Analyzing the MAC Protocols for Wireless Sensor

Network for Energy Efficiency

SUMMARY

SUBMITTED TO

MATS UNIVERSITY, RAIPUR (C.G.)

FOR THE AWARD OF THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

COMPUTER SCIENCE ENGINEERING

By:

P. UDAYAKUMAR

Under the Supervision of

Dr. RANJANA VYAS

Reader (On leave)

MATS UNIVERSITY

Raipur

(2013)

Analyzing the MAC Protocols for Wireless Sensor

Network for Energy Efficiency

SUMMARY

SUBMITTED TO

MATS UNIVERSITY, RAIPUR (C.G.)

FOR THE AWARD OF THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

COMPUTER SCIENCE ENGINEERING

Submitted By

P.UDAYAKUMAR

Under the Supervision of

Dr. RANJANA VYAS

TABLE OF CONTENTS

Abstract I

Chapter 1: Introduction 1

1.1 Overview of WSN 1

1.2 WSN Architecture 1

1.3 Evaluation of WSN 3

1.4 Application of WSN 4

1.5 Medium Access Control Protocols 4

Chapter 2: Related Work 6

2.1 Overview of MAC 6

2.2 Attributes of good protocols 7

2.3 Routing mechanisms 9

2.4 WSN Architecture 11

Chapter 3: Thesis Objectives 12

3.1 Energy wastein WSN 12

3.2 Designing energy efficiency 13

3.3 Energy calculation in election phase 13

Chapter 4: Noteworthy Contribution in the Field of Proposed Work 15

4.1 MAC protocols for WSNs 15

4.2 Analysisof MAC Protocols 18

4.3 Basic synchronization procedure 21

Chapter 5: Design of Proposed Methodology 22

5.1 Related work 22

5.2 Comparative analysis of MAC protocols 24

5.3 Problems in existing protocol and motivation of proposed protocol 26

5.4 Architecture of proposed system 26

Chapter 6: Implementation: Results and Discussion 30

6.1 Comparison of contention-based protocols 30

6.2 Comparison of cluster-based protocols 32

Chapter 7: Conclusion and Future Work 36

References 37

Appendix 40

List of Papers Published by the Candidate 41

LIST OF TABLES

Table 1.1 Hardware features of sensor nodes 2

Table 1.2. Evolution of wireless sensor network 3

Table 1.3 Comparison of WSN protocols 5

Table 2.1 Battery life estimation of sensor nodes 8

Table 2.2 Power used by Tyndall nodes 11

Table 4.1 Radio Model 18

Table 4.2 Current consumption in receiving and sleeping mode 20

List of Figures

Fig. 1.1 Architecture of WSN node 1

Fig. 1.2 Applications of WSN 4

Fig. 2.1 Protocol stack 6

Fig. 2.2 Two tired heterogeneous network 11

Fig. 3.1 Overhearing Mechanism 12

Fig. 3.2 Hidden and Exposed node 12

Fig. 4.1 S-MAC protocol design 15

Fig. 4.2 T-MAC Protocol Design 16

Fig. 4.3 Time-slot structure of TRAMA 17

Fig. 4.4 BMAC low power listening 20

Fig. 5.1 Energy Consumption of existing protocols 25

Fig. 5.2 Latency of existing protocols 25

Fig. 5.3 Energy consumption vs. number of sources 26

Fig. 5.4 Architecture of ETB-MAC protocol 27

Fig. 5.5(a) Sending CTS and RTS 27

Fig. 5.5(b) Exchange of Data and ACK packets 28

Fig. 5.5(c) Sending data 28

Fig. 5.6(a) Proposed RTS format 28

Fig. 5.6(b) Proposed CTS frame format 28

Fig. 5.7 Data transmission of ETB-MAC 29

Fig. 6.1 Random rooting tree of 60 nodes 30

Fig. 6.2 Average delay when sensing range 50m 31

Fig. 6.3 Average delay with varying sensing range 31

Fig.6.4 Packet delivery ratios 32

Fig.6.5 Average delay vs. number of CBR sources 32

Fig.6.6 Energy consumption with varying radius 33

Fig.6.7 Energy consumption with varying distance 33

Fig. 6.8 Energy withvarying node number 33

Fig. 6.9 Total energy consumption vs. number of nodes 33

Fig. 6.10 Total energy in larger network 34

Fig. 6.11 Total energy in smaller network 34

Fig. 6.12 Total energy in with varying number of nodes 34

Fig. 6.13 Average network life time 34

Fig. 6.14 Network life time vs. transmission radius 35

Fig. 6.15 Network life time vs. number of nodes 35

The purpose of this research is to investigate and design a new energy efficient protocol for

wireless sensor networks. The reason for choosing the theme “Analyzing the MAC Protocols for

Wireless Sensor Network for Energy Efficiency” was to explore a relatively new concept in

which our new protocol performs better than the existing protocols.

The first chapter gives an introduction and the architecture for wireless sensor networks. The

introduction includes history of WSNs, design issues and applications. The Medium Access

Control (MAC) protocols and its classifications have been discussed in this chapter.Chapter two

gives a detailed overview of WSN routing protocols.This chapter also gives the description of

MAC layer protocols. It also includes thecomputation of energy, performance and quality of

service of the protocols.

The main objective of our work is described in chapter three and in the fourth chapter we have

analyzed the noteworthy contribution in the field of proposed work. We have also analyzed the

merits and the demerits of the existing protocols. To increase the efficiency we have designed a

new protocol in chapter five. A detailed architecture of the proposed protocol isalso defined in

this chapter.Chapter six presents the performance analysis of the proposed protocol, giving a

detailed comparative analysis of the same with the existing protocols.

The conclusion chapter summarizes the investigations performed and the results obtained. The

scope and the possible future investigations also indicated. Symbols and acronyms used in this

summary are summarized in Appendix A.

Key words: Energy efficiency, MAC, WSN

1

CHAPTER 1

INTRODUCTION

1.1 Overview of WSN

A wireless sensor network consists of thousands of inexpensive tiny nodes with low power, low

cost digital signal processors and radio frequency circuits [1],[2], each having sensing capability

with limited communication power. The prospect of the sensor networks is accelerated by

MEMS (Micro Electromechanical Systems) [3] and radio frequency systems. They comprise of a

radio transceiver, microcontroller, power supply and the actual sensor. The main challenge in

sensor networks is to maximize the lifespan of sensor nodes. Other objectives of WSNs are

reliability, accuracy, cost effectiveness, fairness and throughput and security.

1.2 WSN Architecture

A typical wireless sensor node consists of five main hardware modules as shown in Figure 1.1:

(i) Microcontroller (ii) Radio transceiver (iii) One or more sensors (iv) Memory chip (v) Battery.

The WSN nodes usually wake up on the timer interrupt. They may also have application

dependent additional components such as location finding system and a power generator [1].

Fig. 1.1 Architecture of WSN nodes

2

1.2.1 Sensing unit

A sensor is a hardware device that produces a measurable response in signal to a change in

physical condition such as temperature, pressure, humidity, etc. Sensors send detected values to

the processor which runs the sensor operating system and manages the procedures required to

carry out the given sensing task.

1.2.2 Storage unit

Memory in a sensor node includes in-chip flash memory and RAM of a microcontroller and

external flash memory. Flash is used for persistent storage of application code and text segments

and static random access memory (SRAM) is used for runtime data storage.

