copyright 2011, deepika murali

AN ADAPTIVE GOSSIP PROTOCOL FOR IMPROVING COMMUNICATION

PERFORMANCE IN WIRELESS SENSOR NETWORKS

by

Deepika Murali, B.Tech

A Thesis

In

COMPUTER SCIENCE

Submitted to the Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

MASTER OF SCIENCE

Approved

Dr. Sunho Lim

Chair of Committee

Dr. Noe Lopez-Benitez

Peggy Gordon Miller

Dean of the Graduate School

May, 2012

Texas Tech University, Deepika Murali, May 2012

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ACKNOWLEDGMENTS

First and foremost, I would like to thank my supervisor, Dr. Sunho Lim, who

has extended his knowledge and support to me for the entire duration of my master’s

studies. I would like to thank him for allowing me work in my own way and providing

guidance and a helping hand whenever I was stuck on any problem. I could have not

completed my thesis without his constant encouragement and support.

I would also like to thank Dr. Noe Lopez-Benitez for agreeing to be a part of

my thesis defense committee and spending his valuable time for me.

Finally, I thank my friends and family for their understanding and endless love.

I thank my parents for their unconditional support all these years. They have given up

many things for me to be here and this thesis would certainly not have existed without

them. I would like to thank my brother and grandmother for their constant

encouragement and prayers. Also, special thanks to friends, Bruhadeesh, Anudeep,

Ashok and Ganessh Balaji for always being there for me and helping me overcome my

tough times.


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TABLE OF CONTENTS

ACKNOWLEDGMENTS..……………………………………………………………ii

ABSTRACT…………………………………………………………………………...vi

LIST OF FIGURES…………………………………………………………………..vii

LIST OF ABBREVIATIONS………………………………………………………….x

I INTRODUCTION..…………………………………………………………………..1

II BACKGROUND AND RELATED WORK…………………………………………6

2.1 Gossip-Based Ad Hoc Routing………………………………………………...6

2.2 Smartgossip: An Improved Randomized Broadcast Protocol for WSN……….8

2.3 Parametric Probabilistic Sensor Network Routing…………………………….9

2.4 Gossip-Based Sleep Protocol for Energy Efficient Routing…………………..9

2.5 Random Asynchronous Wakeup protocol……………………………………11

2.6 Summary……………………………………………………………………...12

III AN ADAPTIVE GOSSIP PROTOCOL FOR IMPROVING COMMUNICATION

PERFORMANCE IN WSN……………...…………………………………………...14

3.1 Problem Definition and Motivation…………………………………………..14

3.2 System Model………………………………………………………………...15

3.3 An Adaptive Gossip Protocol………………………………………………...19

3.3.1 Generating Forwarding Candidates……………………………………..19

3.3.2 Determining Gossip Probability…………………………………………20

3.3.3 Determining Sleep Probability…………………………………………..21


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3.4 Pseudo Code..…………………………………………………………………23

IV PERFORMANCE EVALUATION…………………………………………….....25

4.1 Simulation Testbed and Performance Comparison…………………………...25

4.2 Simulation Parameters………………………………………………………..28

4.3 Simulation Results ........................................................................................... 30

4.3.1 Random Network Deployment ................................................................ 30

4.3.2 Average Remaining Energy Comparison................................................. 31

4.3.3 Average Redundancy Comparison………………………………...…….35

4.3.4 Average Latency Comparison…………………………………………...39

4.3.5 Average Number of Messages Exchanged Comparison…………….......42

4.3.6 Total Number of Nodes Participated Comparison…………………..…..45

4.3.7 Average Delivery Ratio Comparison…………………………………....46

4.3.8 Simulation Results changing the fixed time intervals…………………...49

4.3.8.1 Comparison of Average Remaining

energy for fixed intervals 10, 20 and 30 in AGP………………..49

4.3.8.2 Comparison of Average Redundancy for

fixed intervals 10, 20 and 30 in AGP……………………………50

4.3.8.3 Comparison of Average Latency for

fixed intervals 10, 20 and 30 in AGP……………………………51

4.3.8.4 Comparison of Average Number of Messages

Exchanged for fixed intervals 10, 20 and 30 in AGP……….......52


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4.3.8.5 Comparison of Total Number of nodes participated

for fixed intervals 10, 20 and 30 in AGP………………………..53

4.3.8.6 Comparison of Average Delivery Ratio

for fixed intervals 10, 20 and 30 in AGP………………………..54

4.4 Summary…………………………………………………………………….55

V CONCLUSION AND FUTURE WORK…………………………………………..58

5.1 Conclusion……………………………………………………………………58

5.2 Future Work…………………………………………………………………..60

REFERENCES………………………………………………………………………..61

A. CODE IMPLEMENTATION……………………………………………………...63


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ABSTRACT

A Wireless Sensor Network (WSN) consists of a set of battery-powered devices (later

in short, nodes) equipped with sensing, computing, and communicating capabilities,

and it is rapidly integrated with various real-world applications in civil and military

environments. Due to the limited amount of battery energy, however, a great deal of

research effort has been devoted on developing energy efficient techniques in the

