). This can be explained as follows. The

networks with ACKs exhibit less throughput than their counterparts without

ACKs due to the extra channel occupancy caused by ACK transmissions and

frame retransmissions. In the networks without ACKs, the unslotted protocol

yields marginally better throughput than the slotted protocol at high frame ar-

rival rates. As indicated in [126], this may be caused by the additional CCA (i.e.,

the second CCA) duration of the slotted protocol, which creates an unnecessary

extra waiting time before each transmission in networks without ACKs.


(a) S (b) Yav

Figure 4.8: Performance of IEEE 802.15.4 based networks (N = 10 and L = 10):(a) Aggregate network throughput S (b) Average power consumption per nodeYav.

In networks with ACKs, the slotted protocol shows signicantly better through-

put than the unslotted protocol at high λ values. This can be attributed to the

less number of frame collisions experienced by the slotted protocol compared with

that by the unslotted protocol (Figure 4.101). In the network with the slotted

protocol a data frame collides only with another data frame. In contrast, a data

frame may collide either with another data frame (i.e., collision at the rst colli-

sion window) or with an ACK frame (i.e., collision at the second collision window)

in the network with the unslotted protocol. In such networks, the collisions at

the second collision window increase the number of transmission attempts, which

in turn increase the number of collisions during the rst collision window as il-

lustrated in Figure 4.10. These additional collisions lead to more retransmissions

and ACK transmissions resulting a signicant drop in throughput performance.

The average power consumption of a node Yav of all four networks increases

with the frame arrival rate as shown in Figure 4.8(b). The networks with ACKs

1Figure 4.10 depicts the number of frame collisions experienced by a node per second inACK-enable networks with slotted and unslotted protocols. Results are based on ns-2 simula-tions.


(a) ρ

(b) η

Figure 4.9: Performance of IEEE 802.15.4 based networks (N = 10 and L = 10):(a) Frame discard ratio ρ (b) Frame delivery ratio η.


Figure 4.10: Number of collisions in slotted and unslotted protocols.

show a higher power consumption (in both slotted and unslotted scenarios) than

the networks without ACKs due to the excess power used for frame retransmission

and ACK transmission. The networks with slotted protocol (in both with ACKs

and without ACKs) consume extra amount of power on the beacon reception and

additional CCA procedure, and hence they exhibit a higher power consumption

than the networks with the unslotted protocol.

In all four networks, the frame discard ratio ρ follows a similar trend. While

ρ is insignicant at low frame arrival rates, it increases rapidly with λ as shown

in Figure 4.9(a)1. The frame discard ratio and its composition is almost equal

for both slotted and unslotted protocols when ACK frames are absent. However,

in the presence of ACK frames, more data frames are discarded by the unslot-

ted protocol due to its increased level of frame collisions shown in Figure 4.10.

Furthermore, ρ of the networks with ACKs is dominated by the channel access

failures, in contrast to the networks without ACKs where a signicant number of

frames are dropped due to collisions.

In networks with ACKs, the frame retransmission mechanism gives near ideal1In networks without ACKs, ρ of the slotted protocol (Figure 4.9(a)), and η of both the

slotted and unslotted protocols (Figure 4.9(b)) were obtained only from simulations as theseperformance metrics have not been derived in the respective analytical models.


performance in frame delivery for both slotted and unslotted protocols. How-

ever, as shown in Figure 4.9(b), η of the network with the unslotted protocol is

marginally less than that of the network with the slotted protocol. It appears

that the extra amount of frame collisions in the unslotted protocol leads to an

additional amount of frames to be discarded due to transmission failures. On

the other hand, the networks without ACKs, which have approximately similar ρ

values with their counterparts with ACKs, perform poorly in frame delivery due

to the absence of a frame retransmission mechanism. Note that dierent y-scales

are used for the dierent graphs in Figure 4.9(b) for clarity.

4.7.2 Impact of Network and MAC-layer Parameters

This section presents the impact of network and MAC-layer parameters on the

performance of the unslotted IEEE 802.15.4 protocol. To this end, the same

system model described in Section 4.3 is used with dierent values of network

and MAC-layer parameters. Figure 4.11 and 4.12 illustrate the eects of the

frame length (for three dierent lengths: 4, 8, and 12 backo slots) and number

of nodes (for three dierent N values: 5, 10, and 20) on the performance of non-

beacon-enabled IEEE 802.15.4 networks in the absence of ACK transmission.

The eects of the MAC-layer parameters:

• macMaxFrameRetries (for three dierent values: 1,3 and 5),

• macMaxCSMABackos (for three dierent values: 1,3 and 5) and

• the length of the backo window (for three dierent combinations ofmacMinBE

and macMaxBE values : [2,3],[3,5] and [4,8])

on the protocol's performance are depicted in Figures 4.13, 4.14 and 4.15, re-

spectively. For this investigation, a non-beacon-enabled IEEE 802.15.4 network

(where N = 10 and L = 10 backos) is considered with ACK transmission. The

results are given for ve dierent values of λ to represent the entire range of


(a) S for dierent L. (b) Yav for dierent L.

(c) ρ for dierent L.

Figure 4.11: Eects of frame length L on the performance of non-beacon-enabledIEEE 802.15.4 networks without ACKs (N = 10).

frame arrival rates. According to the results in Figures 4.11 - 4.15, it appears

that the impact of network and MAC-layer parameters on the performance of the

unslotted protocol shows a similar trend to that of the slotted protocol presented

in Chapter 3. Therefore, the same discussion on the results in Sections 3.7.2 and

3.7.3 (in Chapter 3) is valid with the results presented in this section.

Research outcomes of this chapter have been either published or under review

in [173][174].


(a) S for dierent N .

(b) Yav for dierent N . (c) ρ for dierent N .

Figure 4.12: Eects of number of nodes N on the performance of non-beacon-enabled IEEE 802.15.4 networks without ACKs (L = 10 backo slots).


(a) S. (b) Yav.

(c) ρ. (d) η.

Figure 4.13: Eects of macMaxFrameRetries on the performance of non-beacon-enabled IEEE 802.15.4 networks with ACKs.


(a) S.

(b) Yav.

(c) ρ.

Figure 4.14: Eects ofmacMaxCSMABackos on the perfor-mance of non-beacon-enabled IEEE802.15.4 networks with ACKs.

(a) S.

(b) Yav.

(c) ρ.

Figure 4.15: Eects of the backowindow length on the performance ofnon-beacon-enabled IEEE 802.15.4 net-works with ACKs.


4.8 Conclusion

A Markov chain based analysis is presented to model the unslotted CSMA/CA

protocol in non-beacon enabled IEEE 802.15.4 WSNs. In the absence of ACKs,

the behaviour of the unslotted IEEE 802.15.4 protocol is modelled by approxi-

mating the protocol's continuous time evolution to a discrete time evolution with

a proper time unit. This study is then extended to analyse the network with

ACKs, taking the characteristics of ACK transmission and frame retransmissions

into account. The proposed analytical models can be used to derive the per-

formance metrics of the protocol including the aggregate network throughput,

average power consumption per node and frame discard ratio. Numerical results

obtained from the proposed models are validated using ns-2 simulations. Then,

these results are used to compare and contrast the performance of the unslotted

protocol with its slotted counterpart. In general, the unslotted protocol exhibits

better performance than the slotted protocol in networks without ACKs; however,

when ACK frames are utilised, the performance of the unslotted protocol becomes

signicantly worse than the slotted version mainly due to collisions between data

frames and ACK frames in the protocol.

Chapter 5

Throughput Analysis of IEEE

802.15.4 MAC Protocol in the

Presence of Hidden Nodes

5.1 Introduction

The performance of the IEEE 802.15.4 MAC protocol has been analysed in the

literature using various mathematical models by considering carrier sense multiple

access with collision avoidance (CSMA/CA) as the basic MAC mechanism. Most

of the existing analyses [86][87][124][126] have been performed for single-hop star-

topology networks assuming that all sensor nodes lie within the carrier sensing

range of each other. However, this assumption is not always valid for practical

WSN deployments due to the limited transmission range of the sensor nodes and

the presence of physical obstacles. This gives rise to the hidden node problem,

which is a common issue in many wireless networks that follow the CSMA/CA

scheme [175].

The hidden node problem in a single-hop, star-topology network is illustrated

in Figure 5.1. In this topology, nodes X and Y are in the communication range

5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 112

Figure 5.1: Hidden node problem in a single-hop star-topology network.

of a common receiver R. Furthermore, X lies beyond the carrier sensing range of

Y and vice versa. When X starts a transmission it may collide with an ongoing

transmission from Y to R. Hence, X is considered as a hidden node to Y and

vice versa. The presence of hidden nodes degrades the performance of wireless

networks due to an excessive amount of collided transmissions. Busy tone mecha-

nisms, ready-to-send/clear-to-send (RTS/CTS) mechanisms, carrier sense tuning

and node grouping have been proposed to overcome the hidden node problem in

wireless networks [45][175][176]. However, the IEEE 802.15.4 standard neither

supports any of these techniques nor provides any other mechanism to prevent

collisions due to hidden nodes.

Although the hidden node problem is a well known phenomenon in wireless

networks, only a few studies have been undertaken to evaluate its impact on

the performance of IEEE 802.15.4 based networks. Most of the previous studies

([177], references there in) have focused on hidden node mitigation techniques

instead. Of those studies that analyse the eect of hidden nodes, all most all are

based on simulations [178]-[180]. Nevertheless, Goyal et al. [171] have recently

proposed a stochastic model to analyse the non-beacon-enabled mode of the IEEE

802.15.4 MAC protocol considering the impact of hidden node collisions on the

packet loss probability and the packet transmission latency. This model divides

the nodes in a given network into two distinct categories: regular nodes and


hidden nodes; and it can be used to evaluate the performance of regular nodes

but not the entire network. Taking a dierent approach based on Markov chains,

Marco et al. [168] have evaluated the data transmission reliability of the unslotted

IEEE 802.15.4 MAC protocol with hidden nodes. Thus far, the models presented

in [171] and [168] are the only mathematical analyses proposed for the IEEE

802.15.4 MAC protocol in the presence of hidden nodes; however, neither of these

models has considered the throughput performance of the protocol.

In contrast, the throughput performance of the IEEE 802.11 protocol [45] (i.e.,

the counterpart of the IEEE 802.15.4 protocol used in wireless local area networks

(WLANs)) has been studied in the literature considering the presence of hidden

nodes [181]-[187]. Wu et al. [181] have proposed a model for the distributed

coordination function (DCF) of the IEEE 802.11 protocol with hidden nodes

to analyse the throughput under the saturation condition. Similar saturation-

throughput analyses of the IEEE 802.11 protocol with hidden nodes can be found

in [182] and [183]. On the contrary, Yang et al. [184] proposed a non-saturated

goodput, a derivative of throughput, analysis for a single cell WLAN system. In

[186], Ekici and Yongacoglu have numerically evaluated the eect of hidden nodes

on throughput performance of symmetric networks where each node sees the same

number of hidden nodes and contending nodes. Moreover, Ray et al. [187] have

presented a queuing theoretic analysis for a network with linear topology and

derived an exact expression for the maximum throughput while considering the

presence of hidden nodes. However, none of aforementioned analyses can be

used to evaluate IEEE 802.15.4 based networks due to the dierences of the

two protocols in many aspects including backo mechanisms and carrier sensing

[45][61].

This chapter analyses the throughput performance of IEEE 802.15.4 based

star-topology networks in the presence of hidden nodes. The proposed analysis

models the wireless channel around the common receiver using a discrete-time

Markov chain (DTMC). Even though this analysis is developed for networks with


special node arrangements, it can be used to approximate the throughput of

generic star-topology networks as shown later in the chapter. In addition, the

proposed analysis can serve as a simple mathematical tool to investigate the

eects of various network parameters including the network size, frame length

and frame arrival rate on the throughput of the network.

5.2 System Model

Consider an IEEE 802.15.4 based star-topology network with N nodes scattered

around a common base station. The nodes directly transmit monitoring data

to the base station, which operates as a passive receiver enabling only uplink

data transmission in the network. Star-topology networks with only uplink data

transmission have been deployed in many current WSN applications including

cultural heritage monitoring [188], smart homes [189] and industrial automation

[190]. The network operates in the beacon-enabled mode of the IEEE 802.15.4

MAC protocol to achieve network-wide synchronisation.

The proposed analytical model is based on several assumptions on the network

of interest. First, it is assumed that the entire beacon interval is active and lled

with the CAP. The transmission of each data frame lasts for xed-L backo

slots, and data frames are assumed to arrive at the nodes according to a Poisson

distribution with an arrival rate of λ frames per frame duration. Accordingly,

the frame arrival probability per backo slot can be derived as p = λ/L. Next,

ideal channel conditions where there are no transmission errors introduced by the

channel are assumed; therefore, frames are dropped only due to their collisions.

Buering at the nodes is not considered, and the capture eect is neglected for

frame transmissions, i.e., all collided frames are assumed to be dropped regardless

of their received signal strengths at the base station. Moreover, node grouping

is assumed; thus, the entire network can be divided into several non-overlapping

groups as specied next.


Figure 5.2: Example network [4,6,8] with node grouping (K = 3).

5.2.1 Node Grouping

Node grouping divides an entire network into K groups such that∑K

j=1 nj = N

where nj denotes the number of nodes in Group j. Such a network is denoted by

the notation [n1, n2, . . . , nK ], and it has the following two characteristics:

• Nodes within a group share the same carrier sensing range. i.e., all sensor

nodes within a group can hear each others' transmissions,

• Nodes in dierent groups have dierent carrier sensing ranges. i.e., nodes

in dierent groups cannot hear each others' transmissions.

Accordingly, the data transmissions originating at dierent groups may collide

at the base station creating the hidden node problem in the network. Figure 5.2

illustrates the concept of node grouping in which a network with N = 18 nodes

is divided into K = 3 groups, where n1 = 4, n2 = 6 and n3 = 8. This network is

represented by the notation [4, 6, 8].

The grouping of sensor nodes is a key technique used to mitigate the hidden

node problem [176]. In [191], a node grouping network set-up has been deployed

in an experimental test bed to emulate a WSN with hidden nodes. Therefore, the


node grouping is not an unrealistic scenario in WSNs, specially in the presence

of hidden nodes. Thus, it is considered as a key assumption of the proposed

network analysis. However, later in this chapter it has been shown that the

proposed analysis can still be used to approximate the throughput performance

of networks that may not necessarily satisfy the node grouping condition.

5.3 Network Analysis

This section analyses the network of interest to determine its throughput per-

formance. Since all data transmissions are bound towards the base station, the

throughput of the network can be determined by analysing the channel seen by

the base station, i.e., the overlapping area of all carrier sensing ranges around the

base station as depicted in Figure 5.2). This channel is referred to as the common

channel throughout this chapter.

To model the common channel, two basic probabilities:

1. The probability of none of the nodes in the Group j begin transmission

(αj),

2. The probability of exactly one node in the Group j begins transmission

(βj).

are dened. These two probabilities are determined by analysing individual

groups as described in the next section.

5.3.1 Analysis of Individual Groups

The node grouping splits the single-hop star-topology network intoK non-overlapping

groups where each group can be considered as a mini star-topology network.

Thus, any existing analysis that derives the probabilities αj and βj for an IEEE

802.15.4 based star-topology network can be devised to analyse the individual


groups. For this study, the analysis proposed by Ramachandran et al. [126] is

chosen to model the individual groups by considering its analytical simplicity and

accuracy.