1.2.3 Transceiver unit

A transceiver is responsible for wireless communication of sensor node. The operational states of

transceiver are: transmit, receive, idle and sleep. The transceiver unit consists of a radio and

antenna. The radio contains different operating modes like transmit, receive, idle and sleep for

power management purposes. The sleep mode provides the lowest power consumption.

1.2.4 Power unit

The power entity is generally composed of a couple of standard AA batteries. Many research [2]

[4] [5] are being conducted to minimize energy consumption and increase the lifetime of sensors.

Power can be stored mainly in batteries or also in capacitors. As shown in Table 1.1 TelosB

consumes the least power, but very limited in terms of storage and processing. On the other

hand, IMote2 is the most powerful in terms of processing, but it consumes a lot of power.

Table 1.1 Hardware features of sensor nodes

MICAZ(Crossbow) TelosB IMOTE2

Processor (MHz) 16 8 13-416

RAM(Kb) 4 10 256

Flash(K B) 512 1024 32000

Active(ma) 48 25 >45

Idle(ma) 8 2 >30

Sleep(µa) 15 6 388

1.2.5 Processing unit

The processing unit consists of mainly of processor; the responsibilities include controlling

sensors, gathering and processing sensed data, executing WSN applications, managing

communication protocols with the help of operating system.

3

1.3 Evolution of Wireless Sensor Network

Department of computer science, Carnegie Mellon Univ. Pittsburgh, USA organized a first

international workshop on sensor [4] on Distributed Sensor Networks (DSN) in 1978, followed

by DARPA organized DSN workshop in 1990 [6]. In 2001 DARPA launched a research program

called SenseIT [7], meanwhile IEEE initiated low data rate wireless personal area network

implemented by standard IEEE802.15.4 [8]. The ZigBee Alliance [9] has published ZigBee

standard for high-level communication protocols for WSNs. Currently, the companies like

Crossbow Technology [10] and Dust networks [11] were also started for implementation of

WSNs. Today WSN developers can choose from several existing and well-developed operating

systems such as TinyOS [12]. Many research programs are still in progress [13]-[17]. The

evolution of wireless sensor networks is shown in Table 1.2.

Table1.2. Evolution of wireless sensor network

Year Authors / Organization Journal/Conf.

/Workshop

Title Remark

1978 Dept. of computer science,

Carnegie Mellon Univ.

Pittsburg, PA, USA.

Workshop Distributed Sensor Nets

(DSN)

First International

workshop on

Sensor

1994 C. E. Nishimura and D. M.

Conlon

Workshop Monitoring Whales and

Earthquakes using SOSUS

Real time

application

2001 A.Manjeshwar and

D.P.Agrawal

Symposium TEEN: a routing protocol for

enhanced efficiency in WSN

Parallel and

Distributed

processing WSNs.

2002 A. Perrig, R. Szewczyk,

J.D.Tygar, V. Wen, and D.

E.Culler

ACM Journal of

Wireless

Networks

SPINS [15]: security

protocols for sensor

networks

Protocol for

security.

2005 M.Connolly & F.O‟Reilly Workshop

REALWSN‟05

Sensor Networks And The

Food Industry

Application in

Food Industries

2008 L.M.Ni International

Conference

SUTC‟08

Ubiquitous and Trustworthy

Computing

For Ubiquitous

computing

2011 Mo Sha, G.Hackmann &

Chenyang Lu, WQOS‟11

IEEE

Conference

Multi-Channel Reliability &

Spectrum Usage in Real

Homes

Empirical Studies

for Home-Area

Sensor Networks

2012 A.Saifullah, Chengjie Wu,

P.B.Tiwari, Young Fu &

Chenyang Lu

Symposium

RTAS‟12

Near Optimal Rate Selection

for Wireless Control Systems

Real time

Embedded

Technology

2013 P. Gireesan Namboothiri,

Krishna M. Sivalingam

Wireless Netw

2013

Throughput Analysis of

multiple channel based

WSNs [17]

Throughput

analysis for large

scale network

4

1.4 Applications of WSN

Wireless sensor networks has many applications, some examples are listed below Figure 1.2.

Fig. 1.2 Applications of WSN

Wireless sensor networks consisting of tiny devices which monitor physical or environmental

conditions such as temperature, pressure, humidity and noise level, home automation, monitoring

physiological health, inventory control and underground mines [18][19].

1.5 Medium Access Control Protocols

Medium Access Control (MAC) protocols play an important role in accessing the channel in

wireless communication. There are many issues in MAC protocol design, such as hidden

terminal problem, collision, overhearing, QoS, etc. Energy is very important for sensor nodes

because all the sensors are battery powered. Some issues may consume more energy, such as

collision, duplication of data or idle listening. Throughput, fairness and end to end delay are

related to QoS in the MAC protocol design.

MAC protocols are classified into three categories: CSMA based protocols, TDMA protocols

and hybrid protocols. In CSMA based protocols, the sensor nodes periodically wake up, listen to

Wireless Sensor networks

Vehicle Monitoring

Medical Monitoring

Animal Monitoring Machinery Monitoring

Navigation Monitoring

Aero-space Monitoring

BSC (Base station controller Processing)

Home Automation

Environmental monitoring

5

the channel and go back to sleep again. The advantage of using CSMA is that it has lower delay

and promising potential throughput at lower traffic loads.

PAMAS [20] is CSMA based protocol tries to avoid overhearing, but does not avoid collisions.

S-MAC [21] is an improvement over PAMAS, by making idle nodes shut off their radios, there

by reduces further wastage of energy. But it does not avoid collisions, which is a significant

wastage of energy. T-MAC [22] solves the S-MAC problem by using short non-sleeping period

when the channel is idle.

On the other hand, in TDMA based protocols, the slots are assigned for sensor nodes and these

nodes wake up and listen to the channel in that assigned slots and then go back to sleep in other

slots. The main advantage is that due to collision free medium access, it will increase throughput

at high traffic loads. But, at low traffic load throughput is decreased due to idle slots. TRAMA

[23] is a TDMA based protocol, using traffic-based scheduling algorithm to avoid wasting slots.

IEEE802.15.4 [8] is the hybrid protocol that is the combination of TDMA and CSMA. It allows

devices to access channels in a contention access period or a collision free period. Z-MAC [24]

and A-MAC [25] also the hybrid type protocols, robust to synchronization error. The comparison

of WSN protocols is shown in Table 1.3.

Table 1.3 Comparison of WSN protocols

Protocol Advantages Disadvantages Time

Sync

needed

Type Suffers

Hidden

Terminal

Problem

S-MAC, T-MAC,

TA-MAC, PAMAS,

B-MAC

Simple, flexible,

lower delay,

topology change at

any time

More collisions,

Low throughput at

higher traffic

No

CSMA

Yes

TRAMA, E-MAC

Increase throughput

At high traffic,

Collision free

Sync Problem, Low

throughput at low

traffic, Fixed topology

Yes

TDMA

No

Z-MAC, A-MAC,

Funneling MAC

Robust to sync

errors, collision

free, good

throughput

Fixed topology,

High overhead

Yes

Hybrid

yes

6

CHAPTER 2

RELATED WORK

2.1 Overview of MAC

The protocol stack used by the base station and sensor nodes is shown in Figure 2.1. The

protocol stack consists of five layers and three planes [1]. The layers are: the physical layer, data

link layer, network layer, transport layer and application layer. The planes are: power

management plane, mobility management plane and task management plane.

2.1.1 Physical layer

The physical layer is to meet the needs of receiving and transferring data collected from the

hardware. The physical layer is responsible for carrier frequency generation, frequency selection,

signal detection, modulation and data encryption, transmission and receiving mechanisms.