WSNs. In this thesis, we propose an Adaptive Gossip Protocol (AGP) to improve the

communication performance in terms of packet delivery ratio, packet latency, and

energy consumption under the energy-constrained WSNs. In the proposed AGP, a

node decides whether it should participate in the communication based on a gossiping

probability. If the node participates in the communication, it judiciously selects a set

of candidate nodes for forwarding. Otherwise, the node decides whether it should

sleep based on a sleeping probability. Here, gossiping probability is determined by the

number of neighbor nodes but sleeping probability is determined by both the number

of neighbor nodes and remaining battery energy. We build a discrete-event driven

simulator using CSIM20 and compare the proposed scheme with conventional

flooding and simple gossiping techniques. Extensive performance study shows that the

proposed scheme can save energy consumption up to 12% and reduce the packet

latency 5 times in comparison to flooding and gossiping techniques. The proposed

scheme also shows the competitive packet delivery ratio, and thus it is a viable

approach for WSNs.


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LIST OF FIGURES

1.1 Naïve flooding mechanism………………………………………………..............2

1.2 Controlled flooding mechanism…………………………………………...............3

2.1 An example of gossip1(p,k) with hop count k=2………………………….............7

2.2 (a) Synchronous GSP and Figure 2.2 (b) Asynchronous GSP…………….…….10

3.1 Random sleep periods…………………………………………………………....16

3.2 Wireless sensor network………………………………………………................18

4.1 Parameter table for gossip1(p,k) and flooding…………………………………...26

4.2 Parameter table for AGP………………………………………………................27

4.3 Random network deployment……………………………………………………30

4.4 Average remaining energy of gossip1 (p,k) where Gp = 0.65

flooding Gp = 1 and AGP comparison……………………………………32







4.8 Average redundancy of gossip1 (p,k) where Gp = 0.65



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flooding Gp = 1 and AGP comparison…………………………………..37



4.12 Average latency of gossip1 (p,k) where Gp = 0.65








4.16 Average number of messages exchanged of gossip1 (p,k)

where Gp = 0.65 flooding Gp = 1 and AGP comparison………………43








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4.20 Total number of nodes participated for gossip1 (p,k)

where Gp =0.60- 0.95, flooding Gp = 1 and AGP comparison…………..45

4.21 Average delivery ratio of gossip1 (p,k) where

Gp = 0.65 flooding Gp = 1 and AGP comparison……………………..47







4.25 Average remaining energy of AGP

for Periodic Time = 10, 20 & 30………………………………………...49

4.26 Average redundancy of AGP for

Periodic Time = 10, 20 & 30…………………………………………....50

4.27 Average latency of AGP for Periodic

Time = 10, 20 & 30……………………………………………………...51

4.28 Average number of messages exchanged in

AGP for Periodic Time = 10, 20 & 30…………………………………..52

4.29 Total number of nodes participated in

AGP for Periodic Time = 10, 20 & 30…………………………………..53

4.30 Average delivery ratio of AGP for Periodic

Time = 10, 20 & 30……………………………………………………..54


x

LIST OF ABBREVIATIONS

1. WSN Wireless sensor networks

2. AD Ad-hoc Networks

3. GSP Gossip based sleep protocol

4. RAW Random asynchronous wakeup protocol

5. Fc Forwarding candidates

6. Sc Sleep candidates

7. Ec Current energy

8. Eavg Average remaining energy

9. RST Random sleep time

10. SLP Sleep period

11. PT Periodic time

12. Gp Gossip probability

13. Sp Sleep probability

14. Rn Rear nodes

15. Fn Front nodes

16. Nn1 Neighboring nodes

17. Nn2 Neighboring nodes and rear nodes

18. SL Sender list

19. D2D Distance to destination


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CHAPTER I

INTRODUCTION

With recent technological advances, Wireless Sensor Network (WSN) has

been integrated with the existing network infrastructure and deployed in diverse

applications in civil and military environments. WSN has no centralized infrastructure

and each node relies on multi-hop relay for communication. The design of a sensor

network includes the deployment of small sensors over a network to monitor a specific

event. When a sensor node discovers a particular event, it propagates the gathered data

to the sink. This data should pass through the intermediate sensor nodes, which

indicates that sensor networks rely heavily on a network-wide broadcast service for

propagation of data.

The sensor networks are usually employed in the field of wildlife monitoring,

disaster relief operations etc., which result in random topology in almost all the cases.