Ramachandran et al. [126] have modelled the behaviour of a generic node and

the channel of a single-hop star-topology network using two dierent discrete-

time Markov chains (DTMCs). They have approximated the probability that the

channel is sensed idle in a given backo slot by the steady-state probability of

channel idleness pci . Similarly, the probability that a node begins transmission

in a given backo slot is approximated by the steady-state probability that a

node transmits pnt . These two probabilities have been computed by solving a

consistent system of equations including the steady state equations of the two

DTMCs. Based on these two probabilities, the probability that any node begins

transmission given that the channel has been idle for two consecutive backo slots

pnt|ii is derived as [126]

pnt|ii =Lpnt

Lpci − 1 + pci, (5.1)

where L is the data frame length.

In the context of the j-th individual group, let pnt|ii(j) represent the probability

that any node in Group j begins transmission given that the channel seen by

Group j has been idle for two consecutive backo slots. Based on pnt|ii(j), the

basic probabilities αj and βj can be derived as

αj = [1− pnt|ii(j)]nj

βj = njpnt|ii(j)[1− jpnt|ii(j)]nj−1,

(5.2)

where nj is the number of nodes in Group j. The rst half of Table 5.1 lists

αj and βj values obtained for the groups shown in Figure 5.2 considering three

dierent frame arrival rates λ: 0.006, 0.06 and 0.6.

It is worthwhile to mention that the analysis of the common channel, which

will be presented next, is valid not only with the analysis in [126] but also with


Table 5.1: Probabilities αj, βj, A, B, C and E for the network shown in Figure 5.2when L = 10.

Group1 Group2 Group3n1 = 4 n2 = 6 n3 = 8 A B C E

λ α1 α2 α3

β1 β2 β3

0.006 0.99756 0.99630 0.99499 0.98890 0.01105 0.99288 0.007100.00244 0.00370 0.00499

0.06 0.97231 0.95298 0.92943 0.86121 0.12922 0.91126 0.084890.02740 0.04608 0.06833

0.6 0.81757 0.72560 0.64279 0.38132 0.37767 0.53951 0.339510.16889 0.23908 0.29207

any of the analyses that derive αj and βj for an IEEE 802.15.4 based star-topology

network.

5.3.2 Analysis of the Common Channel

This section analyses the common channel seen by the base station. Depending

on the number of current and recently ended data transmissions, the common

channel can operate in three dierent modes: idle, success and failure. It dwells

in the idle mode when there is no data transmission from any of the groups. If

there is only one data transmission and it has not collided with any of the recent

data transmissions, the common channel is in the success mode. Conversely,

it operates in the failure mode if there are more than one simultaneous data

transmissions. Even though there is only one data transmission in the common

channel for a given instant, it can be in the failure mode if the current transmission

has collided with recent data transmissions.

Possible modes of the common channel and their transitions can be mod-

elled using a discrete-time Markov chain as shown in Figure 5.3. The IDLE state

represents the idle mode, while SUCCx and FAILx states (where 1 ≤ x ≤ L) char-

acterise the success and failure modes, respectively. The subscript x in SUCCx

indicates that the common channel should remain in the success mode for the


Figure 5.3: DTMC model for the common channel.

next consecutive x backo slots (including the current backo slot) to complete

the current data transmission successfully. Similarly, the subscript in FAILx in-

dicates that the common channel will remain in the failure mode at least for the

next consecutive x backo slots including the current backo slot. Each state in

the DTMC corresponds to a single backo slot, and the transitions among these

states occur at the boundary of backo slots.

Transition probabilities of the DTMC for the common channel can be derived

as follows. Let the common channel be in the IDLE state. If none of the groups

begin transmission, the channel remains in the IDLE state with probability A,

which is given by

A =K∏j=1

αj. (5.3)

The common channel moves from the IDLE state to SUCCL state if only one

node from the entire network starts data transmission, which happens with the

probability B given by

B =K∑i=1

βi

K∏j=1j 6=i

αj . (5.4)


Figure 5.4: Transition from SUCCL to SUCCL−1 state.

Conversely, if more than one node begin transmissions simultaneously, the tran-

sition from the IDLE state to FAILL state happens with probability 1− (A+B).

Next, consider the transition from SUCCL state to SUCCL−1 state. For this

transition to take place, no other transmission, besides the one already going on,

should start. Suppose the ongoing transmission belongs to a node from Group i.

Then, no other node from Group i would begin a transmission due to the basic

CSMA operation. However, nodes from other groups may begin transmissions as

they cannot sense the ongoing transmission. Thus, if the current transmission

belongs to Group i, the common channel moves to the SUCCL−1 state if and

only if none of the nodes in other groups begin a transmission, which happens

with the probability∏K

j=1j 6=i

αj. Since there are K groups in the network, there

exist such K transition possibilities as illustrated in Fig. 5.4. Therefore, the

SUCCL → SUCCL−1 transition can be mathematically expressed as

π(succL−1) =K∑i=1

π

(Group i is transmitting at

SUCCLstate

) K∏j=1j 6=i

αj (5.5)


where π(state) represents the steady state probability of STATE. This can be

simplied to

π(succL−1) =K∑i=1

ψiπ(succL)K∏j=1j 6=i

αj , (5.6)

where ψi is the conditional probability that the current transmission belongs to

Group i given that the common channel is in SUCCL state. Thus, the transition

probability from SUCCL state to SUCCL−1 state can be given as

C =K∑i=1

ψi

K∏j=1j 6=i

αj . (5.7)

Since the derivation of ψi using the basic probabilities αi and βi is mathematically

intractable, ψi is approximated by the probability that the current transmission

belongs to Group i given that at least one group has successfully begun a trans-

mission, which is equal to βi/∑K

r=1 βr. Therefore, the approximated transition

probability C can be expressed as

C ≈K∑i=1

(βi∑Kr=1 βr

)K∏j=1j 6=i

αj . (5.8)

With probability C, the common channel moves through the other SUCC

states until it reaches SUCC1 state, where it completes a successful transmission.

After SUCC1 state, the common channel may move to one of the three dierent

states : IDLE, SUCCL or FAILL; depending on the number of groups that begins

data transmissions at the end of the current backo slot. According to the IEEE

802.15.4 standard, the nodes in the group that has just completed a successful

data transmission can not start another transmission immediately. They have to

wait at least another two consecutive backo slots. Therefore, when the common

channel sees the end of a successful data transmission, only (K − 1) groups are


eligible to begin a new data transmission at the next backo slot. Thus, the com-

mon channel moves from SUCC1 state to IDLE state with probability D, where

D represents the probability of none of the other groups begin data transmission

given that there was a data transmission at the previous backo slot. Hence,

D = C. On the other hand, if only one node from the entire network begin

transmission, the common channel moves from SUCC1 state to SUCCL state.

This happens with probability E, which represents the probability of exactly one

node begins transmission given that there was a data transmission at the previous

backo slot. Following similar arguments to that of deriving C, the probability

E can be approximated as

E ≈K∑i=1

(βi∑Kr=1 βr

)K∑j=1j 6=i

βj

K∏l=1

l 6=i, l 6=j

αl. (5.9)

If more than one node in the entire network begin transmission after SUCC1

state, the common channel moves to FAILL state with probability 1− (C + E).

Unlike in the success mode where only one group is transmitting, the number

of transmitting groups in the failure mode may vary from one to K. However, for

simplicity, it is assumed that the channel experiences a single collision at a time.

This simplication represents the worst case scenario as it allows more groups

to cause future collisions, and hence guarantees that the common channel does

not leave the failure mode prematurely. Because of this simplication, the same

transition probabilities used with SUCC states are applicable to the FAIL states.

The values of the transition probabilities A, B, C and E for the network shown

in Figure 5.2 are listed in Table 5.1 considering three dierent frame arrival rates.

In terms of the transition probabilities, the steady state equations and the

normalisation condition for the DTMC shown in Figure 5.3 can be derived as


π(idle) = Aπ(idle) + C[π(succ1) + π(fail1)] (5.10)

π(succL) = Bπ(idle) + E[π(succ1) + π(fail1)] (5.11)

π(succx) = CL−xπ(succL); 1 ≤ x < L (5.12)

π(failL) = [1− (A+B)]π(idle) + (1− C)L∑x=2

[π(succx) + π(failx)]

+(1− [C + E])[π(succ1) + π(fail1)] (5.13)

π(failx) = CL−xπ(failL); 1 ≤ x < L (5.14)

π(idle) +L∑x=1

π(succx) +L∑x=1

π(failx) = 1, (5.15)

where π(state) represents the steady state probability of STATE. Equations above

can be rearranged to obtain

π(idle) =

[CL

1− A

][π(succL) + π(failL)] (5.16)

π(succL) =

[BCL + (1− A)ECL−1] π(failL)

1− A−BCL − (1− A)ECL−1 (5.17)

π(idle) +

[1− CL

1− C

][π(succL) + π(failL)] = 1. (5.18)

For a given network, the above set of equations can be numerically solved by

using related A, B, C, and E values1 to determine the steady state probabilities

of the common channel DTMC. These steady state probabilities will be used in

Section 5.5 to compute the aggregate network throughput.1These values can be computed using Equations (5.3), (5.4), (5.8) and (5.9) along with

corresponding αj and βj values obtained from the analysis in [126].


5.4 Simplied Analysis for Networks with Uni-

form Groups

Networks with uniform groups (i.e., networks with groups consisting of an equal

number of nodes) is a special case of the node grouping. The analysis presented

in Section 5.3 can be simplied for this case by substituting nj = n to yield

αj = α and βj = β ∀j where j = 1, 2, ..., K. Consequently, the probabilities A,

B, C and E are reduced to αK , KβαK−1, αK−1, and (K − 1)βαK−2 according to

Equations (5.3), (5.4), (5.8) and (5.9). Therefore, Equations (5.16) (5.18) can

be simplied to

π(idle) =

[αL(K−1)

1− αK

][π(succL) + π(failL)] (5.19)

π(succL) =

[(αK +K − 1)βαL(K−1)

]π(failL)

α(1− αK)− [αK +K − 1]βαL(K−1)(5.20)

π(idle) +

[1− αL(K−1)

1− αK−1

][π(succL) + π(failL)] = 1 (5.21)

in networks with uniform groups. Given the corresponding α and β values, Equa-

tions (5.19) (5.21) can be numerically solved to obtain the steady state proba-

bilities of the common channel DTMC. The computed steady state probabilities

will be used to derive the aggregate network throughput.

5.5 Throughput Analysis

The aggregate network throughput S is dened as the fraction of time the com-

mon channel spends in successful data transmission; thus, it represents a dimen-

sionless normalised value. Successful data transmission occurs if and only if the

common channel dwells in SUCC1 state. Before arriving at SUCC1 state, the


common channel has to progress through all other SUCC states. Therefore, the

normalised aggregate network throughput can be obtained by adding the fractions

of time that the common channel dwells in each SUCC state for successful data

transmission. The fraction of time the common channel spends in each SUCC

state for successful data transmission is the same as that in SUCC1 state. Thus,

S = L

[π(succ1)

π(idle) +∑L

x=1 π(succx) +∑L

x=1 π(failx)

], (5.22)

where L represents the number of SUCC states (note: According to Figure 5.3,

the number of SUCC states is equal to the frame length in backo slots). Due to

the normalisation condition of the DTMC model shown in (5.15) the denominator

of (5.22) becomes one. Consequently, S can be simplied to

S = Lπ(succ1). (5.23)

In other words, the normalised aggregate network throughput is entirely deter-

mined by the frame length and the steady state probability of the SUCC1 state.

Analytical results presented in the next section are based on this nding.


In this section, the proposed analytical model is veried using Monte-Carlo sim-

ulations. Simulations are performed using ns-2 [105] based on the assumptions

in Section 5.2. For all simulations, the 2.4-GHz physical layer and the beacon-

enabled (beacon order BO = 6 ) mode of the IEEE 802.15.4 standard are consid-

ered. IEEE 802.15.4 PHY and MAC layer parameters are assumed to have their

default values as specied in the standard [61]. In addition, the transmission and

carrier sensing ranges of the nodes are tuned to be identical. Unless mentioned

otherwise, it is assumed that data frames arrive at the same rate at all nodes,

and the transmission of each data frame lasts for 10 backo slots. Furthermore,

the nodes of the star-topology networks are deployed carefully in the simulation


Figure 5.5: Normalised aggregate network throughput S of dierent networkswith hidden nodes.

set-up to maintain the properties of node grouping (i.e., all nodes in a group can

hear each other while the nodes in dierent groups are hidden from each other).

A simulation trial runs until each node completes 20, 000 frame transmissions.

Simulation results are averaged over 10 simulation trials using dierent random

seeds for each trial.

5.6.1 Validation of Analytical Results

To validate the proposed analysis, four network scenarios with dierent number

of groups K and dierent number of nodes per group nj (i.e., [8,8] network,

[12,4] network, [6,4,2] network, and [10,8,4,2] network)1 are used. Analytical and

simulated aggregate network throughput S of the four networks, obtained against

dierent frame arrival rates λ, are compared in Figure 5.5. Close agreement

of analytical and simulation results validates the proposed analysis for dierent

networks with varying group and node numbers. This in turn suggests the validity

of the approximations made to derive the transition probabilities C and E.1The notation [n1, n2, ..., nk] indicates that the network has K groups and the group j has

nj nodes.


According to Figure 5.5, the aggregate network throughput S of all four net-

works increases monotonically at low-λ values where collisions due to hidden

nodes are unlikely. When collisions due to hidden nodes increase (i.e., in high-

λ region) the throughput decreases with increasing λ. Since the networks with

larger number of nodes (N =∑K

i=1 ni ) generate more trac, they exhibit higher

throughput and reach the corresponding peak value before other networks in the

low-λ region. On the other hand, they experience a large number of hidden node

collisions and consequently show poor throughput performance in the high-λ re-

gion. Apparently, networks with the same number of nodes N and the same

number of groups K (e.g., [8,8] network and [12,4] network) behave dierently

depending on the distribution of nodes among groups. The [12,4] network, in

which a higher number of nodes shares the same carrier sensing range compared

with the [8,8] network, experiences less hidden node collisions. This explains the

higher throughput achieved by the [12,4] network compared with the [8,8] net-

work for the entire range of λ. It appears that the distribution of nodes among

groups within a network has a signicant impact on the throughput performance

of the network. The impact of dierent network parameters on the aggregate

network throughput of a given network is investigated next.

5.6.2 Impact of Dierent Network Parameters on Through-

put

This section investigates the impact of network parameters including the number

of groups K, number of nodes per group n, and frame length L on the aggregate

network throughput S considering networks with uniform groups (i.e., networks

with equal number of nodes in each group). For comparison, it also presents S

of corresponding networks with no hidden nodes obtained using the analysis in

[126].

The eects of the number of groups K on the aggregate network throughput S


(a) S for dierent number of groups Kwhen n = 4 and L = 10

(b) S for dierent number of nodes n per groupwhen K = 3 and L = 10

(c) S for dierent network setupswhen K × n = 12 and L = 10

(d) S for dierent data frame lengths Lwhen K = 3 and n = 4

Figure 5.6: Normalised aggregate network throughput S for varying networkparameters.

is illustrated in Figure 5.6(a). Since additional groups generate additional trac,

S of a network with more groups is expected to be higher than that of a network

with less groups at low-λ values. On the other hand, adding more groups would

increase the number of hidden nodes in the network causing a large number of

hidden node collisions at higher λ values. This explains the low aggregate network

throughput experienced by networks with more groups in this region.

The aggregate network throughput of networks having dierent number of

nodes per group n is shown in Figure 5.6(b). In this case, the network trac


increases with n as K is xed for all networks considered. Therefore, networks

with higher number of nodes per group have better throughput in low-λ region.

However, when λ increases those network show poor throughput performance

than their counterparts with less number of nodes per group. This is because

increasing the number of nodes in each group will potentially escalate the number

of hidden node collisions at high frame arrival rates.

The network congurations considered above have dierent total number of

nodes N , and hence they generate dierent amounts of trac. Dierence in total

network trac may mask the eects of hidden nodes on S up to some extent.