2.1.2 Data link layer

The data link layer should be power-aware and at the same time to minimize the collisions

between neighbors‟ signals because the environment is noisy and sensor nodes themselves are

highly mobile. The data link layer should meet the requirements for medium access, error

control, multiplexing and error detection and correction.

2.1.3 Network layer

The network layer is responsible to find efficient routing for the packet to travel on its way to a

destination. Network layer provide deliver the packets across the network.

Fig. 2.1 Protocol stack

7

2.1.4 Transport layer

The transport layer regulates traffic flow through the network to the distant end and provides

reliability measures. The transport layer divides large, upper layer application data into sequential

segments and also reorders and reassembles them into data packages for forwarding up to the

application layer. Transport layer can provide flow control and congestion control, and high-level

packet error checking.

2.1.5 Application layer

The application layer provides network services directly to the user for electronic mail, file transfers,

virtual terminal, and file servers [26][27]. Network layer also define the format and order of message

exchanges between processes operating on different network or subnet.

The power management plane is responsible for power utilization by the nodes. The Mobility

management plane manages the movement pattern of the sensor nodes, if they are mobile. The

task management plane schedules the sensing and forwarding responsibilities of the sensor

nodes. As wireless sensor networks are fully dependent on battery, the nodes should be

operational for years without the necessity of exchanging the batteries.

2.2 Attributes of Good Protocols

In order to design a good MAC protocol for WSNs, the following attributes are to be considered

[21] [28].

2.2.1 Energy efficiency

Energy efficiency defines as the energy consumed per unit of successful communication. It is

also defined as the ratio of the total energy consumed to the total energy transmitted. The lesser

the number better the efficiency. Since sensor nodes are battery powered, it is very difficult to

change or recharge batteries. The energy consumed by hardware of sensor node is given in Table

2.1.

8

2.2.2 Latency

Latency is referred as end-to-end delay. It is measured by the time interval between when a

message is queued for transmission at the physical layer until the last bit is received at the receiving

node. The latency is measured in seconds, and the performance rating decreases with increasing time.

2.2.3 Throughput

The network throughput is defined as the total number of packets delivered at the sink node per

unit time. It is better that sink node receives more data. Throughput is measured in bits/second

and packets/second, and the performance rating increases with higher rates.

2.2.4 Scalability

The capability of communication systems regardless of the number of sensor nodes performing a

transaction and the size of the network is called scalability. It refers the ability to accommodate

the change in the network size.

2.2.5 Stability

The communication system should handle the traffic congestion and sudden increase in loads is

called stability.

Table 2.1 Battery life estimation of sensor node

System specifications Current

Processor Current (full operation) Current (Sleep)

8 mA 8 µA

Radio Current in receive Current transmit Current sleep

8 mA 12 mA 2 µA

Logger Memory Write Read Sleep

15 mA 4 mA 2 µA

Sensor Board Current (full operation) Current (Sleep)

5 mA 5 µA

Computed battery capacity (mA-hr) 250 1000 3000

Battery life(months) 1.45 5.78 17.35

9

2.2.6 Fairness

Channel capacity should be fairly shared among the nodes. Usually fairness [28] is not very

important factor, but it increases the quality of service.

2.3 Routing Mechanisms

Many specific algorithms [29] [30] [31] have been proposed to solve these problems of routing

data in wireless sensor networks. These routing mechanisms could be classified as data-centric,

hierarchical and location based protocols depending on the network structure and applications.

2.3.1 Data-centric protocols

Data-centric protocols are query-based and depend on the naming data of interest, which can

reduce repeated transmissions. These protocols are able to select a set of sensor nodes and can

allocate data aggregation.

SPIN: Sensor Protocols for Information via Negotiation (SPIN) uses Meta data instead of a full

data packet transmitted at each node to all nodes [15]. SPIN ensures that low redundant data sent

throughout the network and solve problems, such as wasting energy and bandwidth to send extra

copies of data by sensors in the same area [1], of a broadcasting mechanism of flooding. SPIN is

more efficient and fairly simple, but it consumes more energy.

2.3.2 Hierarchical protocols

TEEN: Threshold Sensitive Energy Efficient (TEEN) is a hierarchical protocol designed mainly

for sudden changes in the sensed environment [14]. The sensor network architecture in TEEN is

based on hierarchical grouping. The core advantage of TEEN is that it works well in conditions

where quick changes in the sensed attributes occur. But, in large area networks TEEN tends to

consume considerable amounts of energy, because of long distance transmissions.

LEACH: Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm is to form clusters of

the sensor nodes based on the received signal strength, and use local cluster heads as routers to

the sink [31]. In LEACH mechanism the transmissions are mainly managed by cluster heads to

save energy. The drawback of LEACH is that the dynamic clustering brings extra overhead, such

as rotation of cluster head, advertisement etc., and therefore consumes energy.

10

PEGASIS: Power-Efficient Gathering in Sensor Information Systems (PEGASIS) is an energy

efficient protocol [32] that is slightly better than the LEACH. The PEGASIS is a protocol in

which each and every node communicates with a nearby neighbour for exchanging the data. In

PEGASIS, the cluster head selection neither follows the residual energy of the nodes nor the

location of the base station. The drawback of PEGASIS is that the nodes are grouped into chains

[33], which cause redundant data transmissions.

2.3.3 Location-based protocols

Since sensor nodes are randomly scattered where there is no addressing scheme like IP-

addresses. In most applications, location information is needed to optimize routing in an energy

efficient way. Some algorithms in this scheme are:

MECN: Minimum Energy Communication Network (MECN) [34] recognizes a relay region for

each node, which is consisted of nodes in a surrounding area. The main advantage is that MECN

dynamically adapts to eliminate the nodes to the new sensors since it is capable of self-

reconfiguring. But MECN is not widely used as it increases the overhead.

2.3.4 Network flow and QoS-based protocols

QoS-based protocols consider end-to-end delay requirements and establish paths in sensor

networks. A few examples of these are discussed in this section.

MLER: Maximum Life Energy Routing (MLER) [35] is used to maximally extend the network

lifetime by defining link cost as a function of residual energy of node. The protocol leads to

establish traffic distribution as a result of maximizing the lifetime of the network, that is a

possible solution to the routing problem in sensor networks.

SAR: Sequential Assignment Routing (SAR) is the first protocol [1] for WSN that includes a

notion of QoS. The main aim of the SAR algorithm is to minimize the average weighted QoS

metric throughout the lifetime of the network. The advantages include fault-tolerance and fast

recovery. But SAR suffers from the overhead of maintaining the table and states at each sensor

nodes.

11

2.4 Wireless Sensor Network Architecture

WSNs can be classified into three types of architectures namely, homogeneous, heterogeneous

and hybrid.

2.4.1 Homogenous sensor networks

The base stations and sensor nodes equipped with equal capabilities of computational power and

storage capacity. Data gathering is based on the structure of data dissemination. Flat and

hierarchical topologies belong to this group [36].

2.4.2 Heterogeneous wireless sensor networks

Heterogeneous WSNs have different types of nodes with different functions. The nodes have

different components depending on the type of sensors being used [37]. A two-tiered

heterogeneous network is shown in Figure 2.2. These nodes can be embedded easily [38]. The

energy consumption in different nodes is given in Table 2.2.