These sensors are deployed in large areas and moreover they have a very limited

transmission range. This requires the necessity of one to one transmission resulting in

the deterioration of battery life. This limited battery life is a principal objective in the

design of WSNs due to the difficulty and high cost associated with replacing or

recharging exhausted batteries in a deployed network. A lot of on-going research is

being carried out in order to improve the lifetime of the sensor networks. Hence, we

investigate in improving the energy efficiency of the network by incorporating the

sleep mode of sensors and the routing protocols.


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Initially, flooding mechanisms were used to propagate messages to all the

nodes in the sensor network. Flooding is a simple routing protocol in which every

incoming packet is sent through every outgoing link. Eventually every part of the

network receives the message. Flooding is broadly classified into naïve and controlled

flooding [3]. In the case of naive flooding, each node upon receiving a packet

rebroadcasts the message to all its neighbors resulting in redundant transmissions.

Controlled flooding could partially overcome this problem by keeping track of the

node ids that have already transmitted the packet. Thus, a node broadcasts a packet

only if it has not done so previously. Though it does not forward the same packet,

there is a very high probability that each node receives multiple copies of the same

message. Flooding incurs unnecessary propagation of messages thereby increasing the

redundancy and reducing the lifetime of the network.

Figure 1.1: Naïve flooding mechanism

SINK


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Figure 1.2: Controlled flooding mechanism

The concept of gossiping was introduced to overcome the flooding overhead.

Gossiping limits the propagation of messages as some of the nodes are required to

discard the message instead of forwarding it. The basic concept behind the gossip

protocol is to spread the messages in a rumor like fashion such that each individual

node repeatedly exchanges data with a few randomly selected neighbors causing

information to eventually propagate through the system. The nodes decide whether to

forward or discard the message based on a certain probability. The main advantage of

a gossip based approach is that it provides scalability for communication in large

networks.

Despite optimizations in protocols that use gossiping, all the nodes continue to

keep listening even if there is no data traffic or message transmission. This results in

unnecessary power consumption and significantly reduces the lifetime of the network.

In this thesis, we propose a source based approach to improve the energy efficiency by

SINK


4

introducing the concept of sleep probability. This protocol reduces the number of

nodes that are awake in the network which in turn reduces the energy consumption

considerably. The source decides the number of nodes that have to stay awake or fall

asleep based on the average remaining energy of the nodes. The nodes that are

deduced as forwarding candidates stay awake to gossip the message and the sleep

candidates fall asleep. The Gp and Sp varies with each and every transmission as Gp

and Sp are dynamically decided based on the number of neighboring nodes for Gp, the

number of neighboring nodes and remaining energy for Sp. Though both Gp and Sp are

dynamically calculated they both are independent of each other. The determination of

Gp does not affect the Sp and vice versa. When Gp and Sp are coupled the number of

redundant messages, total number of nodes participating and total number of messages

exchanged can be significantly reduced. Simulations are carried out to study the effect

of AGP using simulation parameters such as delivery ratio, latency, redundant

messages, number of messages exchanged, total number of nodes participating and

energy consumed.

The two major contributions to this thesis are

1. Firstly, the most significant contribution is choosing the forwarding candidates

(Fc) from the neighboring nodes in order to reduce the number of nodes

transmitting the message, secondly we determine the formulas for Gp and Sp

making both gossip and sleep protocols independent of each other.


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2. Simulations are carried out to compare the results of gossip1(p,k), flooding and

AGP in the CSIM environment. All the three protocols are implemented and

results are extracted to compare and analyze.

The remainder of this thesis is organized as follows. In chapter II we present a

brief overview of the background and related works. Chapter III deals with the details

of Adaptive gossip-based sleep protocol. Performance evaluation and simulation

results are presented in Chapter IV. Finally we discuss the conclusion and future work

in Chapter V.


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CHAPTER II

BACKGROUND AND RELATED WORK

This chapter focuses on the existing works of gossiping and flooding. First,

we give a brief description regarding the concept of gossiping and its shortcomings in

ad hoc networks. Then we illustrate the need for gossiping and the previous work

related to energy aware gossiping in WSNs. Finally, we deal with the prior gossip and

sleep protocols for achieving energy efficiency in WSNs. We describe Gossip-based

ad hoc routing [1], Smartgossip [2], Parametric Probabilistic sensor network routing

[3], Gossip based sleep protocol(GSP) [4] and Random asynchronous wakeup protocol

[12] respectively.

2.1 Gossip-Based Ad Hoc Routing

The source-based gossiping approach is a broadcasting protocol that was

introduced to disseminate information in a wireless sensor network. In the case of

gossiping protocol, a node decides whether to forward or discard a packet upon

receiving it. Gossip protocols are probability based and hence if the node decides to

forward the packet, it relays it with a probability p else discards it with a probability 1-

p [1]. This version of gossiping is called gossip1(p). The basic idea of the gossip1(p)

protocol underwent many modifications bringing out various versions such as the

gossip1(p,k) and gossip3(p,k,m). In the case of gossip1(p,k), messages are broadcast

with a probability 1 for the first k hops and then gossip with a probability p.