Therefore, to uncover the actual impact of hidden nodes, S of dierent network

setups that generate the same amount of trac were obtained, and the results

are presented in Figure 5.6(c). As expected, networks with more hidden nodes

exhibit same or less throughput than that of networks with a few hidden nodes

for the entire range of λ. Moreover, Figures 5.6(a) and 5.6(c) clearly illustrate

the impact of the presence of hidden nodes on S by comparing the throughput of

similar networks with and without hidden nodes. As per these gures, networks

with no hidden nodes exhibit a normailsed throughput greater than 0.5 when

they approaches to the saturation (i.e., λ = 1). In contrast, the throughput

of equivalent networks with hidden nodes tend towards zero near the saturation

region. This signicant throughput reduction indicates the excessive amount of

hidden node collisions experienced by networks with hidden nodes in this high-λ

region.

The eects of the data frame length L on the aggregate network throughput

is illustrated in Figure 5.6(d). Since the dierences in frame length aect the

total number of frame arrivals (due to the normalisation of frame arrival rate),

S was obtained against the frame arrival probability p instead of λ. As shown in

Figure 5.6(d), long data frames provide higher network throughput at low frame

arrivals; however, they fail to prevail in high-p region due to increased number

of hidden node collisions caused by their longer duration. Therefore, a careful


Figure 5.7: Normalised aggregate network throughput S for [4,8,12] network whendierent groups generate frames at dierent rates.

adjustment of data frame length may be helpful in achieving maximum possible

aggregate network throughput at a known frame arrival rate in a given WSN with

hidden nodes.

So far in this study, it has been assumed that the frame arrival rate of all

the nodes in the network of interest is similar. However, in some WSN appli-

cations such as human health monitoring [192], frames arrive at dierent nodes

at dierent rates. The proposed analysis can be readily applied to such situ-

ations by embracing dierences in frame arrivals during the calculation of two

basic probabilities αj and βj. To evaluate the aggregate network throughput

with varying frame arrival rates, the [4,8,12] network was considered with three

dierent cases. In Case 1, data frames arrive at an equal rate at the nodes of

all three groups (λ1 = λ2 = λ3). In Case 2, the frame arrival rates of Group 1

(with 4 nodes) and Group 2 (with 8 nodes) are xed to 0.1 and 0.01 frames/frame

duration, respectively. On the other hand, in Case 3 the arrival rates of Group

1 and Group 2 are swapped with each other and are 0.01 and 0.1 frames/frame

duration. For all three cases, the frame arrival rate of Group 3 varies from 0.0002

to 0.8 frames/frame duration.


Analytical and simulation results of aggregate network throughput S obtained

for all three cases are shown in Figure 5.7. The aggregate network throughput

of cases 2 and 3 are signicantly higher compared to that of the Case 1 when

frames arrive at Group 3 at low rates (i.e., λ < 0.02). In this region, most of

the nodes in all three cases generate a fewer number of frames, and consequently

there are less collisions due to hidden nodes. Therefore, the network that has

the highest number of nodes with a higher frame arrival rate (i.e., Case 3) yields

the highest throughput in this region. On the other hand, when λ3 increases,

hidden node collisions escalate due to increased trac in Group 3. Therefore, the

network with the lowest number of nodes with a higher frame arrival rate (i.e.,

Case 2) prevails in the high-λ3 region. The outcomes of this investigation would

be useful for designing IEEE 802.15.4 based WSNs with hidden nodes, in which

dierent nodes generate data at dierent rates (e.g., WSNs deployed in smart

infrastructure monitoring [35], human health monitoring [192]).

5.6.3 Approximating Throughput of Generic Networks

Node grouping, as discussed in Section 5.2.1, may not always be valid for practical

WSN deployments. Therefore, this section investigates the applicability of the

proposed analysis for generic networks that do not necessarily satisfy the node

grouping condition. First, it is shown that the aggregate network throughput

of `networks with relaxed node grouping condition' can be approximated using

the analysis presented in Section 5.3. Then, the aggregate network throughput

of dierent congurations of a given network are shown to be approximately

equal, provided that all network congurations considered have similar average

number of hidden-nodes-per-node. Finally, based on these ndings, a technique is

proposed to approximate the aggregate network throughput of generic networks.

The node grouping condition in a given network can be relaxed by converting

a few of the nodes in each group into intermediate nodes such that they can


(a) Cong. 1 (havg = 8) (b) Cong. 2 (havg = 8.125)

(c) Cong. 3 (havg = 8.5)

Figure 5.8: [8,8] network and its relaxed node grouping congurations.

hear some of the nodes in their neighbouring groups. Figure 5.8(b) illustrates a

possible way of relaxing the node grouping condition of the [8,8] network depicted

in Figure 5.8(a)1. In Figure 5.8(a), all nodes in Group 1 share a single carrier

sensing range and so do the nodes in Group 2. Therefore, no intermediate nodes

exist in Figure 5.8(a). Now, consider the conguration in Figure 5.8(b) where

Node 1 from Group 1 and Node 2 from Group 2 perform as intermediate nodes.

As illustrated, Node 1 can hear2 Nodes 2, 4, 6 and 8 from Group 2, and Node

2 can hear Nodes 1, 3, 5 and 7 belonging to Group 1. The new carrier sensing

ranges of Node 1 and Node 2 now include only one half of the nodes (including

1Nodes in Figure 5.8(a) and 5.8(b) are numbered for the simplicity of explanation.2Symmetric hearing between nodes is assumed, i.e.,

Node A hears Node B ⇐⇒ Node B hears Node ANode A cannot hear Node B⇐⇒ Node B cannot hear Node A.


(a) Cong. 1 (havg = 8) (b) Cong. 2 (havg = 8)

(c) Cong. 3 (havg = 8)

Figure 5.9: [8,8,8] network and its relaxed node grouping congurations.

themselves) in their respective original groups. In other words, Node 1 cannot

hear Nodes 9, 11, 13 and 15 while Node 2 cannot hear Nodes 10, 12, 14 and 16 any

more. Therefore, the presence of intermediate nodes has created dierent carrier

sensing ranges within the groups and consequently divided the entire network

into several sub-groups with overlapping carrier sensing ranges. The nodes in a

given sub-group have the same carrier sensing range, and they can hear not only

the nodes in that particular sub-group but also all the nodes in adjacent sub-

groups. For clarity of illustration, the sub-groups in Figure 5.8(b) are enclosed

by dotted boxes, and each sub-group is linked to its adjacent sub-groups using


Figure 5.10: Normalised aggregate network throughput S of networks with re-laxed node grouping.

arrows. The value h represents the number of hidden nodes related to each sub-

group. For instance, the sub-group of Nodes 9, 11, 13 and 15 has nine hidden

nodes as the nodes in that sub-group can hear only Nodes 3,5 and 7 apart from

themselves. Based on h values of all sub-groups, the average number of hidden-

nodes-per-node havg of the network shown in Figure 5.8(b) can be computed as

havg = [2× (4× 9 + 3× 7) + 2× 8] /16 = 8.125.

Figure 5.8c shows another relaxed node grouping conguration of the [8,8]

network where the number of intermediate nodes equals four. Similarly, Figure 5.9

shows the [8,8,8] network and two of its relaxed node grouping congurations. The

aggregate network throughput of those six network congurations1 are compared

in Figure 5.10. In each network, the congurations with intermediate nodes

have approximately equal throughput to that of the equivalent node grouping

conguration as illustrated in Figure 5.10. This nding suggests that the proposed

analysis can approximate the aggregate network throughput even for networks

with a relaxed node grouping condition.

However, it should be noted that the average number of hidden-nodes-per-1ns-2 simulation results obtained for all congurations are presented along with the analyt-

ical results related to the node grouping congurations (i.e., Conguration 1 of each networks).


(a) Cong. 1 (havg = 8) (b) Cong. 2 (havg = 7.7) (c) Cong. 3 (havg = 5.75)

(d) Cong. 4 (havg = 6) (e) Cong. 5 (havg = 3.5) (f) Cong. 6 (havg = 3.375)

Figure 5.11: Dierent congurations of 16-node-network (N = 16) with dierentaverage number of hidden nodes havg.

node havg of each of the above relaxed node grouping congurations is equal or

approximately-equal to that of the equivalent conguration with node grouping.

Does this imply that two dierent network congurations of a given (in the sense

of N , L and λ) network have equal aggregate network throughput when both

network congurations have similar average number of hidden-nodes-per-node

havg?

To investigate this, dierent congurations of a 16-node-network and a 24-

node-network are set up with dierent havg values. Figures 5.11 and 5.12 show

these congurations, emphasising the possible sub-groups and corresponding havg

values of the 16-node-network and 24-node-network, respectively. For both net-

works, Conguration 1 satises the node grouping condition. Apart from that, all

other congurations are generic WSN deployments as there are no restrictions on

node grouping. Simulation results of the aggregate network throughput obtained


(a) Cong. 1 (havg = 16) (b) Cong. 2 (havg = 15.9) (c) Cong. 3 (havg = 14.2)

(d) Cong. 4 (havg = 13.9) (e) Cong. 5 (havg = 8) (f) Cong. 6 (havg = 7.5)

Figure 5.12: Dierent congurations of 24-node-network (N = 24) with dierentaverage number of hidden nodes havg.

(a) 16-node-network (b) 24-node-network

Figure 5.13: S of dierent network congurations with varying havg: (a) 16-node-network (N = 16) and (b) 24-node-network (N = 24).

for all congurations of the 16-node-network and the 24-node-network are shown

in Figure 5.13(a) and 5.13(b), respectively. As expected, the congurations with


(a) Generic 25-node-network(havg ≈ 15)

(b) Throughput comparison

Figure 5.14: Validation of proposed technique.

similar havg values exhibit almost equal aggregate network throughput in each

network considered.

This result can be exploited to analyse the aggregate network throughput

of a given generic network. First, it is required to nd an èquivalent network

with node grouping' that has the same number of total nodes N and the same

or approximately the same average number of hidden nodes per node havg with

the generic network. Then, S of the generic network can be approximated by

analysing the èquivalent network with node grouping'. This technique can be

explained using the following example. Assume a network with 25 nodes where

havg ≈ 15 as shown in Figure 5.14(a). By applying the analytical model pre-

sented in Section 5.3 to the [12,10,3] network with node grouping, which ful-

ls N = 25 and havg ≈ 15 ([12× 13 + 10× 15 + 3× 22] /25), the aggregate

network throughput of the given network can be found approximately. The

[14,5,5,1] network (with four non-overlapping groups) is another possible èquiva-

lent node-grouping network' to the given generic network. The simulation results

obtained for the network shown in Figure 5.14(a) and the analytical results for

the equivalent networks with node grouping (i.e., the [12,10,3] network and the


[14,5,5,1] network) are compared in Figure 5.14(b). The close agreement between

the analytical results and the simulation results shows the applicability of the

proposed analytical model to evaluate the throughput performance of a generic

star-topology network, which does not meet the node grouping condition.

Outcomes of this chapter have been either published or under review in [193]

and [194].

5.7 Conclusion

An analytical model is proposed to evaluate the performance of the IEEE 802.15.4

MAC protocol in the presence of hidden nodes by grouping the nodes into non-

overlapping carrier sensing ranges. The proposed analysis can be used to derive

the aggregate network throughput. The close agreement between the analytical

results and simulations validate the accuracy of the analysis. The results reveal

that adding more hidden nodes, either by increasing the number of groups or

by increasing the number of nodes in a group, would signicantly decrease the

aggregate network throughput at high frame arrival rates where frames collide

frequently. Furthermore, for a given frame arrival rate, an increased aggregate

network throughput can be achieved by adjusting the frame length appropriately.

Using ns-2 simulations, it is shown that the throughput performance of dierent

congurations that have equal number of average hidden-nodes-per-node of a

given network are approximately equal. Based on these results, a simple technique

is proposed to approximate the throughput of generic star-topology networks with

hidden nodes.

Chapter 6

IEEE 802.15.4 based MAC Protocol

for Hybrid Monitoring WSNs

6.1 Introduction

Remote monitoring and data collection is arguably the main application of the

WSN technology. This wide-ranging application can be broadly classied into

three main categories: realtime monitoring, periodic monitoring, and event de-

tection (ED) [7]. Among them the realtime monitoring is rarely supported by

energy constrained WSNs as it demands a greater amount of energy. Therefore,

most of the existing WSNs are deployed either in periodic monitoring or in event

detection applications. In periodic monitoring, each sensor node monitors a cer-

tain phenomenon of interest according to a predened schedule and transmits

the sensed data periodically towards a sink-node over a long period of time. This

monitoring scenario enables end-users to examine the long-term evolution and

variability of certain monitoring parameters; therefore, it can be duly named as

long-term periodic monitoring (LTPM). On the other hand, in the ED scenario,

each sensor node monitors a certain phenomenon of interest continuously to de-

tect a pre-specied abnormal or rare events. Once an event is detected, sensor

6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 140

nodes alert end users about the precarious situation by transmitting the data

gathered during that event.

With the introduction of multi-sensor WSNs, a new class of applications which

combines the aforementioned basic monitoring scenarios (LTPM and ED) has

emerged recently. For instance, a patient monitoring application may require

to detect sudden falls of patients while periodically monitoring their heartbeats.

Similarly, a SHM system may be deployed to capture both sudden and long-

term variations in structural health [35][195]. A temperature monitoring system

could generate the evolution of the ambient temperature whilst indicating re

alarms [196]. Since these monitoring applications perform both LTPM and ED

simultaneously, they can be simply referred to as hybrid monitoring applications.

Most of the WSNs deployed in the above monitoring scenarios are based on the

IEEE 802.15.4 standard due to its simple, energy ecient data transmission mech-

anism and its availability in many commercial-o-the-shelf (COTS) platforms.

However, similar to many other standard protocols, the IEEE 802.15.4 data trans-

mission mechanism has some inherent drawbacks. For example, it lacks the adapt-

ability for trac variations and has an inecient contention based medium access

scheme, which introduces many frame retransmissions and idle backos. To over-

come these drawbacks, several improvements to the IEEE 802.15.4 standard have

been proposed in the literature under the following key areas: modications to

the CSMA/CA algorithm [138][197]-[200], adjustments to the superframe struc-

ture [201]-[204], and amendments to the guaranteed time slot (GTS) allocation

scheme [205]-[207]. Taking a generic approach, all these modications improve

the performance of the IEEE 802.15.4 protocol without explicitly focusing on the

underlying monitoring application.

In contrast, some of the other studies have suggested dierent amendments to

the IEEE 802.15.4 protocol to tailor it for specic monitoring applications. Krish-

namurthy and Sazonov [36] have presented a TDMA scheduler that improves the

data transmission reliability and energy eciency in IEEE 802.15.4 based WSNs


deployed in periodic SHM applications. To meet the stringent data transmis-

sion requirements in industrial applications, Chen et al. [37] have implemented

a new TDMA based superframe structure on top of the standard protocol. Fol-

lowing a similar approach, a new set of MAC superframes has been introduced to

the standard protocol in [208] for the applications with time-critical communica-

tions. Further improvements to the IEEE 802.15.4 protocol in emergency-event-

notifying WSNs have been suggested in [209]-[211]. Moreover, an IEEE 802.15.4

based MAC protocol with adaptive active period and turned-o beacons has been

proposed to enable a low power data transmission mechanism for WSNs used in

long-lived smart utility networks [38]. Based on the design concept of Z-MAC

[57], Gilani et al. [212] have developed an adaptive CSMA/TDMA hybrid IEEE

802.15.4 MAC protocol for the better performance in trac varying WSNs. By

comprehensively reviewing the requirements of human health monitoring applica-

tions, Li et al. [213] have presented a modied IEEE 802.15.4 protocol known as

Hybrid unied-slot access (HUA) protocol, which allocates radio resources exi-

bly in wireless body area networks (WBANs). Similar amendments introduced to

the standard protocol in the context of WBANs can be found in [214]-[216]. How-

ever, all most all these existing application-specic modications were developed

for WSNs with a single monitoring scenario, and none of them were designed to

meet the requirements of hybrid monitoring WSNs.