Table 2.2 Power used by Tyndall nodes

Mode Small node (10mm) Larger node (25mm)

Sleeping 20 µW 53µW

Processing 10 mW 29 mW

Accessing memory 13 mW 31 MW

Receiving/ listening radio 55 mW 75 mW

Transmitting of radio +10dbm 109 mW 128 mW

Figure 2.2 Two tired heterogeneous network

12

CHAPTER 3

THESIS OBJECTIVES

3.1 Energy Waste in WSN

The main objective of WSN is the lifetime maximization. Radio transceiver is the main energy

consumer. MAC protocols play an important role to control the operation of radio and it

significantly affects the energy consumption of the whole network. Major sources of energy

waste are basically classified into five types [9] [24] [39].

3.1.1 Collision: When at least two sensor nodes try to access the communication channel at the

same time, data collision occurs. The collided packets must be retransmitted and this leads to

wastage of energy.

3.1.2 Packet overhead

Sending, receiving and hearing certain control packets in WSN also consume more energy.

3.1.3 Idle listening

In wireless sensor networks a node listens to the traffic if it is in idle state. If a node does not

transmit or receive, if it has no packets, it means that the node is in listen mode.

3.1.4 Overhearing mechanism

When a node picks up packets that are destined to other nodes is called overhearing. Overhearing

consumes more energy, which may increase when traffic load is heavy. In Figure 3.1, node A

overhears the transmission of packets from the node B to C.

Fig. 3.1 Overhearing Mechanism

Fig. 3.2 Hidden and Exposed node

13

3.1.5 Hidden node problem

In Figure 3.2, nodes P and R are within the range of node Q, but they are not in the range of each

other. If node P is communicating to node Q, and node R wishes to communicate to node S, node

R may sense the channel and finds it idle. Otherwise, it causes collision at node Q.

3.2 Computing Energy Efficiency

Energy efficiency is defined as the ratio of throughput versus energy consumed [33].

Energy Efficiency = Throughput / Energy consumed.

Energy consumption in ordinary sensor node is computed as follows:

Sensing Energy Es: The energy used to activate sensing circuitry within the node. The magnitude

of this energy depends on task that is assigned to the sensor.

Transmitter energy Et: The energy needed for transmission of data. It is depending on transmitter

power, size of data packet, and the data transfer rate.

Receiver Energy Er: A sensor node is also in charge of receiving packets from other nodes is

called receiver energy.

Computation energy Ec: Sensor processing unit must be activated to process the circuitries.

The total energy Etot = Es + Et + Er + Ec.

3.2.1 Energy in S-MAC

The total energy Etot can be given by the sum of the overhearing avoidance Eoa and the ratio of

sleep phase and discover phase Tsl/Tdis.

Etot = Eoa + Tsl/Tdis.

3.2.2 Energy in T-MAC

T-MAC uses adaptive duty cycle. Energy for idle listening Eid and sent messages Est, received

messages Ers, and overhead Eoh, and the total energy Etot can be calculated as

Etot = Eid + Est + Ers + Eoh + (Tsl – Ta)/Tdis.

3.3 Energy Calculation in Election Phase

The distance d between a cluster head CH and base station BS for long distance is given by ld.

The energy consumed to transmit b bits of message is given by:

Etrl = b*Eelec + b*Eamp * ld ----------------------------------------------------(3.1)

14

Where Eamp is the energy consumed by amplifier and Eelec is energy consumed by Electronic

circuit. Energy consumed for transmission with b bits of message for shorter distance sd is given

by:

Etrs =b* Eelec +b* Efs * sd -------------------------------------------------------(3.2)

Where Efs is Energy consumed in short distance by the amplifier. Energy consumed to receive b

bits of message is given by:

Erc=b* Eelec ------------------------------------------------------------------------(3.3)

Let number of sensor nodes is n and c is the clusters, then totally n/c nodes in each cluster. The

energy consumed by cluster head CH from equations (2.2) & (2.3) is given by:

Ech = b* Eelec +b*Efs* sd + {(n/c-1) * (b* Eelec)} ---------------------------(3.4)

Energy consumed by non-cluster head Enc is given by:

Enc=(K*b* Eelec) +{(b* Efs * sd) + (b* Eelec) }-------------------------------(3.5)

Energy consumed to receive messages from the remaining nodes that are not part of the group of

cluster head [39] is given by:

Enrc = (n/c-1) *(Eelec +Eag) --------------------------------------------------------(3.6)

Where Eag is the energy consumed during data aggregation. The total energy consumed by

cluster head from equation (1) and (6) is given by:

Echt = (b* Eelec + b* Eamp * ld) + {(n/c-1) *( Eelec + Eag)} ------------------(3.7)

Normally the sensor nodes sense the environment and transmit data to the cluster heads. The

cluster head receives all the data and aggregates it before sending it to the base station.

15

CHAPTER 4

NOTEWORTHY CONTRIBUTION IN THE FIELD OF PROPOSED WORK

4.1 MAC Protocols for Wireless Sensor Networks

We introduce various MAC protocols for wireless sensor networks. These protocols are

classified into contention-based, contention-free and hybrid protocols.

4.1.1 Contention-Based protocols

S-MAC: S-MAC [21] consists of three major components: periodic listen and sleep, collision

and overhearing avoidance, and message passing as shown in Figure 4.1.

Normal

Active state

Sleep state

S-MAC

Fig. 4.1 S-MAC protocol design

Periodic Listen and Sleep

In many sensor network applications, if no sensing event occurs, then nodes remain idle for a

long time. The data rate during this period is considerably low. In comparison to TDMA

schemes with very short time slots, S-MAC requires low synchronization among neighbouring

nodes, which are free to choose their own listen/sleep schedules. The drawback of the scheme is

that latency is increased due to periodic sleep of each node.

T-MAC: T-MAC (Timeout MAC) [22] is a contention-based, adaptive energy efficient MAC

protocol for wireless sensor networks. T-MAC reduces energy consumption by introducing an

active/sleep duty cycle. TDMA-based protocols are naturally energy preserving, because they

have a duty cycle built-in, and do not suffer from collisions [40].

16

Normal

Active time

TA Sleep time TA TA

T-MAC

Fig. 4.2 T-MAC Protocol Design

Every node in T-MAC periodically wakes up to communicate with its neighbors using a

Request-To-Send (RTS), Clear-To-Send (CTS), Data, Acknowledgement (ACK) scheme, that

provides both collision avoidance and reliable transmission [11], and then goes to sleep again

until the next frame. In the active period, a node will keep listening and potentially transmitting

the messages. When no activation event has occurred then active period ends for a time TA as

shown in Figure 4.2. T-MAC consumes much less energy than S-MAC, but it suffers from the

early sleeping problem.

R-MAC: Reservation-MAC (R-MAC) [41] is a contention-based protocol, uses two separate

periods during the communication process. Initially, nodes compete for time slots reservation for

their future transmissions, and in the next period, each node either transmits data or receives data

from the corresponding sender. R-MAC is slightly better than S-MAC and T-MAC in energy

consumption.

TA-MAC: TA-MAC protocol is based on S-MAC protocol uses both active and sleep periods.

The duty cycle mechanism proposed in the S-MAC protocol is energy efficient, it increases

packet forward latency but reduces throughput in the network. TA-MAC protocol [5] solves this

problem by using Busy-Signal (BS) packet. The main function of the TA-MAC is if a node fails

to get the medium, it goes to sleep. It is active when the receiver is active.

17

4.1.2 Contention-Free protocols

TRAMA: Traffic adaptive medium access protocol (TRAMA) [23] reduces energy consumption

by using unicast, multicast and broadcast transmissions. TRAMA uses a distributed election

scheme in slotted time and determines which node can transmit at a particular time slot. TRAMA

is fair and avoids collisions. TRAMA consists of three components, namely, neighbor protocol

(NP) which gathers information from neighbor nodes, Schedule exchange protocol (SEP) used to

exchange two-hop neighbor information and programs and lastly, adaptive election algorithm

(AEA) decides on the nodes in current time zone.