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Gossip3(p,k,m) is similar to the gossip1(p,k) technique with the addition that the

sender node waits until it receives the sent message from at least m neighbors within a

certain timeout period. If the sender node receives the same message from m

neighbors within the timeout period, it does not rebroadcast the message in order to

avoid redundant transmissions. Though this method reduces the number of

retransmissions, it does not take the factor of energy into consideration since each

individual node retransmits with probability p.

Figure 2.1: An example of gossip1(p,k) with hop count k=2


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2.2 SmartGossip: An Improved Randomized Broadcast Protocol for Sensor

Networks

SmartGossip [2] is another broadcasting protocol in which the author focuses

on four performance metrics namely coverage, energy efficiency, per hop latency and

overhead for various randomized broadcast protocols on a sensor network.

SmartGossip improves the performance of a sensor network since local information

can be exchanged either actively or passively thus making the system intelligent. In

order to achieve efficient exchange of local information, the author converts the

synchronous protocol into an asynchronous setting as it allows overhearing. With the

asynchronous setting, the author modifies gossip1(p,k), gossip3(p,k,m), push&pull

and SPIN protocol by introducing timers into the network protocol instead of hop

count. The probability with which the nodes transmit a message depends on the timer

value and each node waits for a particular timeout period to forward the received

message. If a node overhears the same message within the timeout interval, it does not

retransmit the message instead stays idle. SmartGossip(γ,T, µ1 ,µ2 , σ, δ) is arrived at

by analyzing the strengths and weaknesses of gossip1(p, T, µ), gossip3 (p,k, µ1 ,µ2),

spin-1(µ,k) and push&pull (p, T, µ1, µ2 ) protocol. SmartGossip ensures that the

network is energy efficient with minimum redundancy as it is a blend of gossip3 (p,k,

µ1 ,µ2), spin-1(µ,k) and push&pull (p, T, µ1, µ2 ). Although the Smartgossip reduces

the overall energy consumption by decreasing the number of retransmissions, it still

requires all the nodes to stay awake throughout the lifetime of the network thereby

resulting in unnecessary energy expenditure.


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2.3 Parametric Probabilistic Sensor Network Routing

When considering the efficiency of routing, it is believed that the overall

network is benefited significantly when the drawbacks of mis-informed nodes and

variable network topologies are overcome. The Parametric Probabilistic Sensor

Network Routing [3] is one such light-weight multi-path approach where an

intermediate sensor decides to forward a message with a certain probability which in

turn depends on various parameters. This substantially improves the performance of

previously used controlled flooding methods by involving retransmission as a function

of several parameters. Each node upon receiving a packet retransmits the packet to its

neighbors with a certain probability. There are two variations in Parametric

Probabilistic Sensor Network routing namely the Destination Attractor and the

Directed Transmission. Using this new technology the author claims to prove that

multi-path methods outperform single-path methods in the presence of noise. The

dependence of retransmission probability on network information parameters helps to

create very robust routing protocols in sensor networks.

2.4 Gossip-Based Sleep Protocol for Energy Efficient Routing

The energy constraint in wireless ad hoc and sensor networks calls for an

extension of routing methods. The primary cause for this apparent loss of energy is

that all the nodes keep listening even if the network is free of traffic. This reduces the

lifetime of the nodes which in turn reduces the lifetime of the network.


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T 2T 3T

T

Figure 2.2 (a): Synchronous GSP and Figure 2.2 (b): Asynchronous GSP [4]

Node 1

Node 1

Node 2

Node 2

Node 3

Node 3


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A lot of research is going on to make ad hoc and sensor networks as energy

efficient as possible. But, the Gossip-based sleep protocol (GSP) clearly distinguishes

itself from the rest by considering the factors simplicity, scalability and connectivity of

the network. Thus, Gossip-based sleep protocol [4] improves the energy efficiency of

the network by employing sleep mode to the nodes. With this GSP the nodes that do

not choose to participate get into sleep mode with a probability p and wake up after a

specific period. This process is repeated throughout the lifetime of the network. The

major assumption in the paper is that if p is the gossip sleep probability then 1-p is

probability for the nodes that are awake to transmit the message. This shows that

almost all the nodes stay connected without affecting the network connectivity. GSP is

designed for both synchronous (GSP1) and asynchronous networks (GSP2). Thus by

introducing sleep mode, the total energy consumption of the network can be reduced

thereby increasing the lifetime of the network.