This chapter presents a new wireless MAC mechanism that enables the IEEE

802.15.4 standard to provide an energy ecient, reliable, and delay bounded data

transmission for hybrid monitoring WSNs. The new protocol modies the `hy-

brid medium access mechanism' proposed in the IEEE 802.15.4 standard (See

Chapter 2 Section 2.4.3.1) by introducing a TDMA schedule to transmit regu-

lar LTPM trac while utilising the standard CSMA/CA mechanism to transmit

rarely and randomly generated ED trac. Several techniques are suggested with

the proposed hybrid protocol to improve the energy performance of LTPM data

transmission, and the reliability in ED data transmission. Given the monitoring


requirements of the underlying application, the hybrid protocol guarantees the

required quality of service (QoS) in data transmission by controlling the number

of nodes allowed to associate with the network. The proposed protocol was im-

plemented using ns-2 [105], and its performance was evaluated in terms of energy

consumption, delay, and reliability in data transmission. Extensive ns-2 simula-

tions verify the improved performance of the proposed protocol in the context of

hybrid monitoring.

6.2 System Model

The system model considered in this chapter is a beacon enabled IEEE 802.15.4

star topology network composed ofN sensor nodes and a common network coordi-

nator. Each sensor node contains at least two dierent sensors and simultaneously

performs two monitoring scenarios (LTPM + ED) as shown in Figure 6.1.

In the LTPM application, all nodes synchronously monitor the phenomenon of

interest throughout a Tm period for a given monitoring instance. The monitoring

instances occur periodically for every Tcycle duration. During a given monitoring

instance each node generates m data frames of Lltpm backos slots. The LTPM

data frames generated at all nodes should be received completely at the network

coordinator-node (i.e., the sink-node) within Trpt duration, where Trpt ≤ Tcycle,

to enable end-users to extract useful information before the next monitoring in-

stance.

In the ED application, all nodes monitor the phenomenon of interest syn-

chronously with a sampling frequency of 1/Ted. When an event is detected, each

node generates a single data frame of Led backo slots. The alarm data generated

at all nodes should be received at the network coordinator-node before d seconds,

where d < Ted << Trpt, to alert end-users in a timely manner. Due to the delay

constraint on alarm data transmission, the network always operates in the active


(a) LTPM scenario

(b) ED scenario

Figure 6.1: Hybrid monitoring scenario.

mode1 (i.e., SO = BO). Furthermore, it is assumed that all nodes are within

the carrier sensing range of each others, and only uplink data transmission exists

within the network.

6.2.1 QoS Requirements of Hybrid Monitoring Application

In the system considered, LTPM generates regular data streams in contrast to

the random and infrequent event detections. Therefore, LTPM data should be

transmitted with a minimum energy cost to achieve overall energy eciency in

data transmission. Moreover, the reliability of LTPM data transmission is critical1Only the coordinator-node remains active always, Sensor nodes power-o their radio during

idle times of medium access for energy eciency.


as almost all LTPM data are required to deduce the long-term behaviour of a

certain phenomenon of interest. On the other hand, the delay in transmission is

not a major concern for LTPM data, and hence nodes are allowed to exploit the

`store now and transmit later' mechanism.

On the contrary, ED data have to be transmitted within a stringent time

constraint imposed by the monitoring application. Moreover, they have to be

delivered reliably to generate the complete monitoring report of a given precarious

situation. Energy eciency is not considered as a key requirement for ED data

transmission due to the rarity in event detection.

6.3 New MAC Mechanisms

Because of the disparity between the data generation and QoS requirements of two

monitoring scenarios, two distinct channel access mechanisms are proposed for

LTPM and ED data transmissions. These new MAC mechanisms are developed

on the IEEE 802.15.4 standard without altering any of its PHY layer function-

alities. However, each of the new mechanisms amends the IEEE 802.15.4 MAC

layer functionalities in its own way to meet the requirements of the corresponding

monitoring application as described in the next sections.

6.3.1 MAC Mechanism for LTPM Data Transmission

In the LTPM application, each node generates a signicant amount of data; thus,

the network operates with a high trac volume. Under such trac conditions,

the IEEE 802.15.4 CSMA/CA mechanism - the basic MAC protocol of the stan-

dard - fails to transmit data reliably due to the competition in medium access

[36][93][150]. This competition, which is inherited in any contention based MAC

protocol, causes a signicant amount of energy wastage on backing o, channel

sensing, and frame retransmissions as shown in Chapter 3 Section 7. Therefore,

a scheduled based mechanism that eliminates the competition in medium access


is required to achieve a reliable and energy ecient LTPM data transmission.

The IEEE 802.15.4 standard provides a schedule based access mechanism us-

ing an optional GTS mechanism (See Chapter 2 Section 2.4.3.1 for more details).

However, this mechanism fails to guarantee dedicated channel resources for all

nodes in large networks as it can allocate only up to seven GTSs at a time. Fur-

thermore, the length of a GTS is restricted to integer multiplies of a superframe

slot1; therefore, a GTS may not exactly t with the duration of a given data

transmission causing a wastage in scarce channel resources. These drawbacks of

the standard GTS mechanism have been investigated in the literature, and some

improvements have been proposed in [205][206][214] with the cost of additional

control overheads. Even in these improved versions of the GTS mechanism, nodes

have to request GTSs from the network coordinator at each time they generate

data. Although this demand based resource allocation is well suited for networks

with dynamic trac generation (e.g., military applications), it creates a signi-

cant amount of unnecessary control overheads (used for allocation, deallocation,

and reallocation of GTSs every now and then) in networks deployed in LTPM

applications where each node periodically generates the same amount of data.

Therefore, instead of the demand assignment GTS mechanism, an IEEE

802.15.4 based xed assignment mechanism is proposed to transmit LTPM data

reliably with minimal energy. As shown in Figure 6.2, the new xed assignment

mechanism is essentially a TDMA based protocol in which the sensor nodes access

specic durations of superframes - known as dedicated time slotss (DTSs) - in a

round robin manner to transmit LTPM data. DTSs in all superframes are equal

in length, and they are located at the end of superframes (i.e., just before the bea-

cons) to minimise time synchronisation errors [217]. During DTSs, LTPM data

frames are transmitted as a continuous ow2 without following the IEEE 802.15.4

CSMA/CA mechanism. Access to the DTS in a given superframe is limited to

1superframe slot = 2SO × 3× unit-backoslot , where SO represents the superframe order.2Data frames are only separated by interframe spacing (IFS) dened in the IEEE 802.15.4

standard.


Figure 6.2: DTS mechanism.

a single node; therefore, dierent nodes access DTSs at dierent superframes

enabling a contention-free MAC mechanism1 for LTPM data transmission.

In a network with N sensor nodes, N consecutive superframes form a trans-

mission cycle where each node gets access to the DTS once (Figure 6.2). The

arrangement of DTSs in a transmission cycle, which is denoted as the DTS sched-

ule, can be completely described using three parameters: the length of the DTS

Tdts, beacon interval of the network BInwk, and number of nodes in the network

N . The network coordinator broadcasts these parameters at the beginning of

each transmission cycle using a special beacon known as the superbeacon. The

format of the superbeacon and calculation of parameters Tdts and BInwk for a

network of N sensor nodes will be presented in detail in Section 6.4.1.

The proposed protocol provides a simple TDMA scheduling mechanism com-

pared with the complex solutions proposed in [36][212] due to its `xed length

and location of DTSs' and `Single DTS per superframe' conditions. Moreover, it

allows nodes to shutdown their transceivers for all the time but their respective

DTSs and beacon durations and thereby minimises the energy consumption in1See Section 6.4.2 for the implementation of this mechanism.


radio-communication. The proposed DTS based MAC mechanism will be referred

to as the DTS mechanism throughout this chapter for clarity.

6.3.2 MAC Mechanism for ED Data Transmission

In the ED application, all data frames have to be transmitted within a stringent

delay constraint. Clearly, the DTS mechanism mentioned above is unable to

meet this strict requirement as it may delay a particular data transmission up to a

complete transmission cycle (i.e., N.BInwk). Thus, the IEEE 802.15.4 CSMA/CA

mechanism - the random access scheme of the standard - is proposed to use during

the periods in between DTSs to transmit randomly generated ED data frames.

The idea of using the CSMA/CA mechanism for a delay bounded reliable

data transmission has its own doubts. As mentioned in the previous section,

CSMA/CA based MAC protocols discard many data frames due to the contention

in medium access at high trac condition. On the other hand, the results pre-

sented in Chapter 3 (see Figures 3.8(c) and 3.9(c)) suggest that the IEEE 802.15.4

CSMA/CA protocol with ACK frames can provide a reliable data transmission

for a large range of network set-ups with dierent number of nodes N and frame

lengths L at low frame arrival rates, which is the typical scenario of the ED appli-

cation. However, it should be noted that this observation was made for networks

with Poisson frame arrivals. On the contrary, in the ED application, data frames

are generated at all nodes synchronously. The synchronicity of frame generation

creates a congestion period in data transmission just after each detection instance

(Figure 6.1); thus, it adversely aects the reliability in data transmission regard-

less of the sampling frequency of the ED application [137][218][219]. Therefore,

as described next, additional techniques have to be devised to improve the data

transmission reliability of the IEEE 802.15.4 CSMA/CA protocol in networks

with synchronised low density trac.


6.3.2.1 Improving Data Transmission Reliability of networks with

synchronised trac

The MAC layer unreliability problem of the IEEE 802.15.4 based WSNs that gen-

erate synchronised trac has been studied comprehensively in [137][218]. Using

experimental results, Francesco et al. [137][218] have emphasised the protocol's

inability to achieve an acceptable degree of transmission reliability, when MAC-

layer parameters have their default values. As a remedy, they have proposed

using of non-standard (out of the possible range) values for MAC parameters

with the cost of longer delays in data transmission. To avoid the synchronised

transmissions occurred right after the radio inactive period of the IEEE 802.15.4

MAC protocol, Misi¢ et al. [220] have suggested deferring each data transmission

for a duration of a frame length. However, their suggestion has overlooked the

impact of the network size on medium access congestion, and hence it fails in

networks with large number of nodes.

Following Misi¢ et al. [220] approach, this section proposes to apply a random

delay for each data frame to break the synchronicity in `starting the CSMA/CA

mechanism at the MAC layer' of networks with synchronised frame arrivals. By

introducing a pseudo-randomness to arrival process, the random delays make

data frames to appear at theMAC layer at dierent time instances1, even though

they are generated synchronously at the Application layer. The proposed random

delays are uniformly distributed within the window [0, Drnd]. The maximum value

of the random delay Drnd can be quantied as Drnd ∝ NL by owing to the fact

that the congestion of the common transmission medium is increased with the

number of nodes in the network N and the length of the data frames L (Section

3.7.2 in Chapter 3). Using a proportional constant δ, Drnd can be given as

Drnd = δNLtunit-backoslot , (6.1)

1Once data frames arrive at MAC layer, the standard CSMA/CA mechanism is used totransmit them.


where tunit-backoslot denotes the duration of a single backo slot1.

The parameter δ is quantied using simulation based experiments, and the

related results are presented in Appendix D.1. According to the experimental

results, when δ ≥ 1.0 the random delay technique improves the data transmission

reliability of networks with synchronised data arrivals up to a certain level that

can be approximated using the reliability of equivalent networks having Poisson

data arrivals. However, it should be noted that larger δ values will increase

Drnd, which in turn increases the delay in transmission. Thus, only the minimum

δ value that validates the above reliability approximation (i.e., δ = 1.0) will be

considered for the rest of the study. For simplicity, the IEEE 802.15.4 CSMA/CA

protocol associated with the random delay technique will be referred to as the

randomly-delayed CSMA/CA throughout this chapter.

Given that the randomly-delayed CSMA/CA provides a reliable transmission,

an upper bound Dmax for the delay in data transmission can be presented as

Dmax = Drnd +Dcsma, (6.2)

where Drnd and Dcsma represent the maximum delays introduced by the ran-

dom delay and the standard CSMA/CA mechanism, respectively. While Drnd is

computed using (6.1), Dcsma can be given as

Dcsma =

[∑macMaxBE - 1

i=macMinBE2i + [(macMaxCSMABackos + 1) + macMinBE

−macMaxBE ]× 2macMaxBE

+ncca × (macMaxCSMABackos + 1) + (Led + tack)

]× (macMaxFrameRetries + 1)× tunit-backoslot , (6.3)

where macMinBE, macMaxBE, macMaxCSMABackos, and macMaxFrameRe-

tries represent the CSMA/CA related parameters (denitions and default values

1= 0.00032 s in the 2.4-GHz physical layer.


can be found in Table 3.1 in Chapter 3); and ncca, Led, tack , and tunit-backoslot

represent the number of CCAs before a transmission (default value = 2), length

of the ED data frame in backo slots, waiting time for relevant ACK frame (= 3

backo slots), and the duration of a backo slot, respectively.

Thus, it appears that the randomly-delayed CSMA/CA mechanism not only

improves the data transmission reliability, but also delivers data within a certain

delay boundary.

6.4 Hybrid Protocol

After choosing appropriate MAC mechanisms for each monitoring application, it

has to be meticulously decided how these two MAC mechanisms are going to be

coexist in hybrid monitoring WSNs. The straightforward solution would be using

a switching scheme where the application layer essentially signalling the MAC

layer to use the most appropriate MAC for the application. Even though this

approach is standard compliant and easy to implement, it will not be optimal

in hybrid monitoring. If both applications generate data simultaneously, the

switching scheme can only serve a single application as only one MAC mechanism

can operate at a given time with this scheme. Therefore, in such situations,

measurements from the other application could not be collected within the delay

constraint specied. This causes a measurement hole in the monitoring, and

consequently it will jeopardise the integrity of the hybrid monitoring application.

Therefore, in this section, the proposed medium access control mechanisms for

LTPM and ED data transmissions are merged (instead of switched between each

other) to create a hybrid data transmission mechanism that fulls QoS require-

ments of the hybrid monitoring application. In this hybrid protocol, LTPM data

are transmitted as a continuous ow during the DTS of each superframe, and then

the time in between DTSs (i.e., contention access period (CAP)) is utilised to

transmit delay bounded ED data using the randomly-delayed CSMA/CA mech-


Figure 6.3: Hybrid MAC mechanism.

anism as shown in Figure 6.3.

These two MAC mechanisms are unied with the following strategies to guar-

antee the delay bounded transmission of ED data even in the presence of DTSs:

Backo Counter Freezing: Suppose an event has occurred just before the

beginning of a DTS. Then, the corresponding ED data frames may be dropped

due to the channel access failures as the channel is busy with LTPM data trans-

missions. To avoid this scenario, the backo counter (including the random delay)

of the randomly-delayed CSMA/CA freezes during the time reserved for DTSs.

When the DTS duration expires, the backo counter resumes decreasing its value

as usually. This strategy virtually hides the DTS schedule from the random-

delayed CSMA/CA mechanism.

Minimum Duration of CAP: Suppose the CAP of superframes is too

small such that the ED data frames generated by a particular event have to be

transmitted over a several superframes. This will increase the delay in ED data

transmission as the backo-counter-freezing eect adds an additional Tdts waiting


time for each superframe. Therefore, the minimum duration of CAP of the hybrid

protocol is set to Dmax (Equation 6.2) to ensure that ED data transmissions of

an event occurred at any instant of the current sueperframe will be completed

before the beginning of the DTS in the next superframe. In other words, this

strategy limits the additional delay experienced by ED data transmissions, due

to the unication process, to a single Tdts duration.