Fig. 4.3 Time-slot structure of TRAMA

There are two types of time slots namely random access slots for signaling and schedule access

slots for transmission as shown in Figure 4.3. In random access mode, each node transmits by

selecting a slot randomly. All nodes must be in either transmit to neighbors or receive from the

neighbors.

DE-MAC: Distributed Energy-Aware-MAC (DE-MAC) is a TDMA based MAC protocol [42]

to address the energy management problem in WSNs. It employs a periodic sleeping mechanism

to avoid idle listening and overhearing. In the beginning of the election process, each node sends

its energy level to all of its neighbors. A node with the minimum energy level is elected at the

end of election process. One or more winners are elected by this process and winners have time

slot twice the number of losers. This gives more energy savings compared to TRAMA.

4.1.3 Hybrid protocols

Z-MAC: Zebra-MAC (Z-MAC) is hybrid protocol [24], the combination of both CSMA and

TDMA. Z-MAC is robust to synchronization errors but its performance always falls back to that

of CSMA. Z-MAC is robust in synchronization error, slot assignment failures and time varying

18

channel conditions. But the problem in Z-MAC is that it does not solve hidden terminal

problem, and also it suffers huge overhead. Hence it is not suitable for individual nodes.

Funneling-MAC: Funneling-MAC is the combination of hybrid TDMA and CSMA/CA MAC

protocol [43]. It has a unique funneling effect [44], in this the events generated by the sensor

fields travel hop-by-hop in a many-to-one traffic pattern towards different sinks. It uses local

TDMA scheduling the funneling region alone to give additional scheduling opportunities to the

nodes near to the sink.

4.2 Analysis of MAC Protocols

This thesis analyses the MAC protocols and the models are validated and compared in detailed

simulations. Each simulation result is the result of more than 15 runs with the outcome of the

factors like collisions, latency, and the likes.

4.2.1 Energy consumption

The main constraint on sensor network is that sensors rely on batteries. As sensors used in large

numbers it will be difficult to change or recharge batteries in the sensors. A classical energy

model was proposed by [44] in Table 4.1, consists of low power consumption radio.

Table 4.1 Radio Model

Radio Mode Energy Consumption

Transmitter Electronics (ETx-elec)

Receiver Electronics (ERx-elec)

Eelec = ETx-elec = ERx-elec

50nJ/bit

Transmitter Amplifier (Eamp) 100 pJ/bit/m2

Idle (Eidle) 40nJ/bit

Sleep 0

Energy used in transmitting or receiving one bit is found by using power value, i.e.

Energy = power * time.

The energy consumption of Mica2 is the sum of energy transmitting, receiving, listening,

sampling and sleeping [45]. The calculation of energy in transmitting and receiving one bit is

given as:

19

Energy = Current * Voltage * Time …………………………….………..(4.1)

According to [46], a node sends\ a packet every 80msec and every packet will take 9msec for

sending and receiving. When a node receives a packet it will retransmit immediately. Let B be

the bakeoff time, time to switch to radio mode is STx, i.e. 250 µsec, Time to transmit a frame is

Txt and Srx be the time to switch to receive mode. Then time frame can be calculated as:

TF = B + STx + Txt + Srx ……………………………………………….(4.2)

If Rxt is the time taken to receive a frame, then the time cycle can be calculated as,

T = B + 2STx + Rxt + Txt ………………………………………………..(4.3)

Typical sources of energy loss in wireless sensor networks include idle listening, packet

collisions, protocol overhead, and message overhearing.

(A) Idle Listening

Idle listening occurs when a station listening an inactive medium. For example Chipcon CC2420

transceiver of 250 kbps [47] node can transmit 4.1ms energy. There are four techniques to reduce

idle listening. They are: static sleep scheduling, dynamic sleep scheduling, preamble scheduling

and off-line scheduling.

(B) Static Sleep Scheduling

Every frame in S-MAC protocol is divided into listen and sleep periods as discussed in section

4.1.1 is further divided into the synchronization period and data transfer period. In sleep period a

node listens for a SYNC message from its neighbour. Within the time period, if a node does not

hear any SYNC message then the node will set and broadcast its own sleep schedule.

(C) Dynamic sleep schedule

T-MAC protocol as discussed in section 4.1.1 introduces a listening timeout mechanism by

dynamically adapting an active listening period in response to network traffic to improve idle

listening overhead. T-MAC achieved five times the energy savings than S-MAC.

(D) Preamble scheduling

BMAC (Berkeley-MAC) [45] and WiseMAC [48] approach to organize sleep schedules by

allowing nodes to adopt sleep schedule with fixed sleeping cycle frequency. As shown in Figure

4.4, if a node senses activity, it wakes up, synchronizes and receives the packet.

20

Fig. 4.4 BMAC Low power listening

(E) Offline scheduling

Traffic-Adaptive MAC (TRAMA) is a schedule-based protocol described in section 4.1.2 uses an

Adaptive Election Algorithm (AEA) to randomly assign time slots. TRAMA establishes

collision-free data transfer to maximize sleep time for effective utilization of channel and

minimize latencies.

4.2.2 Frame collisions

When a wireless sensor node sends a MAC protocol frame, or message, which collides in time

with another message frame, collision occurs. In most single-channel radios, the radio cannot

simultaneously receive while in transmit mode. S-MAC and T-MAC protocols use contention

and RTS-CTS exchanges to reduce collisions.

4.2.3 Protocol overhead

Wireless protocol overhead consumes both energy and bandwidth. The networks serve as an

integrated system to transfer data between distributed application layer programs and provide

reliable data delivery. Adding data message headers and 2-to-1 Manchester encoding to the RFM

TR1001 [49] transceiver reduces an effective 60% [50] reduction.

4.2.4 Message overhearing

Receiving and discarding messages projected for other nodes is called message overhearing.

Receiving all messages is an efficient method will increase throughput and decrease latency

specifically in cases where the radio receive mode spends more energy than the transmission

mode.

Table 4.2 Current consumption in receiving and sleeping mode

Radio Receive Mode Power-down mode

CC2420 [48] 19.7A 1A

CC1000 [48] 9.6A 0.2A

Sending Node

Receiving Node

Sample period

Preamble

Sample period Sample period

21

4.2.5 Node energy capacity

IEEE 802.15.4 WSN transceiver platforms operates on two AA batteries and can achieve

approximately 3000mAh assuming 2.1 volt cutoff and a 20mA slow drain application [51]. The

current consumption of CC2420 and CC1000 is given in Table 4.2.

4.3 Basic Synchronization Procedure

In WSN synchronization transfer of time value from one node to another is the main procedure.

The method is very simple, read the clock, put the time value in radio packet and finally send it.

Node A read the time value Ta and node B can read the time value Tb. The difference in time D

can be computed as,

D = Tb-Ta ………………………………………………… (4.4)

Round-trip synchronization:

In this procedure, node A sends a packet with timestamp T1 to node B. After the communication

delay F, node B receives the packet and records its time T2.