2.5 Random Asynchronous Wakeup Protocol

Random Asynchronous Wakeup protocol [13] is a power saving technique in

which nodes make adaptive decisions whether to sleep or to stay awake. In this wake

up protocol, routing is based on forwarding sets and a random wake up scheme. This

paper focuses on high density networks where several paths exist between the source

and destination to employ the RAW protocol. Routing in this protocol is based

primarily on two kinds of sets namely, the Neighbor set and Forwarding set. The node

maintains local information about its two hop neighboring nodes using which it selects


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the next node to forward the messages to the sink. The author again discusses two

cases namely; the Hop-based forwarding candidate set (h-FCS) and greedy

forwarding. In h-FCS the sender randomly chooses a node among its one hop

neighbors to forward the message whereas in greedy forwarding the nodes takes into

account the geographic distance to forward the message to the node that is closest to

the destination. Another significant contribution of this paper is the random wake up

scheme where the nodes sleep and wake up at random time intervals within a fixed

time-slot. This enables the network to maintain connectivity all through network

lifetime and also reduces the latency significantly.

2.6 Summary

The most significant limitation with basic gossiping, SmartGossip, RAW and

parametric probabilistic routing is that all the nodes deployed in the network keep

listening all the time which is undesirable to achieve energy efficiency. Moreover in

the Gossip-based sleep protocol the potential disadvantage is that the packets may go

through longer paths resulting in more energy consumption. This reduces network

throughput and increases end to end delay. Moreover the nodes choose either to sleep

within the synchronous time (GSP1) interval or to stay awake. There is higher chance

that more than one neighbor of the sender node may sleep at the same time leading to

message loss. The other (GSP2) chooses asynchronous time intervals to sleep where

the sleep time is also not constant and hence probability that those nodes might sleep

for a longer period of time resulting in no connectivity is significantly high. Thus it is


13

essential to achieve minimum energy consumption with minimum participation of

nodes. It is also necessary to maintain the connectivity between the nodes in the

network when we reduce the number of nodes participating in a network broadcast.


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CHAPTER III

AN ADAPTIVE GOSSIP PROTOCOL FOR IMPROVING

COMMUNICATION PERFORMANCE IN WSN

In this chapter, we propose an Adaptive Gossip Protocol, called AGP, for

improving the energy efficiency in WSNs. We first briefly introduce the problem

definition and motivation. Then we suggest the system model and initial deployment

procedures. Finally the methodologies for calculating major system parameters (i.e.,

Fc, Gp and Sp) are discussed.

3.1 Problem definition and motivation

In the current gossip protocols, most of the nodes participate in forwarding

every message received. When a sender node forwards a message, it chooses the

specified probability for gossiping the message without taking any other parameters

into consideration [1]. Moreover all the nodes in the network keep listening till the last

message reaches the sink. Thus every node participates in receiving all incoming

messages although in some cases it can restrict itself from forwarding the received

packet to its neighboring nodes. Therefore, each sender node forwards a copy of the

message only once but the receiver node might receive multiple copies resulting in

redundancy. Hence there is very limited chance that a node might not receive a

message packet more than once. From this we infer that there are large amounts of

energy wastage as most of the nodes participate in all sending and receiving thereby

reducing the lifetime of the network. Hence, gossiping would also act very similar to


15

flooding as all the nodes would participate in the transmission thus incurring very high

redundancy and energy consumption.

Thus in order to achieve energy efficiency it is essential to reduce the number

of nodes participating in the transmission of messages. This can be done by selecting a

certain set of nodes to forward the message. This would in turn reduce the total

number of messages exchanged during transmission thereby reducing the number of

redundant messages received at every hop and at the sink. Since it is unnecessary for

at each node and if the nodes that do not participate in the transmission are made to

sleep, then a reasonable delivery ratio can be achieved with minimum energy

consumption.

3.2 System model

In this thesis we propose an Adaptive gossip protocol in which each node is

chosen to either gossip or sleep based on the average remaining energy of the

neighboring nodes. The forwarding candidates are the nodes that are selected to gossip

the message. The Gp is adaptively generated by the sender node based on its remaining

energy. The Sleep candidates (Sc) are the nodes that are assigned to sleep. These nodes

wake up after a specified sleep time period to take part in the next transmission. Thus,

we focus on reducing the number of nodes participating in message forwarding

thereby reducing the total number of messages exchanged, the number of redundant

messages and energy consumption. Moreover in this mechanism both gossip and sleep

protocols are independent of each other. Unlike existing methodologies, if the gossip


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probability is p then the sleep probability is not taken as 1-p. The sleep probability is

calculated by the receiver node based on its remaining energy (Ec) and number of

neighboring nodes (Nn). Thus, the entire functionality of the network such as

calculating the gossip and sleep probabilities, forwarding and sleep candidates is

dependent on the number of Nn and their Ec.