Because of the above strategies, the duration of DTS Tdts needs to be con-

trolled to meet the delay requirement of ED data transmission. On the other

hand, controlling Tdts limits the amount of LTPM data transmitted during a su-

perframe, which in turn limits the total amount of LTPM data transmitted by all

nodes in a given network within Trpt. Therefore, for a given hybrid monitoring

application, there exists a maximum number of nodes Nmax that can operate with

the proposed hybrid protocol.

The proposed hybrid protocol has two operational phases: initial phase and

steady phase. At the beginning of the the initial phase Nmax is computed. Then,

the network set-up takes place by associating sensor nodes and establishing the

DTS schedule. After the initial phase, the steady phase commences where all data

transmissions and modications to the established DTS schedule (if required)

occur. The two phases are described in detail below.

6.4.1 Initial Phase

The initial phase starts with the initialisation of the network coordinator. During

its initialisation, the network coordinator is fed with the following requirements

of the hybrid monitoring application:

• Number of LTPM data frames m generated during Tm in a sensor node,

• Reporting cycle Trpt of LTPM data,

• Delay bound d for ED data,


• Length of LTPM data frames Lltpm,

• Length of ED data frames Led.

Using the above application-specic requirements and a few other IEEE 802.15.4

protocol-specic characteristics (i.e., beacon frame duration, Rx-to-Tx turnaround

duration, inter frame spacing (IFS), CSMA/CA parameters, and unit bakco

length) the network coordinator calculates the following quantities:

• Transmission duration tltpm of single LTPM data frame (including IFS),

• Maximum delayDcsma caused by CSMA/CAmechanism (See Equation (6.3)),

• Transmission duration tbcn of beacon frames (including Rx-to-Tx turnaround).

Next, the network coordinator nds the number of nodes Nmax that can be

associated with the network by calculating the following intermediate parameters:

DTS length Tdts: This can be given as

Tdts = nltpmtltpm, (6.4)

where nltpm is the number of LTPM data frames transmitted within the DTS.

nltpm can be computed as

nltpm =

⌊d− (tbcn +

Dmax︷︸︸︷Drnd +Dcsma)

tltpm

⌋, (6.5)

by considering the fact that the maximum delay in ED transmission should be

less than the delay bound of the ED application, i.e.,

Tdts + tbcn +Dmax < d. (6.6)

Number of DTSs ndts required for a single node to transmit LTPM

data within a reporting cycle: Since the number of LTPM frames that a node


has to transmit during a single report cycle equals m,

ndts =

⌈m

nltpm

⌉. (6.7)

Minimum beacon interval BImin−nwk that the network can operate: Due

to the minimum duration of CAP, the network should operate with a BI1 that

satises,

BI ≥Tdts︷︸︸︷

nltpmtltpm +

Dmax︷︸︸︷Drnd +Dcsma +tbcn. (6.8)

Thus, the minimum BI value that satises the above inequity gives BImin−nwk.

Then, Nmax can be obtained using BImin−nwk as

Nmax =

⌊Trpt

ndtsBImin−nwk

⌋(6.9)

due to the fact that a single DTS corresponds to a single BI.

However, it should be noted that the calculation of Drnd in (6.5) and (6.8)

requires the knowledge about total number of nodes in the network [refer (6.1)].

Since this information is not available at the beginning of the initial phase2,

the recursive algorithm described in Algorithm 6.1 is used to nd Tdts, ndts,

BImin−nwk, and Nmax. The algorithm initialises with the maximum number of

nodes Nmax−ltpm allowed to associate with the network when there exists only the

LTPM application [See Appendix D.2].

Once Nmax is calculated, the network coordinator begins the association pro-

cess to allow sensor nodes to join the network. For this purpose, the network

coordinator starts a countdown timer3, sets BI to BImin−nwk, and begins bea-

con transmission. When beacons are received, sensor nodes follow the standard1Because of the assumption SO = BO, the beacon interval and superframe duration can

be interchanged. As mentioned in Chapter 2, BI = aBaseSuperframeDuration × 2BO, where0 ≤ BO ≤ 14. Thus, BI can represent only 15 dierent values.

2The network coordinator is in the process of nding the maximum number of nodes per-mitted in this phase.

3The countdown timer sets the duration of the initial phase, Tinit.


Algorithm 6.1 Find Nmax, Tdts, ndts, and BImin−nwk.Calculate Nmax−ltpmInitialise : Nprev = 0; Ncurrent = Nmax−ltpmwhile Ncurrent 6= Nprev doNprev ← Ncurrent

Calculate Drnd substituting Nprev in (6.1)Calculate Tdts, ndts, BImin−nwk, and Nmax using (6.4) (6.9)Ncurrent ← Nmax

end whilereturn Tdts, ndts, BImin−nwk, and Nmax

association procedure specied in [61]. For each node associated, the network co-

ordinator assigns a unique number (denoted as association number) that equals

i for the i-th node to associate, where i ∈ N and 1 ≤ i ≤ Nmax. The associa-

tion numbers are used to distinguish sensor nodes from each other instead of the

64-bit long device addresses recommended by the IEEE 802.15.4 standard. The

respective association number and the remaining time to expire the initial phase

(i.e., current value of the countdown timer) are informed to each node during

their associations. The information about the expiring time of the initial phase

enables nodes to shutdown their transceivers until the beginning of the steady

phase.

The network association process ends either by expiring the countdown timer

(i.e., elapsing Tinit duration) or by reaching the number of nodes associated with

the network N to Nmax. In the latter case, the network coordinator waits until

the countdown timer expires to stop the initial phase. At the end of the initial

phase, the network coordinator calculates the Drnd, nltpm, Tdts, and ndts using

(6.1), (6.4) (6.7) for the current network of N nodes. To reduce the energy

consumed for beacon listening in the current network, the network coordinator

then sets BI to its maximum value BInwk that can operate with the given LTPM

application. For a network of N nodes, BInwk equals to the maximum value of


Figure 6.4: SuperBeacon payload.

BI that satises the inequality

Trpt ≥ NndtsBI. (6.10)

Finally, the network coordinator declares the end of the initial phase by trans-

mitting the rst superbeacon. The superbeacon frame fully conforms with the

standard beacon format specied in [61] and carries DTS-schedule-specic infor-

mation in its payload as illustrated in Figure 6.4.

SuperBeacon Payload: The superbeacon payload consists of three com-

mon elds: isSuperBeacon, superFrameNumber, and ACK ; and three superbea-

con elds: isChanged, dtsSchduleData, and disAssociatedNodes. The common

elds appear in both superbeacons and general beacons (i.e., the beacons trans-

mitted in between superbeacons) of the hybrid protocol, while the superbeacon

elds present only in superbeacons.

The single bit long isSuperBeacon eld is used to distinguish superbeacons.

It is set to one in superbeacons and to zero in general beacons. The superFra-

meNumber is 10 bits in length and contains the corresponding sequence number

of the current superframe within the transmission cycle. It is always set to one

in superbeacons owing to the fact that each superbeacon marks the beginning of

respective transmission cycles. On the other hand, the superFrameNumber may


vary from two to N1 in general beacons depending on their positions within the

transmission cycle. The ACK eld is one bit in length and is set to one if and only

if all LTPM data frames transmitted during the DTS of the previous superframe

were received successfully. If not, the ACK eld is set to zero.

The isChanged eld of superbeacon elds is set to one if the existing DTS

schedule has been modied. Otherwise it is set to zero. The dtsSchduleData eld

contains the number of node associated with the DTS schedule N and duration

of the DTS in backo slots Tdts. The beacon interval of the network, which is

the remaining parameter required to construct the DTS schedule, is explicitly

contained in the `Superframe Specication' eld of the beacon frame (see Figures

44 and 47 in [61]). Therefore, it is not included in the dtsSchduleData eld to

avoid the repetition.

If the current DTS schedule has been changed due to disassociation of some

nodes, the disAssociatedNodes eld will contain their association numbers. This

information helps the remaining nodes to adjust to the new DTS schedule by

reordering their association numbers as described in the next section.

6.4.2 Steady Phase

The steady phase starts with the transmission of the rst superbeacon. At the

beginning of this phase, all associated nodes listen to the superbeacon, obtain the

DTS-schedule-specic parameters, and consequently construct the DTS schedule

that is followed by the entire network. Then, nodes track the sequence number

of each superframe (by decoding the superFrameNumber eld of beacon frames)

to start the data transmission.

Data Transmission: In the i-th superframe of the transmission cycle, the

node with association number i (i ∈ 1, 2, ..., N) accesses the DTS and transmits

LTPM data. If the data transmission in the DTS was successful (i.e., ACK eld

1i.e., the total number of nodes associated with the network. For practical WSNs, themaximum value of N is assumed to be equal to 1024.


of the next beacon is received as one), the node's transceiver goes back to sleep.

Otherwise, the node retransmits the corrupted or lost LTPM data1 immediately

using the standard CSMA/CA mechanism without waiting for its next allocated

DTS. Meanwhile, if an event is detected all nodes transmit ED data using the

randomly-delayed CSMA/CA mechanism during the CAP in between DTSs. Af-

ter N superframes, the network coordinator transmits the next superbeacon to

indicate the beginning of the next transmission cycle where the data transmission

continues in similar manner.

Apart from the data transmission, the association of new nodes and disassoci-

ation of existing nodes may also occur during the steady phase. New nodes may

be associated with the network either to replace dead nodes or to increase the

monitoring entities. On the other hand, existing nodes those fail to transmit data

(due to depleted energy level or other hardware failures) would be disassociated

from the network to avoid under-utilised resource reservations.

New Node Association: A new node added to the network follows the

standard association procedure of the IEEE 802.15.4 standard. It sends an às-

sociation request' command once it receives a beacon frame (Note: Since this

communication takes place at the beginning of superframes, it does not overlap

with DTSs located at the end of superframes). When the network coordinator

receives the àssociation request' command, it veries the total number of nodes

in the network including the new node Nnew with Nmax computed in the initial

phase. If Nnew ≤ Nmax, the network coordinator accepts the association and send

àssociation success' command with the relevant association number. Otherwise,

it ignores the request. Although the association is success, the new node has to

wait until the next transmission cycle to transmit data.

Meanwhile, the network coordinator calculates the new values for Tdts, ndts,

and BInwk for the network of Nnew nodes using (6.4) (6.10). Then, it broadcasts

1Although the DTS mechanism provides a non-contentional MAC scheme, data frames canbe still lost or corrupted due to channel errors and other interferences.


the new DTS schedule by transmitting a superbeacon with modied parameters

at the beginning of the next transmission cycle. Accordingly, the nodes update

the necessary parameters, reconstruct the DTS schedule, and continue with the

steady phase.

Existing Node Disassociation: If a node does not transmit during its

DTS for consecutive 2j transmission cycles, the network coordinator disassoci-

ates that node from the network. By following a similar approach to the GTS

disassociation in the standard protocol [61], the value of j is determined as

j = 2(8−BOnwk) 0 ≤ BOnwk ≤ 7

j = 2 8 ≤ BOnwk ≤ 14 (6.11)

where BOnwk represents the current beacon order of the network. Due to the lim-

ited length of disAssociatedNodes eld in superbeacons, the network coordinator

can disassociate only up to four nodes during a transmission cycle.

At the beginning of the next transmission cycle after a disassociation, the net-

work coordinator informs the remaining nodes about the modications required

to the DTS schedule (i.e., new values of Tdts, N , and BInwk) along with the as-

sociation numbers of the disassociated nodes. The remaining nodes then update

the relevant parameters, and change their association numbers appropriately1.

This reordering maintains the continuity in the sequence of association numbers,

and hence it guarantees the allocation of DTSs for all the remaining nodes in the

new DTS schedule.

If a node does not have any data to transmit during its allocated DTS2, it

will transmit a dummy data frame at the beginning of that DTS to avoid being

disassociated. The dummy frame has the same format of a general data frame1i.e., the nodes with higher association numbers than that of the disassociated nodes de-

crease their association numbers by relevant steps, while the others remain with the existingassociation numbers.

2In general, this situation may occur near the end of Trpt period as transmission cycles maynot perfectly align with the reporting cycle.


(Figure 52 in [61]), however its payload contains only a single byte of all zeros.


In this section, the performance of the proposed MAC mechanism is investigated

using simulation based experiments. To this end, a new simulation platform was

developed on top of the existing implementation of the IEEE 802.15.4 protocol in

ns-2 simulator [108][126]. The functionalities of the protocol were implemented

at the Service Specic Convergence (SSCS) and Medium Access Control (MAC)

sub-layers either by developing new modules or by modifying the existing mod-

ules (note: A summary of the new implementation is presented in Appendix

C.2.) These new implementations and modications were done in a way such

that a network can operate in one of the three dierent MAC mechanisms: the

DTS mechanism1, randomly-delayed CSMA/CA mechanism, and hybrid (DTS

+ randomly-delayed CSMA/CA) mechanism.

Using the new simulation platform, four dierent experiments were carried

out to evaluate the performance of the proposed hybrid MAC protocol and its

basic components. In the rst experiment, the performance of the DTS mecha-

nism is studied by investigating its ability to deliver a reliable, energy ecient

data transmission mechanism for LTPM data. The second experiment investi-

gates the performance of randomly-delayed CSMA/CA mechanism in delivering

delay-bounded, reliability-critical ED data. Then in the third experiment, the

performance of the hybrid MAC protocol is studied in the context of a hybrid

monitoring scenario deployed in a structural health monitoring (SHM) applica-

tion. Finally, the network scalability of the proposed hybrid protocol is examined

during the fourth experiment.

In all experiments, a beacon enabled star topology network comprised with N

sensor nodes and a common coordinator is assumed. The 2.4 GHz physical layer1Algorithms related to this mechanism can be found in Appendix D.2.


Table 6.1: Simulation parameters.

Parameter Value Parameter Value

PHY layer 2.4 GHz band

Channel rate 250 kbps Radio Characteristics

Propagation model Two-ray ground Transmit Power 30.67 mW

PHY header 6 Bytes Receive Power 35.28 mW

MAC header 13 Bytes Idle Power 712 µW

tunit-backoslot 320 µs Sleep Power 144 nW

tbcn 3 backos

tack 3 backos MAC Parameters

IFS 2 backos Default Max

Rx-to-TX turnaround 0.6 backos macMinBE 3 8

Tinit 600 s macMaxBE 5 8

δ 1.0 macMaxCSMABackos 4 5

BO†† 9 macMaxFrameRetries‡ 3 7

SO† 9† only used in simulations related to the standard protocol‡ macMaxFrameRetries is set to 2 in hybrid protocol simulations to minimise Dcsma

and the two-ray ground propagation model are chosen as the wireless communi-

cation channel between sensor nodes and the coordinator. The wireless channel

is assumed to be error-free, and no hidden nodes are considered. Thus, in all sim-

ulations, frames are dropped only due to channel access failures (i.e. number of

backo stages exceedmacMaxCSMABackos+1) or frame retransmission failures

(i.e. number of transmission attempts exceed macMaxFrameRetries + 1). Unless

mentioned otherwise, ACK frames and frame retransmissions are deployed in all

experiments, and the default values of all MAC-layer parameters are assumed.

Furthermore, nodes transceivers are assumed to be Chipcon CC2420 radios, and

hence their energy characteristics are represented using the energy model de-

veloped in [126][149]. These radio characteristics along with other simulation

parameters are summarised in Table 6.1. Simulation trials for each experiment

are run independently, and their results are averaged over 50 dierent seeds.


6.5.1 Experiment 1 - Performance Evaluation of the DTS

Mechanism

In this experiment, the performance of the DTS mechanism is studied in terms

of its reliability (expressed by the reliability factor 1 R) and power consumption

(expressed by the per-bit energy cost2) of LTPM data transmission. For compar-

ison, the performance of the standard medium access mechanism of the IEEE

802.15.4 MAC protocol (i.e., CSMA/CA mechanism) in LTPM applications is

also examined. Simulation results were obtained for three LTPM scenarios of

dierent data generation rates represented by three m values3: 250, 500, and

1000. For all simulations, the reporting cycle Trpt and LTPM data frame length

Lltpm were set to 10 minutes and 12 backo slots, respectively. In simulations

associated with the IEEE 802.15.4 CSMA/CA mechanism, LTPM data frames

were transmitted at continuous bit rates (CBRs) throughout the reporting cycle

to minimise the contention in channel access. Each simulation trial was run for

a 100 Trpt duration.