T2 = T1 + F + D ………………………………………………... (4.5)

Now node B sends a response packet with timestamp T3 to node A. when the second packet

arrives, node A makes timestamp T4. The value of T4 is computed as,

T4= T3 + F – D …………………..…………………………….. (4.6)

Equation (3) – (2) gives the clock offset D

T4 – T2 = T3 + F – D – T1 – F – D ………..……………………………(4.7)

D = (T3 – T1 – T4 + T2) / 2 ……………………………………….……(4.8)

Sum of the equations (2) & (3) can be used to compute communication delay F

T2 + T4 = T3 + F – D + T1 + F + D ………………………………..… (4.9)

F = (T4 + T2 – T3 – T1) / 2 ………………..…………………………....(4.10)

If the value of D is known, node A can estimate the time of node B.

22

CHAPTER 5

DESIGN OF PROPOSED PROTOCOL

5.1 Related work - Clustering Algorithms

The clustering algorithm provides network scalability and energy efficient communications by

minimizing transmission overhead and increasing transmission consistency. Clustering method is

used to save communication bandwidth since it limits the scope of inter-cluster interactions to

cluster heads and avoids duplicate messages among sensor nodes [52]. Moreover, clustering can

stabilize the network topology at the level of sensor nodes and thus reduces the maintenance

overhead [53].

5.1.1 LEACH

In recent years many clustering algorithms on self-configuring clustering had been presented for

energy efficiency. Low-Energy Adaptive Clustering Hierarchy (LEACH) [44][54] is used in

distributed algorithms in which clustering explicitly encourages data aggregation to reduce the

transmission burden in the network. Later, the low energy adaptive clustering hierarchy with

deterministic cluster head selection (DCHS) was proposed [55]. It extends LEACH‟s algorithm

by a deterministic component followed by hybrid energy-efficient distributed clustering (HEED)

[56].

Operation of LEACH

The operation of LEACH is classified into number of rounds. Each sensor node elects itself to be

a cluster head at the beginning of round r + 1 (which starts at time t) with probability Pi(t). Pi(h)

is chosen such that the expected number of cluster heads for this round is k. Thus, if there are N

sensor nodes in the network, the expected number of cluster heads is:

E[N] = …………………………………….(5.1)

Each and every sensor nodes to be a cluster head in N/k rounds on average. Ci(t) is denoted as

the indicator function determining whether or not sensor node i has been a cluster head in the

23

most recent (r modN/k) rounds, then each sensor node should choose to become a cluster head at

round r with probability:

……………………5.2)

In this case, only sensor nodes that have not already been cluster heads recently may become

cluster heads at round r + 1.

In our research, we divide these clustering algorithms into auto-configuring cluster formation

and centralized cluster formation. In centralized cluster formation, the base station elects leaders

(cluster heads) each round to afford guarantee about the placement and number of cluster heads

by a centralized clustering scenario. Hence, these protocols often need sensor nodes to be

equipped with high-sensitivity global positioning system receivers for gathering position

information of sensor nodes. In auto-configuring cluster formation, each sensor node makes

autonomous decisions itself by means of distributed algorithm. The major advantages of this

approach are that no long-distance communication with the base station is required and

distributed cluster formation can be done without the exact location information of the sensor

nodes in the network. Finally, no global communication is needed to set up the clusters [55].

5.1.2 Deterministic Cluster Head Selection (DCHS)

DCHS is the modified version of LEACH designed to increase the lifetime of LEACH. Equation

(3) shows the sensor node‟s residual energy, which increases the probability of any sensor nodes

that have not been a cluster head for last K/N rounds.

………..(5.3)

Where r is the number of consecutive rounds in which a sensor node has not been a cluster head.

and denote the residual and initial energy for sensor node n respectively. r is reset to 0

when a sensor node becomes a cluster head. For the deterministic selection of cluster heads only

local and no global information is necessary. Also, the nodes determine themselves whether they

become cluster heads.

24

5.1.3 Hybrid Energy-Efficient Distributed clustering (HEED)

HEED also based on clustering algorithm, but unlike LEACH, it does not select cluster heads

randomly [53]. The sensor nodes that have a high residual energy only can become CH. Its main

characteristics are:

To achieve well distribution of cluster heads in the network.

Avoiding the probability that two sensor nodes within each other‟s transmission range

becoming cluster heads.

Each sensor node is mapped to exactly one cluster and can directly communicate with

CH.

5.2 Comparative Analysis of MAC Protocols

MAC is a wide-ranging research area where many researchers have done research work in the

area of MAC protocol for wireless sensor networks. Contention-based MAC protocols are

widely used because of its simplicity and robustness [57] to the hidden terminal problem. Due to

idle listening, energy consumption for these contention-based protocols is very high. TDMA-

based MAC protocols [58] on the other hand are based on reservation and scheduling and have

the natural advantage of energy conservation because of the duty cycle of radio is reduced and

also there is no overhead and collisions. SAS-TDMA [59] improves the quality of service for the

entire network. It achieves significant improvements for realistic dynamic wireless sensor

networks when compared to existing scheduling algorithms with the aim to minimize latency for

real-time communication.

S-MAC as discussed in section 4.1.1 is a contention-based MAC protocol uses fixed duty cycle

and reduces energy consumption by putting nodes into sleeping mode intermittently, but it is

unable to adapt its operation to varying traffic rates. TA-MAC has two mechanisms. First one is

an adaptive contention window, which can protect data from loss due to limit of the buffer. The

second method is to use BS packet, which tries to tell nodes a few hops away about the current

transmission so that they can wake up in time to continue the transmission. L-MAC is well suited

for data collection applications in which sensors have to report to sink nodes or base station

25

through multiple nodes. R-MAC protocol mainly focuses on overhearing avoidance by adjusting

the listen-sleep durations according to the traffic load of the network. R-MAC reduces the

collisions and also uses variable duration of the listen and sleep periods according to the traffic

load.

Simulation Setup

We use Castalia 3.0 in OMNET++ [60] [61] software to simulate the three MAC protocols under

the same scenario. Every node except sink collects information and sends messages to the sink

node through several hops. We have done different tests to measure energy consumption.

Fig. 5.1 Energy Consumption of existing protocols

Fig. 5.2 Latency of existing protocols

Figure 5.1 shows the energy consumption of S-MAC, L-MAC and TA-MAC protocols. It shows

that S-MAC uses more energy than L-MAC and TA-MAC. Energy consumption increases with

increase in message inter-arrival time. In our experiment, it is seen that when traffic is light L-

MAC and TA-MAC protocols can save more energy than S-MAC protocol due to idle listening.

It is seen from Figure 5.2 that the latency of L-MAC is much less than the other two protocols.

Under the high traffic, and TA-MAC and S-MAC suffer the huge delay. Further TA-MAC

suffers more latency than S-MAC.

26

Fig. 5.3 Energy consumption vs. number of sources

We have compared S-MAC, T-MAC, R-MAC and TA-MAC protocols for energy consumption

vs. number of sources. It is shown in Figure 5.3 that if the number of sources increased, then the

energy consumption will be slightly decreased. S-MAC consumes more energy than other

protocols. TA-MAC consumes less energy than S-MAC, T-MAC and R-MAC protocols.

5.3 Problems in the Existing Protocols and Motivation of Proposed Protocol

The first drawback of existing protocols (S-MAC, T-MAC and TRAMA) is the increased

idle listening.

The second limitation is overhearing. This is due the fact that the sensor nodes listen to

messages that were not projected for them.

The third problem is collisions. The sensors waste energy in retransmitting the collided

packets.

Many authors have designed the proposals (R-MAC, OB-MAC and SIFT) to solve these issues,

but only to a certain level. TRAMA is a schedule-based protocol that attempts to solve these

problems by using distributed election algorithm. It uses Neighbor Protocol (NP) to get

information from the neighbor nodes. This increases overhearing, which is the motivation to

design a new protocol: Energy Efficient Token Bus Protocol (ETB-MAC) that can handle these

problems.