T 2T 3T

Figure 3.1: Random sleep periods

The Nodes are randomly deployed in a rectangular network and are made to

wake up at periodic time intervals at which point each node chooses a random time to

sleep before the next periodic clock tick. Although, the SLP (i.e. the time for which

the node sleeps) is fixed for the entire network, each node individually chooses the

time at which it should sleep. The node wakes up after the specified sleep interval and

NODE 1

NODE 2

NODE 3


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stays awake until the next periodic clock tick. At each periodic clock tick, the nodes

keep repeating the same process of choosing a RST at which the node should sleep

until an event is detected. Initially when the network is deployed, the sink voluntarily

sends one hop messages to all its one hop neighbors with the ‘distance to destination’

field of the control message packet assigned to a value of ‘Zero’. The D2D field is

initially set to zero for all nodes in the network. The field value is modified when a

node receives D2D information from one of its one hop neighbors. When a node

receives D2D information from its one hop neighbor, it modifies the field value by

adding one to the value being sent by the sender node only if its D2D value is set to 0

(i.e. the D2D field has not been modified before). When the D2D field value has been

set for all the nodes, each node is aware of its ‘distance to destination’ in terms of hop

count. When a node P receives a packet from one of its neighboring nodes, it first

checks the ‘distance to destination’ field. If the difference between the current node’s

D2D value and the received value is 1then P adds the sender node to the list of rear

nodes. On the other hand, if the difference between the current node’s D2D value and

the received value is -1, the sender node is added to the list of front nodes. If neither of

the aforementioned criteria is met, the sender node is added to the list of neighboring

nodes for node P where the received value and current value are same. Therefore, Rn

has all nodes whose values are one less than P, Nn1 has nodes whose values are same

as P and Fn has nodes whose values are one greater than P. In order to calculate the Fc

nodes using we combine both Nn1 and Rn to create the new Nn2. Thus each node

maintains four lists namely the rear node list, front node list and the neighbor node


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list1 and neighbor node list2 which are used for message transmission in the network.

Basically, the forwarding candidates and the sleep candidates are chosen from this

neighboring node2 list. Furthermore, at every periodic clock tick, each of the nodes in

Nn2 sends its remaining energy value to its front nodes and neighboring nodes. This in

turn helps to determine the forwarding and sleep candidates.

Figure 3.2: Wireless sensor network

Source

Node

Neighboring

Node

SINK


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3.3 An adaptive gossip protocol

3.3.1 Generating forwarding candidate nodes

When an event is generated at the source node, the sender node determines the

FC and Gp. However, only the Gp determines if the message is to be transmitted by the

current forwarding candidate. Sleep probability indicates how much time the node

should sleep when the node is chosen as a Sc. Firstly, the FC is determined based on

the remaining energy of the neighboring nodes. The sender node takes the average of

remaining energy of its nodes in the Nn2 and compares it with the remaining energy of

each of its neighboring node. If the remaining energy of a neighboring node is greater

than the average remaining energy of all the neighboring nodes, then that particular

node is chosen as a forwarding candidate. This process is repeated for all the nodes in

Nn2list of the sender and thus the Fc list is created.

Moreover if a node has only one rear node then the forwarding candidate is

always the same node and this node should stay awake throughout the lifetime of the

network except for the RST. Furthermore, each node maintains a sender node list

where it stores the entire node ID’s from which it received the messages. The

receiving node eliminates these node ID’s from the neighboring list while calculating

forwarding candidates. This is because a node can send only one copy of a specific

message ID and incase if the same sender node is chosen as the Fc by the receiving

node then the message is discarded. Hence, the neighboring node is first reduced to a

list that hold capable candidates and then the Fc are calculated. Thus the concept of Fc


20

helps to select only a few nodes to participate in forwarding a packet thereby

eliminating the need for all nodes to stay active throughout the lifetime of the network.

3.3.2 Determining gossip probability

When a sender node chooses its Fc, it transmits the message to the selected

nodes at a random gossip probability. The sender gossips the packet along with

information about the captured event. The Gp at the sender node is determined

adaptively based on the number of neighboring nodes and it indicates the percentage

of messages that should compulsorily reach the forwarding candidates. The gossip

probability is calculated by taking the reciprocal of the number of forwarding

candidates and raising it to the power of reciprocal of its neighboring nodes. Since we

calculate the gossip probability for one current node from a set of forwarding

candidates, we arrive at a part of the formula - 1/Fc. This is then raised to the power of

1 over number of neighboring nodes, since the sender can gossip with a lower gossip

probability when the number of neighboring nodes is high and vice versa. Thus, as the

number of nodes increases, the Gp of the current node decreases. Therefore the source

node transmits the message with a suitable gossip probability to its neighboring nodes.

The forwarding candidate after transmitting the message double’s their RST and sleep

to conserve energy. On the other hand, neighboring nodes which are not chosen as

forwarding candidates fall asleep based on the Sp which is determined by each

individual node. Based on the Sp, the sleep candidates need to sleep for a particular


21

time within the time slot. After the sleep period, the node wakes up and stays awake

for the rest of the time interval.