Reliability and power consumption of the DTS mechanism and IEEE 802.15.4

CSMA/CA mechanism in LTPM data transmission are shown in Figures 6.5 and

6.6, respectively. As shown in Figure 6.5, the IEEE 802.15.4 CSMA/CA mech-

anism fails to transmit LTPM data reliably even in networks with a few sensor

nodes (N ≤ 8) due to its contention based medium access. Moreover, the data

transmission reliability of the CSMA/CA mechanism decreases rapidly with in-

creased network size and data generation, as they intensify the contention in

medium access. A substantial improvement can be observed in the data trans-

mission reliability when the CSMA/CA mechanism is applied with ACK frames

1i.e., the ratio between the number of frames successfully received by the coordinator andthe total number of frames generated by all sensor nodes.

2i.e., the average power consumption per successfully transmitted data bit.3Note: When the reporting cycle Trpt and LTPM data frame length Lltpm are xed, the

data generation rate can be completely represented by the number of data frames m generatedwithin a reporting cycle.


Figure 6.5: Reliability in LTPM data transmission.

and frame retransmissions. However, these improved values too fall below the ex-

pected degree of reliability of LTPM data transmission (i.e., R ≈ 100%). On the

other hand, irrespective of the network size and data generation rate, the DTS

mechanism achieves the ideal data transmission reliability for LTPM applications

by completely eliminating the contention in medium access (note: R curves and

per-bit energy cost curves of DTS mechanism for dierent m values lie on top of

each other in Figures 6.5 and 6.6, respectively).

Contrast to the signicantly increasing power consumption of the IEEE 802.15.4

CSMA/CA protocol, that of the DTS mechanism remains constant with increased

network sizes and data generation rates. More Importantly, that constant value

is signicantly less than the power consumption of the IEEE 802.15.4 CSMA/CA

protocol (both with and without ACK) in all LTPM scenarios considered. For

instant, when m = 1000 and N = 40 the per-bit energy cost of the DTS mecha-

nism is approximately 12 times and 16 times less than that of the IEEE 802.15.4

CSMA/CA protocol with ACK and without ACK, respectively (Figure 6.6).


Figure 6.6: Power consumption in LTPM data transmission.

6.5.2 Experiment 2 - Performance Evaluation of the Randomly-

delayed CSMA/CA Mechanism

This experiment investigates the performance of the randomly-delayed CSMA/CA

mechanism in terms of delivering a reliable and delay bounded transmission mech-

anism for ED applications that generate synchronised low-density trac. To

this end, eight network congurations comprised of dierent network sizes N

and frame lengths Led were considered. In each network conguration, sensor

nodes were deployed in an ED application where they sample the monitoring

phenomenon at a rate of one sample per second synchronously. By consider-

ing the worst case scenario, it was assumed that each node detects an event at

each sampling instance, and hence generates ED frames at the sampling rate.

ED frames are transmitted to the coordinator node using three dierent sets of

transmission strategies:

1) IEEE 802.15.4 CSMA/CA with default MAC parameter values,

2) IEEE 802.15.4 CSMA/CA with maximum MAC parameter values, and


3) Randomly-delayed IEEE 802.15.4 CSMA/CA with default MAC parameters

values (δ = 1.0).

Simulation trials of each transmission strategy in a given network conguration

run until each node completes 20, 000 frame transmissions.

As shown in Figure 6.7a, the standard IEEE 802.15.4 CSMA/CA protocol

with the default parameter values is not reliable enough to transmit synchronised

low-density ED trac. This is because the default MAC parameter values are

unable to break the synchronicity in data transmission procedures. On the other

hand, the maximum MAC parameter values relax that synchronicity up to some

extend; therefore, the standard protocol with the maximum parameter values

exhibits an acceptable level of reliability in data transmission particularly in small

networks (N ≤ 10). Nevertheless, this transmission strategy too fails in data

transmission reliability when the network size and/or frame length increase. In

contrast, the randomly-delayed CSMA/CA mechanism achieves an almost ideal

degree of reliability1 (i.e., R ≈ 100%) in all considered networks as it completely

breaks the synchronicity in data transmission regardless of network size and frame

length.

The maximum transmission delay caused by the randomly-delayed CSMA/CA

mechanism is depicted in Figure 6.7b. For each network conguration, the av-

erage and range2 of the maximum delay are presented along with the respective

theocratical upper bound Dmax, which is derived in Section 6.3.2. As shown in

Figure 6.7b, the maximum delay in data transmission increases with network size

N and frame length L as Drnd ∝ NL. This relationship has been already taken

into account while calculating Dmax, and hence the experimental maximum delay

lies noticeably below the theocratical upper bound for all networks considered.

Therefore, it is apparent that a randomly-delayed CSMA/CA based WSN de-

1Note that the randomly-delayed CSMA/CA is a contention based MAC mechanism; thus,it does not guarantee R = 100%.

2i.e., the dierence between the maximum and minimum values obtained for the maximumdelay in all simulation trials related to a given network conguration.


(a) Reliability

(b) Maximum delay

Figure 6.7: Performance of the randomly-delayed CSMA/CA mechanism in EDdata transmission: (a) reliability R (b) maximum delay.


Table 6.2: Sensor measurements and monitoring application requirements.

Sensor Characteristics Application requirementsMeasuring Number Sampling Sample Monitoring Reporting Delay

quantity of axes frequency size period period constraint

naxes fs (Hz) ls (bits) Tm (s) Trpt (min) d (s)

Acceleration 3 200 16 210 30 −Strain 3 1 16 − − 0.25, 0.5, 1.0

ployed in an ED application will satisfy the delay constraint d of the monitoring

application when Dmax ≤ d.

6.5.3 Experiment 3 - Performance Evaluation of the Hy-

brid Protocol

In this experiment, a proof of concept for the proposed hybrid MAC protocol is

presented in the context of a structural health monitoring (SHM) application.

Then, the performance of the hybrid protocol is examined in the same context

with regard to its reliability, power consumption, and delay in data transmission.

The SHM application considered in this experiment is a hybrid monitoring

application where a WSN is deployed to monitor structural strains and acceler-

ations, which are the most common SHM measurements found in the literature

[221]. Thus, each sensor node in this hybrid monitoring WSN is comprised of an

accelerometer and a strain transducer. Sensor nodes collect acceleration measure-

ments periodically to enable long-term structural health analyses, while monitor-

ing strains to detect sudden anomalies in the structural health. The character-

istics of these sensor measurements and the monitoring application requirements

are formulated in Table 6.2 based on previous studies on SHM [221]-[223].

Using the SHM application requirements in Table 6.2 and the IEEE 802.15.4

protocol-specic characteristics in Table 6.1, the initialising-parameters m, Lltpm,

and Led of the hybrid protocol in the above monitoring application can be de-

termined as 2400 frames, 12 backo slots, and 3 backo slots, respectively (See


Table 6.3: DTS-scheduling parameters Tdts, ndts, and BInwk (when Trpt = 30 min.).

Application 1 Application 2 Application 3Number d = 0.25 s d = 0.5 s d = 1.0 s

of m = 2400 m = 2400 m = 2400

nodes Tdts† ndts BInwk

† Tdts ndts BInwk Tdts ndts BInwkN [BO]‡ [BO] [BO]

8 336 100 1.96608 [7] 1120 30 3.93216 [8] 2590 13 15.72864 [10]

16 322 105 0.98304 [6] 1092 31 1.96608 [7] 2590 13 7.86432 [9]

24 294 115 0.49152 [5] 1050 32 1.96608 [7] 2590 13 3.93216 [8]

28 280 120 0.49152 [5] 1050 32 1.96608 [7] 2590 13 3.93216 [8]

32 −§ − − 1050 32 0.98304 [6] 2590 13 3.93216 [8]

40 −§ − − 1022 33 0.98304 [6] 2590 13 1.96608 [7]† Tdts and BInwk are expressed in backo slots and seconds, respectively‡ Corresponding beacon order BO§ Nmax equals to 28 in Application 1

Appendix D.3 for computation). Along with these parameter values, three dier-

ent delay constraints (i.e., 0.25 s, 0.5 s, and 1.0 s) are considered. Consequently,

the hybrid protocol is simulated for three monitoring applications: Application 1

( d = 0.25 s, m = 2400), Application 2 (d = 0.5 s, m = 2400), and Application

3 (d = 1.0 s, m = 2400). Using simulations, the maximum number of sensor

nodes Nmax that is allowed to form a network for each of the above applications

are found as 28, 52, and 122, respectively. DTS-scheduling parameters Tdts, ndts,

and BInwk of various networks deployed in these monitoring applications are then

obtained, and they are summarised in Table 6.3.

The parameter values in Table 6.3 lead to the following observations on the

behaviour of the hybrid protocol:

• Tdts shrinks with decreasing d. This decrement, which occurs to facilitate

the tightened delay bound in ED data transmission, conforms with Inequal-

ity (6.6). Consequently, ndts increases, and BInwk decreases with stricter

delay requirements,

• BInwk decreases with increasing N . This observation conforms with In-

equality (6.10),


• Tdts decreases with increasing N . This decrement, which occurs to accom-

modate the increased Drnd with rising N , conforms with Equations (6.5)

and (6.4). However, for larger Tdts, increments in Drnd may not be signif-

icant enough to make an impact on relevant ndts value (Equations (6.5)

(6.7)). Thus, in such situations, neither ndts nor Tdts changes with N as

seen in the Application 3.

These observations along with the parameter values in Table 6.3 provide a proof-

of-concept for the proposed hybrid protocol by demonstrating its ability to con-

struct dierent DTS schedules based on the network size and application require-

ments.

Next, the performance of the hybrid protocol in the aforementioned moni-

toring applications is investigated. With each monitoring application, three dif-

ferent event detection rates EDrates 1: one frame/min, 10 frames/min, and 60

frames/min; are considered. Two additional m values (i.e., 1200 and 4800) are

also employed for some simulations. Similar to the previous experiments the per-

formance of the protocol is compared with that of the standard IEEE 802.15.4

MAC protocol. In simulations related to the standard protocol, LTPM data

frames are transmitted at continuous bit rates (CBRs) throughout the reporting

cycle while ED data frames are transmitted synchronously at their detection rates.

On the other hand, in simulations related to the hybrid protocol, LTPM data are

transmitted using the DTS mechanism while the randomly-delayed CSMA/CA

mechanism is deployed to transmit ED data frames. Each simulation trial in this

investigation runs for a 500 Trpt duration.

The reliability of data transmission R of dierent networks deployed in the

Application 3 2 (i.e., the one with d = 1.0 s) is shown in Figure 6.8. As expected,

1Since extreme events are rare, it is dicult to study their impact on the performance ofthe protocol. Thus, they are assumed to be regular and frequent during this investigation.

2According to simulations, the reliability and power consumption of networks deployed inall three applications show similar trends respectively. Therefore, the results obtained only forthe Application 3 are presented here to avoid the repetition.


(a) LTPM data transmission (m = 2400, Trpt = 30 min, d = 1.0 s)

(b) ED data transmission (EDrate = 60 frms./min, Trpt = 30min, d = 1.0 s)

Figure 6.8: Data transmission reliability of hybrid protocol: (a) LTPM datatransmission with varying EDrate (b) ED data transmission with varying m.

neither ED data nor LTPM data are delivered reliably in networks with the

standard IEEE 802.15.4 protocol. Furthermore, in such networks a rise in one

type of data transmission will adversely aect the transmission reliability of the

other type of data as shown in Figures 6.8(a) and (b). This is because the standard


protocol uses the same MAC mechanism simultaneously for both LTPM and ED

data transmissions. In contrast, the networks with the hybrid protocol, which use

two separate MAC mechanisms for LTPM and ED data, always achieve a near

ideal level of reliability (R ≈ 100) for both types of data transmissions regardless

of network size and monitoring-application requirements.

Figure 6.9: Power consumption of hybrid protocol (m = 2400).

Per-bit-energy costs1 of both hybrid and standard protocols are illustrated in

Figure 6.9. For all network sizes and ED rates considered, the hybrid protocol

consumes signicantly less amount of power compared with the standard pro-

tocol. The improved energy performance of the hybrid protocol can be mainly

attributed to the energy eciency of the DTS mechanism in LTPM data transmis-

sion (See Figure 6.6). Moreover, the hybrid protocol shows only an insignicant

increment in the per-bit energy cost with rising event detections, in contrast to the

standard protocol's notably escalating power consumption under the same condi-

tions. This is because the hybrid protocol relax the medium-access-contention in

ED data transmission by introducing random delays appropriately. Even though1Unlike reliability, the energy consumption on ED and LTPM data transmissions can not

be determined separately (in particular for the standard protocol). Thus, `per-bit energy cost'of the overall transmission process is considered here.


(a) d = 0.25 s (b) d = 0.5 s

(c) d = 1.0 s

Figure 6.10: Maximum delay in ED data transmission.


random delays escalate with increased network size causing longer backing o

durations, their impact on the per-bit-energy cost is trivial as shown in the closet

in Figure 6.9. Therefore, sensor nodes conformed with the hybrid protocol in

a given monitoring application consume all most a constant amount of power

regardless of the network size N . This fact highlights the energy stability of the

proposed hybrid protocol.

The delay characteristics of the proposed hybrid protocol is investigated next.

To this end, the maximum delay in ED data transmission of dierent networks

deployed in all three monitoring applications has been obtained, and the results

are illustrated in Figure 6.10. For all considered monitoring applications and

network sizes, the hybrid protocol is able to transmit ED data within the delay

constraint imposed by the monitoring application. The protocol achieves this

by setting an upper bound that is less than or equal to the delay constraint

d. According to Equation (6.6), this upper bound can be quantied as Tdts +

tbcn + Drnd + Dcsma. In each network considered, the maximum delay in ED

data transmission varies in between the upper bound and the respective Tdts

value as shown in Figure 6.10. If the upper bound tends to exceed d due to

increasing network size, the protocol automatically adjusts it by lowering the

DTS length. This reduction may cause subsequent decrements in the maximum

transmission delay (Figures 6.10(a) and (b)). On the other hand, for a given DTS

length the maximum delay increases with the network size N due to rising Drnd

(Figure 6.10(c)).

6.5.4 Experiment 4 - Network Scalability of the Hybrid

Protocol

The proposed hybrid protocol controls the number of nodes allowed to form a

sensor network for a given hybrid monitoring application (See Section 6.4.1).

Although this control is needed to satisfy the requirements of the underlying


application, it may aect the size/scale of the sensor network. Therefore, to in-

vestigate the network scalability of the hybrid protocol, this experiment examines

the maximum number of nodes Nmax that is allowed to form a network under

dierent application requirements. For this purpose, a network-coordinator-node

developed in the simulation environment was initialised repeatedly with dierent

values of the application specic parameters Trpt, m, and d. During theses sim-

ulations, the parameters listed in Table 6.1 were applied. In addition, the frame

length parameters Lltpm and Led were set to 12 and 3 backo slots, respectively.

At the end of the each initialisation trial, the Nmax value generated at the network

coordinator is observed.