5.4 Architecture of Proposed System

ETB-MAC architecture is shown in Figure 5.4 in which the data related to this event can be

considered an urgent traffic and must be delivered to the sink (cluster head).

27

Fig. 5.4 Architecture of ETB-MAC protocol

ETB-MAC designed to provide low latency and an energy efficient path to deliver this urgent

traffic from the source node generating this traffic towards the cluster head.

Fig. 5.5 (a) Path established from S node to Sink

Initially a node starts a transmission by sending an RTS packet indicates that it has a packet to

send. Upon receiving an RTS packet, each neighbour node decides to join the communication by

sending message, otherwise simply transmits the message to the neighbouring nodes except the

node from where the message comes from. The sender sends the data through the path as shown

in Figure 5.5(a) by S-A-B-C-D-E-F-Sink. In our approach, only the nodes closer to the

destination are considered for sending data.

S

A B

C

D E

F

Sink

28

Fig. 5.5(b) sending CTS and RTS

Fig. 5.5(c) sending data

5.4.1 Format of RTS and CTS

The structure of RTS and CTS are proposed as shown in Figures 5.6 (a) & 5.6 (b) without

violating the IEEE802.11 standard. NNA stands for Next-Node-Address obtained by the sender‟s

routing agent and defines the address of the next node in the routing path in which the packets

are transmitted. SA is Sender-Address defines a node ignores receiving a CTS in which address

of previous node is specified. The CTS and RTS messages have identical structure of frame

format. FC refers to frame control and CRC is cyclic redundancy check.

FC Duration(NAV)

Address of

Previous Node

NNA Sender

Address CRC

Fig. 5.6(a) Proposed RTS frame format

FC Duration(NAV)

Address of

Previous Node

NNA Sender

Address CRC

Fig. 5.6(b) Proposed CTS frame format

29

When a node has data to transmit to sink, it takes immediate procession of the medium and also

informs the neighbors of that decision. Suppose a node has no data, it turns off its radio to save

energy. Normally if a medium is allocated to one node, other nodes also try to occupy the

medium.

5.4.2 Description of algorithm

The node (S) begins by sending RTS packets to the next neighbor node (A) as shown in Fig.

5.5(b). After receiving control packets, node (A) replies CTS message back to the sender. All

other neighbors of sender and receiver turn off their radio and go to sleep except the next node

(B) of the receiver. The node (A) now gets DATA packets from S after getting CTS packets from

the next node (B) as shown in Fig. 5.5(c). The sender S exchanges DATA/ACK packet from

node A and the forwarding process of CTS packets continues between the nodes included in the

path B-C-D-E-F till it reaches the Sink. From the Figure 5.7, it is shown that for each

DATA/ACK packets corresponds to transmission time „T‟ during which each neighbor can be

switched off its radio to save energy. That means in our proposal the start node S can transmit

RTS/CTS/DATA/ACK to its neighbor A within the time duration T. Sometimes the contention

problem may arise when two RTS packets collide in case two source nodes try to transmit

DATA packets at the same time. But it can be resolved by using Backoff algorithm.

Fig. 5.7 Data transmission of ETB-MAC

30

CCONTENTION-BASEDHAPTER 6:

IMPLEMENTATION: RESULTS AND DISCUSSION

6.1 Comparison with Contention-Based Protocols

In this chapter, we provide extensive simulation results and comparisons of the performance

between our ETB-MAC algorithm and two other popular routing algorithms like S-MAC and T-

MAC in terms of throughput (packet delivery ratio) and latency under variable workload

conditions. We use Castalia-3 on OMNeT++ simulator to evaluate the performance of our new

ETB-MAC protocol.

In our simulation, sensor nodes N are randomly distributed in a [100m × 100m] flat area with

transmission radius R from 50m to 200m depending upon the density of the network with data

length 2000 bits. The sink node is located at one end as shown in Figure 6.1. In addition, all

sensor nodes are stationary. When an event happened, a node senses it and sends a report to the

sink.

Fig. 6.1 Random rooting tree of 60 nodes

6.1.1 Performance analysis

In this section, we study the average delay with different sensing range and throughput with

varying CBR sources. We compare our ETB-MAC algorithm with well-known contention-based

31

protocols of S-MAC and T-MAC. ETB-MAC1 is the output if our algorithm is implemented

with S-MAC and ETB-MAC2 is the output if our algorithm is implemented with T-MAC.

We plot the average delay as a function of the number of event reports required when we fix the

sensing range of 50m as in Figure 6.2. It shows that even with the higher number of reports

required, ETB-MAC outperforms S-MAC and T-MAC in terms of the end-to-end delay.

Next we would prefer to consider the case when the sending nodes are scattered throughout the

network. In this experiment, we randomly place 120 nodes in the simulation area. After

observation we find that at every 0.5 second duration of the 100 seconds simulation time, ETB-

MAC achieves better latency than S-MAC and T-MAC, which is illustrated in Figure 6.3.

Fig. 6.2 Average delay when sensing range 50m

Fig. 6.3 Average delay with varying sensing range

We then compared the throughput achieved by ETB-MAC with S-MAC and T-MAC protocols

under constant-bit-rate (CBR) traffic. In our model, 120 sensors are scattered over a

[100m×100m] area, the transmission range of each sensor is 60m.

When the number of contending CBR flow is lesser as plotted in Figure 6.4, ETB-MAC loses

throughput compared to contention based MAC protocols. This loss in throughput happens

because in this case, contention window in S-MAC and T-MAC is large enough to resolve the

collision between the CBR flows. When the number of CBR flows increases further (Figure 6.5),

ETB-MAC outperforms S-MAC and T-MAC in terms of raw throughput (packet delivery ratio is

higher).

32

Fig. 6.4 Packet delivery ratios

Fig. 6.5 Average delay vs. number of CBR sources

6.2 Comparison with Cluster-Based Protocols

We provide extensive simulation results and comparisons of the performance between our ETB-

MAC algorithm and two other popular „cluster‟ based routing algorithms like LEACH and

HEED algorithms in WSNs.

Simulations are done under various network environments with various factors such as node

number, transmission radius, BS location, network scale, traffic pattern as well as network

structure (flat and hierarchical). If our algorithm is implemented with LEACH, then the outcome

is ETB-MAC1 and in case if it is implemented with HEED, then the outcome is ETB-MAC2.

6.2.1 Energy consumption

In this section, our study is focused on the performance of average energy consumption under

different transmission radius R , different source to BS distance d , different node number N,

different BS location, different network scale as well as different traffic pattern.

We primarily study the influence of transmission radius R on energy consumption since different

routing algorithms will choose the next hop node based on their next hop selection criteria. The

simulation environment has 120 nodes randomly deployed in a 100×100m2 area with base station

BS placed in the middle of the area. In Figure 6.6 we can see that LEACH always consumes the

largest amount of energy since it utilizes the multi-path energy model with average long distance.

33

Fig. 6.6 Energy consumption with varying radius Fig. 6.7 Energy consumption with varying distance

In case of the HEED routing algorithm, the hop number is larger when R is small; this results in

more energy consumption. As R increases, it will prefer to choose the next hop with the distance

ri ≈ R to get close to the BS (HEED). It gets the best performance of energy consumption at R

=100. ETB-MAC2 algorithm consumes the least energy since it always tries to divide d into

several pieces with similar distances. Figure 6.7 shows the energy consumption under different

source to BS distance. When d > 120, LEACH consumes the largest energy since multi-path

model is used under which power attenuates in the fourth order of distance.