The gossip probability is calculated using

Gp - Gossip Probability

Fc - Forwarding Candidates

Nn - Neighboring nodes

3.3.3 Determining sleep probability

The Sp is also calculated adaptively by the receiving node based on the

remaining energy and neighboring nodes of the current node. The Sp indicates how

much time the nodes should sleep if it’s a sleep candidate. The sleep probability is

calculated by taking the reciprocal of the remaining energy of current node and raising

it to the power of reciprocal of its neighboring nodes. This is done to achieve

minimum sleep probability when the energy of the node is high and vice versa. Thus,

as the remaining energy decreases the sleep probability of the current node increases.

After the sleep period, the node wakes up and stays awake for the rest of the time

interval.

The sleep probability is calculated using

Gp = (1/Fc) (1/N

n2)

Sp = (1/Ec) (1/ N

n2)


22

Sp - Sleep Probability

Ec - Current Energy

Nn - Neighboring nodes

The Adaptive gossip based sleep protocol thus reduces the total number of

nodes participating in any transmission thereby increasing the lifetime of the network.

This methodology also reduces the number of redundant messages as only a limited

number of nodes are participating in a message transmission. Moreover all the nodes

do not stay active throughout the lifetime of the network. Each node takes its turn for

both gossip and sleep which helps in preserving the energy at each node for a longer

time period.


23

3.4 Pseudocode

curr_nde - current node

Fc - forwarding candidates

Fs - nodes chosen to sleep

Ec - current energy

EAvg - average remaining energy

Gp - gossip probability

Sp - sleep probability

RST - random sleep time

SLP - Sleep Period

PT –Periodic Time

SL - Sender List

Nn – neighboring nodes

Nrn – remaining neighboring nodes

A) /* when an event occurs */

Upon receiving a packet

If (curr_nde = Fc)

{

Continue to determine Fc ;

If (curr_nde.Nn[i] == sender node)

{

No participation in gossiping

}

If (curr_nde.Nn[i] != sender node)

{

Nrn = Nrn +1;

n

EAvg = ∑ Nrn->Ec[i]

i=0 ;

n

for (i=0;i<n;i++)

{

If (Nrn->Ec[i] >= EAvg) then

Fc = Fc+1;

Store the respective node id;

}

}

Else

{

Sc = Sc+1;

Store the respective node id;


24

}

B) Upon receiving a packet

If (curr_nde == Fc)

{

Continue to determine Gp

Gp = ((1/Fc)^(1/ Nn))

Broadcast the message using the probability Gp

SLP= 2*RST

}

Wake up and stay idle until next message is received

C) Upon receiving a packet

If (curr_nde = Sc)

{

Continue to determine Sp

Sp = ((1/Ec)^(1/Nn))

SLP = Sp time of PT

}

Wake up and stay awake until next message is received

In the above pseudo code part A) represents the calculation of the forwarding

candidate nodes, part B) represents the calculation of Gossip probability and part C)

represents the sleep probability.


25

CHAPTER IV

PERFORMANCE EVALUATION

In this chapter, we present simulation results of AGP and compare with the

existing methodologies, gossip1(p,k) and flooding. First, we describe the simulation

testbed and simulation parameters. . Then, we analyze the performance cons and pros

of each scheme in terms of delivery ratio, latency, number of redundant messages,

total number of messages exchanges, total number of nodes participated and energy

consumed.

4.1 Simulation Testbed and Performance Comparison

We develop the three protocols AGP, gossip1(p,k) and flooding using the C

Simulator (CSIM). The size of the network is 250x200 m2

with a total of 500 nodes

deployed randomly. In all three implementations, the deployment must take place such

that the number of isolated nodes is kept to a minimum. In the case of gossip1(p,k)

and flooding implementations each node has a one-hop range of 15 and moreover due

to the dense deployment of nodes each node has at least one neighboring node. The

sink is located at the (0,0) location and the source is located at the top right corner in

order to test if the message is propagating through a longer path to reach the sink. In

order to remove any anomalies in the results and to get a broader outlook on how the

performance metrics are varying, the results are extracted by varying the gossip

probability and number of packets sent. In the case of gossip1(p,k) algorithm, the

nodes gossip with a probability of 1 for the first k hops and with a probability of p


26

(gossip probability specified by the user) from the k+1th hop. The gossip1(p,k) is

implemented by keeping the hop count value constant and varying the gossip

probability from 0.60 to 0.95 in intervals of 0.05. The performance metrics are

extracted for each of these probabilities by varying the number of packets sent from

10-100. The graphs are plotted for each of these performance metrics and similarly the

same is followed for the flooding algorithm. In the case of the flooding algorithm,

both p and k are made 1 so that all nodes gossip with a probability of 1 irrespective of

the hop count. Thus, the performance metrics for the gossip1(p,k) and flooding are

extracted and is used for comparison against the result of the Adaptive gossip

protocol. The following table describes the network parameters that is used for

simulating gossip1(p,k) and flooding.