As shown in Figures 6.11(a) and 6.11(b), a higher network scalability can

be achieved either by relaxing the delay constraint d of the ED application or

by decreasing the amount of LTPM data m generated or by increasing the re-

porting cycle Trpt. This observation has been mathematically represented by

Equation (6.9) in which Nmax ∝ Trpt and Nmax ∝ 1/ndts [note: ndts ∝ m and

ndts ∝ 1/d according to (6.7) and (6.5)]. Moreover, it is worthwhile to observe

that Nmax for a given Trpt reaches to an upper limit as m decreases and d relaxes

(See the inner-right corner of Figures 6.11(a.2) and (b.2)). The upper limit of

Nmax is achieved when the parameters m and d are relaxed to certain values such

that all m frames would be able to be transmitted within a single DTS (i.e.,

ndts = 1) and BInwk would reaches to its minimum possible value. However,

it should be noted that even this minimum value of BInwk satises the mini-

mum CAP requirement presented in Section 6.4 to enable delay bounded ED

data transmission. In contrast, there is no such minimum CAP requirement in

networks deployed entirely in LTPM applications. Therefore, a higher network

scalability can be achieved in such networks than those deployed in hybrid mon-

itoring applications with equivalent LTPM requirements. For comparison, the

Nmax−ltpm values obtained for the same set of LTPM requirements have been

depicted in Figures 6.11(a.1) and (b.1).


(a) Reporting cycle, Trpt = 1 min.

(b) Reporting cycle, Trpt = 5 min.

Figure 6.11: Network scalability of hybrid protocol (in terms of the maximumnumber of nodes allowed to form the network Nmax).


When the monitoring requirements become strict, Nmax of the proposed hy-

brid protocol decreases. However, the protocol is capable of forming a network

(i.e., Nmax > 0) even for monitoring applications with extreme requirements as

illustrated in Figure 6.11(a) (For instance, the protocol forms a network with 5

nodes for a monitoring application where m, Trpt, d, Lltpm, and Led are equal to

1000 frames, 1 minute, 0.25s, 12 backos, and 3 backos, respectively). Never-

theless, sometimes the network size Nmax determined by the hybrid protocol may

not be sucient to cover the entire monitoring area of a given application with

extreme requirements. For such situations, it is suggested to deploy a cluster of

mini-networks where data transmission within each mini-network is governed by

the proposed hybrid protocol.

Research outcomes of this chapter have been either published or under re-

viewed in [219][224]-[226].

6.6 Conclusion

The IEEE 802.15.4 MAC standard appears to have shortcomings in delivering

a reliable and energy ecient data transmission for WSNs deployed in delay

bounded hybrid monitoring applications. By addressing the shortcomings of

the standard protocol, an IEEE 802.15.4 compliant new MAC mechanism is

developed for such hybrid monitoring WSNs. The new MAC protocol deploys

two dierent medium access mechanisms, namely DTS mechanism and random-

delayed CSMA/CA mechanism, to discretely meet the unique data-transmission-

requirements associated with each monitoring application (i.e., long-term periodic

and event detection). Furthermore, the proposed protocol comprehensively ad-

dresses network administrative tasks including network initialisation, new node

association, and existing node disassociation. The new hybrid protocol is built on

an ns-2 simulation environment, and then a series of simulation based experiments

are carried out to investigate its performance. Simulation results demonstrate


that the new protocol not only outperforms the standard IEEE 802.15.4 protocol

(in terms of reliability and power consumption of data transmission) but also

provides a reliable, energy ecient, and delay bounded data transmission mech-

anism for hybrid monitoring applications with dierent QoS requirements. Since

the new protocol does not alter any of the IEEE 802.15.4 PHY layer functionali-

ties, it can be easily implemented in commercial-o-the-shelf (COTS) platforms

after introducing a few software modications to the existing IEEE 802.15.4 ra-

dios.

Chapter 7

Conclusion

7.1 Summary and Conclusion

This thesis investigated performance of the IEEE 802.15.4 Medium Access Control

(MAC) standard under dierent operational conditions and application scenarios.

The major investigations and their outcomes can be summarised as follows:

• The beacon-enabled IEEE 802.15.4 MAC protocol with acknowledgment

(ACK) frame transmissions is analysed using a Discrete Time Markov Chain

(DTMC) model [150][151]. This analytical model is then used to derive the

performance of the protocol in terms of its throughput, power consumption

and data transmission reliability. Using the proposed analysis, impact of

the network and MAC-layer parameters on the performance of the protocol

is investigated [153]. The protocol's performance is further examined un-

der erroneous channel conditions by extending the proposed model [152].

Altogether, these investigations provide a generalised platform to analyse

the performance of the beacon-enabled IEEE 802.15.4 MAC protocol with

ACK frame transmission.

• Operational beahviour of the non-beacon-enabled IEEE 802.15.4 MAC pro-

tocol is studied with particular emphasis on possible contention scenarios

7. CONCLUSION 179

in data transmission. Based on a time discretisation approximation, the

non-beacon-enabled protocol is modelled using a DTMC [173][174]. The

performance of the protocol - in terms of throughput, power consumption

and data transmission reliability - is evaluated, and it is compared and

contrasted with that of the beacon-enabled protocol. The proposed model

provides a new analytical tool to evaluate the performance of the non-

beacon-enabled protocol both with and without ACK frame transmissions.

It has an added advantage of general applicability to wide range of network

congurations with dierent network and MAC-layer parameter values.

• A DTMC based analysis is proposed to investigate the performance of the

IEEE 802.15.4 MAC protocol in the presence of hidden nodes [193][194].

The proposed analysis, which models concurrent data transmissions oc-

curred in IEEE 802.15.4 based networks with hidden nodes, is used to derive

the aggregate network throughput by grouping nodes into non-overlapping

carrier sensing ranges. Using the proposed model, the impact of the various

network parameters on the aggregate throughput is examined, and then sev-

eral recommendations are discussed to improve the performance. Based on

experimental ndings, the node grouping condition of the analysis is relaxed

to propose a simple technique that approximates the throughput of generic

(i.e., without node grouping) IEEE 802.15.4 based networks with hidden

nodes. This investigation provides a basis for future studies on analysing

the inuence of hidden nodes on the performance of IEEE 802.15.4 based

networks.

• The necessity of having hybrid monitoring scenarios in WSNs is identied

in the context of multifaceted monitoring applications emerged recently

[224]. Quality of Service (QoS) requirements of data transmissions associ-

ated with dierent monitoring scenarios (in particular long term periodic

monitoring (LTPM) and event detection (ED)) are presented to show the

7. CONCLUSION 180

incapacity of the current IEEE 802.15.4 standard in such hybrid monitoring

WSNs. Two distinct MAC mechanisms - known as DTS mechanism and

randomly-delayed CSMA/CA mechanism [219] - are proposed to meet the

dierent data-transmission requirements posed by LTPM and ED applica-

tions. By carefully merging these two mechanisms, a hybrid MAC protocol

is developed on top of the existing IEEE 802.15.4 MAC standard [225][226].

The new protocol provides a reliable, energy ecient, and delay bounded

data transmission mechanism for hybrid monitoring WSNs with dierent

data-transmission requirements.

7.2 Future Work

This section provides recommendations for possible directions for future research

on the subject of this thesis.

• WSNs operate generally in hostile environments where data transmission

may frequently aect by channel errors. Therefore, a natural extension to

the study presented in Chapter 4 would be to analyse the performance of

non-beacon-enabled IEEE 802.15.4 MAC protocol under erroneous chan-

nel conditions. The error channel model developed in Chapter 3 possibly

provides a good basis for such an extension.

• Owing to the fact that buering of data frames may be required in some

WSNs, the analytical models presented in Chapter 3 and 4 could be ex-

tended to investigate the performance of the IEEE 802.15.4 MAC protocol

(both beacon-enabled and non-beacon-enabled) under the inuence of data

frame buering.

• Even in the presence of hidden nodes, MAC level acknowledgements might

provide a robust mechanism for reliable data transmission and thereby im-

prove the network throughput. Thus, investigating the throughput of IEEE

7. CONCLUSION 181

802.15.4 based networks that deployed ACK frame transmission in the pres-

ence of hidden nodes could be a possible extension to the analysis presented

in Chapter 5.

• Another potential improvement to the work presented in Chapters 4 and 5

includes developing an analytical model to evaluate the throughput of the

non-beacon-enabled IEEE 802.15.4 MAC protocol in the presence of hidden

nodes.

• The proposed hybrid protocol was developed for single-hop transmissions

in hybrid monitoring WSNs. Extending this protocol for multi-hop trans-

missions would enable large scale WSNs to meet the stingiest requirements

posed by underlying hybrid monitoring applications.

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[224] S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, Wireless SensorNetworks for Structural Health Monitoring: Considerations for Communi-cation Protocol Design, in Proc. of the IEEE 17th International Conferenceon Telecommunications (ICT), Apr. 2010, pp. 694699. 176, 179

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http://ns2wpan.blogspot.com.au/2013/11/simulating-ieee802154-networks.html

Appendix A

Steady State Transition Equations

of Node Model DTMCs

A.1 Beacon-enabled IEEE 802.15.4 - Analytical

Model

The steady state probabilities of Markov chain model of node (Figure 3.1) can beobtained by solving the balance equations shown in (A.1.1), where w1 = 2macMinBE

and wy = 2BEy . BEy represents the backo exponent of yth backo stage. Thenotationπ(statei) represents the long term proportion of transitions into statei.The normalised condition of the DTMC is presented in (A.1.2).

π(idle) = (1− p)π(idle) + (1− pci)∑x

x=1 π(csxy0) + (1− pci|i)∑x

x=1 π(csxy(−1))

+q∑x−1

x=1 π(ackx) + π(ackx)


where (g, h) =

(0, 1) 1 ≤ z ≤ 2; (4, 1) z = 3

(1, 1) 4 ≤ z < w1 − 1; (1, 0) z = w1 − 1

APPENDIX 205

π(box1z) = (1− q)π(ackx−1)/w1 + hπ(box1(z+1))

where 2 ≤ x ≤ x; h =

1 1 ≤ z < w1 − 1

0 z = w1 − 1

π(boxyz) = [(1− pci)π(csx(y−1)0) + (1− pci|i)π(csx(y−1)(−1))]/wy + hπ(boxy(z+1))

where 1 ≤ x ≤ x; 2 ≤ y ≤ y; h =

1 1 ≤ z < wy − 1

0 z = wy − 1

π(csxy0) = g(1− q)π(ackx−1) + h[(1− pci)π(csx(y−1)0)

+(1− pci|i)π(csx(y−1)(−1))]/wy + π(boxy1)

where (g, h) =

(0, 0) x = 1; y = 1

(1, 0) 2 ≤ x ≤ x; y = 1

(0, 1) 1 ≤ x ≤ x; 2 ≤ y ≤ y

π(csxy(−1)) = pciπ(csxy0) where 1 ≤ x ≤ x; 1 ≤ y ≤ y

π(txx) = pci|i∑y

y=1 π(csxy(−1)) where 1 ≤ x ≤ x

π(ackx) = π(txx) where 1 ≤ x ≤ x(A.1.1)

π(idle)+x∑x=1

y∑y=1

wy−1∑z=1

π(boxyz)+x∑x=1

y∑y=1

0∑z=−1

π(csxyz)+x∑x=1

π(txx)+x∑x=1

π(ackx) = 1

(A.1.2)

APPENDIX 206

A.2 Beacon-enabled IEEE 802.15.4 - Simplied Model

The steady state probabilities and the normalisation condition of Markov chainmodel of node (Figure 3.3) can be obtained as

π(idle) = (1− p)π(idle) + (1− pci)π(csy1) + (1− pci|i)π(csy2) + qπ(ack)

π(bo1) = (1− pn1 )[pπ(idle) + π(bo1) + (1− q)π(ack)]

π(cs11) = pn1 [pπ(idle) + π(bo1) + (1− q)π(ack)]

π(cs12) = pciπ(cs11)

π(boy) = (1− pny )[(1− pci)π(cs(y−1)1) + (1− pci|i)π(cs(y−1)2) + π(boy)] ; 2 ≤ y ≤ y

π(csy1) = pny [(1− pci)π(cs(y−1)1) + (1− pci|i)π(cs(y−1)2) + π(boy)] ; 2 ≤ y ≤ y

π(csy2) = pciπ(csy1) ; 2 ≤ y ≤ y

π(ack) = π(tx)

(A.2.3)

π(idle) +

y∑y=1

π(boy) +

y∑y=1

2∑z=1

π(csyz) + π(tx) + π(ack) = 1 (A.2.4)

APPENDIX 207

A.3 Non-beacon-enabled IEEE 802.15.4 with ACK

Transmission

The steady state transition equations of the Markov chain model of node in Fig-ure 4.5 and the relevant normalisation condition can be obtained as follows. Thenotation π(statei) represents the long term proportion of transitions into statei,and w1 = 2macMinBE, wy = 2BEy where BEy represents the backo exponent ofyth backo stage.

π(idle) = (1− p)π(idle) + (1− pci)∑x

x=1 π(csxy) + q∑x−1

x=1 π(ackx) + π(ackx)


where (g, h) =

(0, 1) 1 ≤ z ≤ 2; (4, 1) z = 3

(1, 1) 4 ≤ z < w1 − 1; (1, 0) z = w1 − 1

π(box1z) = (1− q)π(ackx−1)/w1 + hπ(box1(z+1))

where 2 ≤ x ≤ x; h =

1 1 ≤ z < w1 − 1

0 z = w1 − 1

π(boxyz) = [(1− pci)π(csx(y−1))]/wy + hπ(boxy(z+1))

where 1 ≤ x ≤ x; 2 ≤ y ≤ y; h =

1 1 ≤ z < wy − 1

0 z = wy − 1

π(csxy) = [g(1− q)π(ackx−1) + h(1− pci)π(csx(y−1))]/wy + π(boxy1)

where (g, h) =

(0, 0) x = 1; y = 1

(1, 0) 2 ≤ x ≤ x; y = 1

(0, 1) 1 ≤ x ≤ x; 2 ≤ y ≤ y

π(tax) = pci∑y

y=1 π(csxy) where 1 ≤ x ≤ x

π(ackx) = π(txx) = π(tax) where 1 ≤ x ≤ x

Appendix B

Fractions of Time Spent by a Node

in Dierent Transceiver Activities

B.1 Analysis of Beacon-enabled IEEE 802.15.4

The fraction of time spent by a node in dierent transceiver activities in eachanalytical models are listed in Table B.2, where

T = 1 + (L− 1)x∑x=1

π(txx) + (Lack + sdack − 1)x∑x=1

π(ackx)(B.1.1)

Tsim = 1 + (L− 1)π(tx) + (Lack + sdack − 1)π(ack). (B.1.2)

Since the analytical model presented in Section 3.3 and the extended model pre-sented in Section 3.5 share the same node model, corresponding fractions of timespent by a node in these models are listed under the same column. In TableB.2, tbcn, fbcn and p represent the beacon duration in terms of backo slots, fre-quency of beacon reception and frame arrival rate per backo slot, respectively.fbcn = 1/BI where BI is the beacon interval in terms of backo slots.

APPENDIX 209

Table B.1: Fractions of time spent by a node in dierent transceiver activities inbeacon-enabled networks.

Parameter Analytical model Simplied modelExtended model

pnbcn tbcn.fbcn tbcn.fbcnpnsi 3 (fbcn + p) 3 (fbcn + p)

pnir 0.6(fbcn +

[∑xx=1

∑yy=1 π(csxy0)

]/T)

0.6(fbcn +

∑yy=1 π(csy1)/Tsim

)pni π(idle)/T π(idle)/Tsimpnbo

∑xx=1

∑yy=1

∑wy−1z=1 π(boxyz)/T

∑yy=1 π(boy)/Tsim

pncs∑x

x=1

∑yy=1

∑0z=−1 π(csxyz)/T

∑yy=1

∑2z=1 π(csyz)/Tsim

pnack (Lack + sdack)∑x

x=1 π(ackx)/T (Lack + sdack)π(ack)/Tsimpntx L

∑xx=1 π(txx)/T Lπ(tx)/Tsim

B.2 Analysis of Non-beacon-enabled IEEE 802.15.4

Table B.2 lists the fractions of time spent by a node in dierent transceiver activ-

ities in both analytical models (i.e., with and without ACKs). The parameters p,

L, and Lack represent the frame arrival rate per mini slot, data frame length and

ACK frame length in backo slots, respectively. Λ and Λack are given in (B.2.1)

and (B.2.2).