From the Figure 6.8 we conclude that the variation or fluctuation of the average energy

consumption becomes smaller as N increases. Our proposed ETB-MAC algorithm consumes less

energy as it always finds the suboptimal hop number and intermediate nodes as N increases.

Fig. 6.8 Energy with varying node numbers

Fig. 6.9 Total energy consumption vs. number of nodes

Then, we study the energy consumption under different network scale. Figure 6.9 shows a small

scale network where there are 100 nodes randomly deployed in 120×120m2 area with BS is

34

located at (50, 100) and we set R = 50, dc= 100 and Δ = 25. Our ETB-MAC2 has the best

performance. This is because the average source to the sink node is relatively small.

Figure 6.10 shows a similar case under large scale network where there are 200 nodes randomly

deployed in 200×200m2 area with BS at (150, 150). It is worth observing that again our ETB-

MAC2 performs better than other algorithms, the reason is that in ETB-MAC algorithm all the

nodes within the cluster are in sleeping mode, but one node (elected node) collects the

information from the surroundings and sends them to the sink node or base node.

In a very small scale network where there are 50 nodes randomly deployed in an area of

100×100m2 as shown in Figure 6.11, our ETB-MAC algorithm suffers slightly due to the

election algorithm.

Fig. 6.10 Total energy in larger network

Fig. 6.11 Total energy in smaller network

Fig. 6.12 Total energy with varying number of nodes

Fig. 6.13 Average network life time

35

As shown in Figure 6.12 the total energy consumed is increasing with increasing number of nodes. It

is suggested that all parameters including energy usage, battery level and distance should be

incorporated in the cost function. Our proposed ETB-MAC algorithm consumes less energy than

other algorithms.

6.2.2 Network lifetime

Our major study includes the average network lifetime under different network topologies, as shown

in Figure 6.13, there are 250 nodes randomly placed in an area of 200×200m2 with BS placed at (120,

120). The network lifetime usually decreases with R since more energy will be consumed on average.

HEED algorithm has a longer lifetime when R ≈ 110, because it tends to choose the next hop node

with distance when R = 150. When R ≤ 100, the lifetime of ETB-MAC is relatively shorter as shown

in Figure 6.14 because the sub-optimal hop number cannot be met and larger hop number is needed.

The network lifetime of different cluster head selection schemes is shown in Figure 6.15.

Thus to sum up the comparative studies reveal that our ETB-MAC2 has the longest lifetime while

LEACH algorithm has the worst average network lifetime.

Fig. 6.14 Network life time vs. transmission radius

Fig. 6.15 Network life time vs. number of nodes

In this chapter, we have compared the performance of our ETB-MAC with LEACH and HEED

routing algorithms in terms of energy consumption, hop number, network lifetime, successfully

delivered packets and delay time. We also studied the latency and throughput our algorithm with the

contention-based algorithms of S-MAC and T-MAC. From the extensive simulation results, we see

that our ETB-MAC has the best performance of energy consumption, packet delivery ratios, average

delay and network lifetime.

36

CHAPTER 7

CONCLUSION AND FUTURE WORK

In this proposal we have designed a new protocol ETB-MAC and compared against the existing S-

MAC, T-MAC, HEED & LEACH protocols. Depending on the traffic load, our protocol improves the

energy consumption significantly when compared to the contention-based protocols of S-MAC and T-

MAC. This is due to the fact that one node is elected as cluster head which consumes energy and most

of the other nodes within the cluster go to sleep. The simulation results show that ETB-MAC

consumed less energy than other protocols within an area of about 100 m2 even in high traffic. We

have also proposed new optimized cluster head selection method by reducing energy consumption of

overall networks and preserves network topology and connectivity.

The results of the simulation show that ETB-MAC outperforms the cluster-based protocols of HEED

& LEACH algorithms on network lifetime, data transmission capacity and energy efficiency with the

concern of position distributions. Therefore, our scheme can surely guarantee to prolong network

lifetime, reduce data transmission latency and improve the utilization of energy.

In our research we have analyzed contention-based protocols up to 100 nodes, and in the future,

simulations can be done with more number of nodes and also with other existing protocols with

different parameters.

37

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40

APPENDIX A

A1. Symbols Symbol Meaning

Es Sensing Energy

Et Transmitter Energy

Er Receiver Energy

Ec Computation Energy

Etot Total Energy

Eoa Overhearing Avoidance

Tsl Energy in Sleep Phase

Tdis Energy in Discover Phase

Eid Energy in Idle Listening

A2. Acronyms

Acronym Meaning

CRC Cyclic Redundancy Check

CSMA Carrier Sense Multiple Access

CSMA/CA Carrier Sense Multiple Access with Collision Avoidance

IEEE Institute of Electrical and Electronics Engineers

MAC Medium Access Control

QoS Quality of Service

RTS Request To Send

TMAC Time out MAC

TDMA Time Division Multiple Access

WSN Wireless Sensor Network

BS Base Station

CH Cluster Head

HEED Hybrid, Energy-Efficient, Distributed

LEACH Low Energy Adaptive Clustering Hierarchy

41

LIST OF PAPERS PUBLISHED BY THE CANDIDATE JOURNALS

1. P. Udayakumar, Ranjana Vyas, O.P. Vyas, the research paper titled “Energy Efficient Election

Protocol for Wireless Sensor Networks”, was published in the IEEE DigitalExplore, pp. 1028-

1033, ISBN 978-1-4673-4921-5, July 2013.

2. P. Udayakumar, Ranjana Vyas, O.P. Vyas, Research paper titled “TokenBusBased MAC Protocol for

Wireless Sensor Networks”, was published in the International Journal of Computer

Applications (IJCA), ISSN 0975-8887, vol. 43, No.10, April 2012.

3. P. Udayakumar, Ranjana Vyas, O.P. Vyas,Research paper titled “Analysing and Designing Energy

Efficiency in Wireless Sensor Networks”, was published in the International Journal of

Engineering Research & Technology(IJERT), ISSN: 2278-0181, vol. 1, issue 9, pp. 1-6, Nov

2012.

CONFERENCES

4. P. Udayakumar, Ranjana Vyas, O.P. Vyas, paper titled “Energy Efficient Election Protocol for

Wireless Sensor Networks”, presented in the IEEE International Conference ICCPCT-2013

Nurul Islam Center for Higher Education, KumaraCoil (TN) March 21-22, 2013, and the paper

was published in the conference proceedings.

5. P. Udayakumar, Ranjana Vyas, O.P. Vyas, “Thorough Analysis of Contention-Based MAC Protocols for

Energy Efficient Wireless Sensor Networks”, presented in the International Conference, CUTSE

University, Malaysia on 6-7 Nov 2012 and the paper was published in the conference proceedings,

pp. 218-221.

6. P. Udayakumar, Ranjana Vyas, O.P. Vyas, “Comparative Analysis of Contention-Based MAC

Protocols for Energy Efficient Wireless Sensor Networks”, presented in the International

Conference on Information and Communication Technology (ICT-2011) organized by IRNeT,

Chennai from 24-25 Dec 2011 and the paper was published in the conference proceedings,

pp. 130-133.

7. P. Udayakumar, Ranjana Vyas, “MAC protocols for Wireless Sensor Networks”, presented in the

National Conference on Innovative Trends in Management Science and Technology (ITMAST-2012)

organized by CCEM, Raipur on 8 Apr 2012 and the paper was published in the conference

proceedings, pp. 417-419.