Table B.2: Fractions of time spent by a node in dierent transceiver activities innon-beacon enabled networks.

Parameter Model without ACKs Model with ACKspnsi (3× 20)p (3× 20)p

pnir 12∑y

y=1 π(csy)/Λ 12∑x

x=1

∑yy=1 π(csxy)/Λack

pnrt 12π(ta)/Λ 12∑x

x=1 π(ta)/Λack

pni π(idle)/Λ π(idle)/Λack

pnbo 20∑y

y=1

∑wy−1z=1 π(boyz)/Λ 20

∑xx=1

∑yy=1

∑wy−1z=1 π(boxyz)/Λack

pncs 8∑y

y=1 π(csy)/Λ 8∑x

x=1

∑yy=1 π(csxy)/Λack

pnack - (12 + Lack)∑x

x=1 π(ackx)/Λack

pntx (20× L)∑x

x=1 π(txx)/Λ (20× L)∑x

x=1 π(txx)/Λack

APPENDIX 210

Λ = π(idle) + 20

y∑y=1

wy−1∑z=1

π(boyz) + 8

y∑y=1

π(csy) + 12π(ta)

+20Lπ(tx). (B.2.1)

Λack = π(idle) +x∑x=1

[20

y∑y=1

wy−1∑z=1

π(boxyz) + 8

y∑y=1

π(csy) + 12π(tax)

+20Lπ(txx) + (12 + 20Lack)π(ackx)

]. (B.2.2)

Appendix C

Modications to ns-2.34 Simulator

During the course of this dissertation, several modications were introduced to

ns-2.34 simulator to model IEEE 802.15.4 based networks accurately. The mod-

ications come under two main streams:

1. Modications to CCA Procedure

2. Modications to implement the new hybrid protocol.

Above modications (including source codes) have been published on [227], and

they can be summarised as below.

C.1 Modications to CCA Procedure

According to the IEEE 802.15.4 standard, PHY layer of a node should perform

a CCA continuously for eight symbol durations upon receiving a CCA request

from the MAC layer. However, in the ns-2 simulator, CCA procedure has been

implemented as an event of a single time instant rather than an event of successive

eight symbol durations as specied in the standard. More specically, in the

current release of the simulator (i.e., ns-2.34), the PHY layer of a node performs

the CCA at the boundary of the fourth symbol duration after receiving a CCA

request, and the outcome of that CCA is informed to the MAC layer at the end

APPENDIX 212

Figure C.1: Timing of CCA procedure in dierent scenarios.

of the eighth symbol duration. Although this implementation diers largely from

the standard, it produces accurate CCA outcomes for the beacon enabled IEEE

802.15.4 networks whose frame transmissions last for integer multiples of backo

slots. In such networks, the beginning and ending of frame transmissions and

the beginning of CCAs occur only at the boundaries of backo slots. Thus, the

outcome of a CCA performed at the fourth symbol after a backo slot boundary

is similar to that of a CCA performed continuously for the rst eight symbols of

a backo slot.

Nevertheless, the current CCA implementation in ns-2 simulator fails to pro-

duce accurate CCA outcomes in the non-beacon enabled networks in which frame

transmissions may begin and end at any time instant. For example, if a node's

transmission ends during the rst four symbols or begins during the last four

symbols of another node's CCA procedure, the current implementation of CCA

indicates idle channel while in reality it should not.

APPENDIX 213

Therefore, a modication to the current CCA implementation in ns-2 sim-

ulator is proposed to generate accurate CCA outcomes in non-beacon enabled

networks. In the modied implementation, a CCA is performed in two phases:

at the middle of the rst and eighth symbol durations. If the channel is found

busy during the rst phase (i.e., in the rst symbol) the PHY layer indicates

a `busy channel' massage at the end of eighth symbol duration. Otherwise, the

PHY layer performs the second phase of CCA (i.e.,in the eighth symbol) and

reports the outcome of that phase as the nal outcome at the end of eighth sym-

bol duration. Since all transmissions in IEEE 802.15.4 networks last for more

than eight symbol durations, the modied CCA implementation produces ac-

curate CCA outcomes by sensing the channel at the beginning and the ending

of a CCA duration. The timing diagrams shown in Figure C.1 summarises the

aforementioned scenarios. The proposed modication to ns-2 simulator has been

implemented by modifying the existing CCA timer and handler (i.e, CSH and

CSHandler in ./wpan/p802_15_4phy.cc and ./wpan/p802_15_4phy.h les) to

enable the rst phase of CCA and by adding a new timer and handler (i.e, CSH2

and CSHandler2) to enable the second phase of CCA.

C.2 Implementation of Hybrid Protocol

The hybrid protocol presented in Chapter 6 is implemented in ns-2 simulator by

developing new software modules and modifying the existing ones. The modica-

tions are summerised in Table C.1. To simulate the complete protocol, the new

and modied modules are linked to all the other software modules in the ns-2

implementation of the IEEE 802.15.4 at ./ns-2.34/wpan/.

APPENDIX 214

Table C.1: Impelemetation of hybrid protocol in ns-2 .

New modules

p802_15_4sscs_hybrid_init.hp802_15_4sscs_hybrid_init.cc

Functionalities :coordinator node:

initialise the coordinator node with initialising-parameters Led, Lltpm, and m

calculate Nmax, BImin−nwkstart and handle the countdown timer (Tinit)

call p802_15_4mac.cc to set BI in MAC-PIB database to BImin−nwkand to transmit beacons for association purposes

maintain a counter to get the number of associated nodes

manage sensor nodes association at the initial phase

sensor nodes:call p802_15_4mac.cc to listen the beacons

call p802_15_4mac.cc to transmit association request

get association conrmation from p802_15_4mac.cc

call p802_15_4mac.cc to set the variable association_number

turn o the radio transceiver after a successful association, until the expiration

of countdown timer or beginning of the next superbeacon transmission

(depending on the network's operational phase)

p802_15_4sscs_dts_schedule.hp802_15_4sscs_dts_schedule.cc


calculate the DTS schedule parameters (i.e., Tdts, ndts, and BInwk)

for a network with N nodes

initialise the DTS schedule parameters in MAC-PIB database

by calling p802_15_4mac.cc

call p802_15_4mac.cc to transmit superbeacons

start and handle a timer for superbeacon transmission

maintain a disassociation-counter for each associated node

handle node disassociation

continued on next page...

APPENDIX 215

handle association requests from the new nodes

(while the network is in the steady phase)

calculate new DTS schedule parameter values for the network with the new N

call p802_15_4mac.cc to update new DTS schedule parameter values

in MAC-PIB database after each new association or disassociation

sensor nodes:call p802_15_4mac.cc to reset all current rx-tx operations and to reset

the variable association_number, if received a disassociation conrm

call p802_15_4sscs_hybrid_init.cc to reassociate with the network

Modied modules

p802_15_4mac.hp802_15_4mac.cc


transmit beacons (with modied payloads) and superbeacons

update the DTS schedule parameters in MAC-PIB database

sensor nodes:receive beacons (with modied payloads) and superbeacons

update the DTS schedule parameters in MAC-PIB database

start and handle a timer to locate the start of DTS in each superframe

maintain a counter to nd the associated DTS within the transmission cycle

handle data transmission within DTSs

transmit dummy frames during the associated DTSs when no data to transmit

send disassociation conrm command to p802_15_4mac.cc,

if the node's association number is found in Disassociated Nodes eld

set and reset the variable association_number

call p802_15_4_csma.cc to transmit data if the data transmission within

node's DTS is not acknowledged by the immediate beacon

call p802_15_4_csma.cc to transmit ED data

set an identier for ED data frames

continued on next page...

APPENDIX 216

p802_15_4csma.hp802_15_4csma.cc

Functionalities :sensor node:

introduce a random delay before starting the csma/ca procedure for ED data

freeze the backo procedure (including random delays) during DTSs

seize all CSMA/CA trac during the DTS of each superframe

p802_15_4timer.hp802_15_4timer.cc

Functionalities :dene timers for

- countdown timer (Tinit),

- superbeacon transmission,

- locating the start of the DTS in each superframe

p802_15_4eld.hp802_15_4paket.h

Functionalities :make changes in the payload of beacon and superbeacon frames

Appendix D

Supportive Calculations and

Algorithm Descriptions

D.1 Quantifying δ

The proportional constant δ in Equation (6.1) in Chapter 6 can be found by

matching the data transmission reliability of networks having randomly-delayed

synchronised data arrivals with that of equivalent networks having Poisson data

arrivals. To quantify δ, three dierent simulations were performed for IEEE

802.15.4 based networks with these two dierent types of frame arrivals (i.e.,

randomly-delayed synchronised arrivals and Poisson arrivals) . The rst simula-

tion was conducted for dierent network sizes ranging from 4 to 80 with a xed

frame length (L = 10), while the second simulation was performed for dierent

frame lengths with a xed network size (N = 40). The frame arrival rate λ

was xed to 1 frame/s for all networks during rst two simulations. Finally, the

third simulation considered dierent frame arrival rates with xed network size

(N = 40) and xed frame length (L = 10). For simulations, 13 dierent Drnd

values were applied for each network with randomly-delayed synchronised data by

varying δ from 0.1 to 3. Furthermore, 2.4 GHz PHY layer and default values for

APPENDIX 218

all MAC layer parameters were assumed for simulations. ACK frame transmis-

sion and frame retransmissions were deployed in each simulation to minimise the

number of frames dropped due to collisions. Each simulation trial was run until

each node transmits 20,000 frames, and results were averaged for 10 simulation

trials.

Data transmission reliability of each network set-up was evaluated using the

performance index R (reliability factor), which represents the ratio between the

number of frames successfully received by the coordinator and the total number of

frames generated by all sensor nodes. To simplify the comparison, the normalised

dierence of R (i.e., ∆R) is dened as

∆R =

[Rsynchronised −RPoisson

]× 100%

RPoisson(D.1.1)

where Rsynchronised and RPoisson denote the reliability factors of the network

with randomly-delayed synchronised data and the equivalent network with Poisson

arrivals, respectively. Figure D.1 depicts ∆R with varying δ in dierent network

set-ups.

According to simulations, ∆R ≈ 0 when δ ≥ 1.0 for all network congurations

and frame arrival rates considered1. Therefore, by applying a random delay where

δ ≥ 1.0, the data transmission reliability of the IEEE 802.15.4 based networks

with synchronised data arrivals can be improved to be matched with the reliability

of equivalent networks having Poisson data arrivals.1Note: The chosen values for L, N , and frame arrival rate almost represent the entire

possible range for these parameters in the context of the reference hybrid monitoring WSN. Forinstance, it is not possible to go beyond 5 frames/s arrival rate while satisfying D < Ted as themaximum transmission delay of the standard CSMA/CA protocol with the default settings isequal to 0.17664s (Equation 6.3)

APPENDIX 219

(a) ∆R vs δ for dierent number of nodes N (L = 10 backos, frame arrivalrate = 1 frames/s)

(b) ∆R vs δ for dierent frame lengths L, (N = 40 and frame arrival rate= 1 frames/s)

(c) ∆R vs δ for dierent frame arrival rates λ, (N = 10 and L = 10backos)

Figure D.1: Normalised dierence in data transmission reliability ∆R with vary-ing δ

APPENDIX 220

D.2 Algorithms for Networks Deployed Only in

LTPM Applications

When a sensor network deployed entirely for the long term periodic monitoring, it

can operate only with the DTS mechanism. Therefore, all the parameters related

to the DTS scheduling in such networks (i.e., Nmax−ltpm, ndts−ltpm, BInwk−ltpm,

and Tdts−ltpm) can be completely determined using the monitoring requirements

m and Trpt of the underlying LTPM application as follows:

Nmax−ltpm: At the beginning of network initialisation, the network coordinator

determines (Nmax−ltpm) to be the maximum value of Nmax that validates the

following inequality.

Nmax ≤⌊

Trptκ(BImin − tbcn)

⌋(D.2.1)

where BImin and tbcn represent the minimum possible BI value of the IEEE

802.15.4 standard1, which can be obtained by setting BO = 0, and transmission

duration of beacon frames (including Rx-to-Tx turnaround), respectively. The

coecient κ represents the number of superframes required to transmit m frames,

and it can be found as the minimum value that satises

κ ≥⌈

mtltpm(BImin − tbcn)

⌉(D.2.2)

where tltpm represents the transmission duration of single LTPM data frame in-

cluding IFS.

BInwk−ltpm and ndts : Once Nmax−ltpm is calculated, the network coordinator

starts the association process. If the number of nodes associated is equal to N ,1Selecting BImin for BI gives higher granularity in time domain which in turn yields higher

values for Nmax

APPENDIX 221

the following inequality is applied to nd ndts−ltpm, BInwk−ltpm.

mtltpm < ndts(BInwk − tbcn) <TrptN

(D.2.3)

where ndts ∈ N+ and BInwk belongs to one of the 15 possible BI values of the

IEEE 802.15.4 standard. To minimise the energy wastage on beacon listening,

BInwk−ltpm is set to the maximum value of BInwk that validates the above Inequal-

ity. Thus, ndts−ltpm is the minimum ndts that can be associated with BInwk−ltpm

in (D.2.3).

Tdts−ltpm: Finally, the duration of the DTS Tdts−ltpm can be obtained as

Tdts−ltpm =

⌈m

ndts−ltpm

⌉tltpm. (D.2.4)

It is worthwhile to notice that Tdts−ltpm < (BInwk−ltpm − tbcn). Therefore, there

exists an unoccupied duration of each superframe that can be utilised for future

communications in node-association and/or retransmission of corrupted LTPM

data.

After computing the above parameters, the network coordinator constructs

the DTS schedule and transmits the initial superbeacon to begin the steady phase.

During the steady phase, the network follows the same procedure described in

Chapter 6.

D.3 Computation of Initialising-Parameters

Initialising-parameters Led, Lltpm, and m of the hybrid protocol for the SHM

application given in Table 6.3 can be determined as follows:

Led: The payload of an ED data frame carries the strain data generated during

a sampling instance. As there are three measuring axes, this payload equals to

6 bytes. After adding PHY and MAC layer headers of 6 and 13 bytes to this

APPENDIX 222

payload, an ED data frames becomes 25 bytes in total length. Because a single

backo-slot corresponds to 10 bytes in 2.4 GHz band, the length of an ED data

frame Led can be given as 2.5 backo-slots. After rounding o, this eventually

becomes 3 backo-slots.

Lltpm: The payload of a LTPM data frame can carry multiple acceleration

measurements due to `Store now, transmit later' behaviour of the LTPM ap-

plication. Therefore, to minimise the protocol overhead associated with data

transmission, the payload of a LTPM data frame is assumed to be at its maxi-

mum possible value allowed by the IEEE 802.15.4 standard (i.e., 104 bytes). By

adding PHY/MAC layer headers and then performing a basic rounding o, the

length of a LTPM data frame Lltpm nally becomes 12 backo-slots.

m: The total amount of LTPM data generated during a reporting period Θ

can be given as

Θ = fs × Tm × naxes × ls (D.3.5)

where fs, Tm, naxes, and ls represent the sampling frequency, measuring period,

number of measuring axes, and sample size of the LTPM application, respectively.

By substituting the relevant values from Table 6.3 into D.3.5, Θ can be found

as 252000 bytes. After dividing this amount of data by the payload of a single

LTPM data frame (i.e., 104 bytes) and then performing necessary rounding o,

m would become 2400 frames.

performance analysis of ieee 802.15.4 based wireless

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