Parameter Value

Number of nodes 500

Network Size 250 × 200 m2

Simulation Time 100sec

Communication Range 15m

Initial energy of each node 100 J

Receiving power consumption 0.01 J

Transmitting power consumption 0.02J

Probability 0.60-1

Hop Count (K) 4

Packet size 512Bytes

Mobility No Mobility

Bit rate 2Mbps

Figure 4.1: Parameter table for gossip1(p,k) and flooding


27

For the proposed protocol each node has a one-hop range of minimum 22 so

that when we choose the forwarding candidates each node has at least one node to

transmit the message. The results for the proposed protocol are extracted by varying

the number of packets sent as gossip probability and sleep probability are calculated

adaptively by the nodes. The performance metrics are extracted for each of this

randomly generated gossip and sleep probabilities by varying the number of packets

sent from 10-100. The node which has only one forwarding candidate would

compulsorily gossip with the probability 1. Moreover the network has periodic

intervals for every 10ms and each node chooses to sleep randomly for any 0.1ms. The

nodes double this random sleep period (0.2) and sleep after gossiping the message.

Parameter Value

Number of nodes 500

Network Size 250 × 200 m2

Simulation Time 100sec

Communication Range 22m

Initial energy of each node 100 J

Receiving power consumption 0.01 J

Transmitting power consumption 0.02J

Sleep Power consumption 0.0005J

Random Gossip Probability 0-1

Random Sleep Probability 0-1

Packet size 512Bytes

Mobility No Mobility

Bit rate 2Mbps

Periodic Time 10ms

Random Sleep Time 0.1ms

Figure 4.2: Parameter Table for AGP


28

4.2 Simulation Parameters

In this section we define the performance metrics considered for our

simulation.

1. Average remaining energy

Average remaining energy is the value obtained when the remaining energies

of all the nodes in the network are summed up and divided by the total number

of nodes.

2. Average redundancy

Redundancy is the total number of duplicate messages reached at the sink.

Total number of redundant packets divided by number of packets sent gives us

the Average redundancy.

3. Average messages exchanged

The total number of messages exchanged for an event is calculated by

summing up the sent messages at each node. Therefore, average messages

exchanged is the total sum by number of nodes in the network.

4. Average latency

Latency is the measure of time delay which is obtained by finding the

difference of packet received time and packet sent number. This value by

number of packets sent gives the average latency.


29

5. Total number of nodes participated

It is count of total number of nodes participated in transmitting certain number

of packets. Even if a node participates in one single transmission it is added to

the list.

6. Average delivery ratio

The ratio of number of packets sent to the number of packets received is called

delivery ratio. The value eliminating the duplicate count gives the count of

total number of packets delivered.


30

4.3 Simulation Results

4.3.1 Random network deployment

In the simulation that we are running, 500 nodes are deployed randomly

throughout a 250x200m2 area network. The destination sink is at (0,0) and the source

is placed at the top right corner of the network. Whenever a node detects an event, it

should transmit the message to the sink through the intermediate nodes. It is essential

that we maintain proper connectivity while deploying the nodes. Hence we try and

make sure that each node has at least one neighboring node to which it can forward the

messages. A neighboring node is any node that is deployed within the one-hop range

of the node. Hence we control the random deployment of the network by distributing

20 nodes for every 10m. The source node used in the simulation is node number 486

and the co-ordinates are (246,196).

Figure 4.3: Random network deployment

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DEPLOYMENT depl


31

4.3.2 Average remaining energy comparison

Energy efficiency is one of the most important parameters that are taken into

consideration when designing a network or while choosing transmission algorithm for

the network. WSN’s operate primarily on a battery source which has limited energy

capacity. Hence, in order to increase the lifetime of a network, the energy

consumption of a WSN needs to be minimized as much as possible. In the following

section, we make a comparison between the three protocols to determine the most

energy efficient protocol. The average remaining energy of each node in the network

is plotted against the number of packets sent.

The energy efficiency of a system is affected by various factors of the network.

One of the most prominent factors is the total number of nodes participating in the

message transmission. If the number of nodes participating in the message

transmission increases, then the energy expenditure is also very high. Also, if the

nodes in the network are always awake and are always snooping for new messages,

then the energy consumption rapidly increases. Energy efficiency of flooding is very

low because; almost all the nodes in the network take part in the transmission. Though

gossip1(p,k) made efforts to overcome this, the energy consumed still remained high.

This is because the main concentration of gossip1(p,k) was to limit the nodes from

participating but was not to completely terminate the participation.


32

Based on the graphs we can infer that the energy consumption of AGP is

relatively low when compared to gossip1(p,k) and flooding. This is primarily because

AGP incorporates the sleep protocol, thereby reducing constant snooping for packets.

Also, the energy consumption in the sleep state is a lot lesser than the idle state for a

node. Furthermore, since only a few nodes are chosen as forwarding candidates, the

number of nodes participating in the transmission is also reduced. Thus from the

graphs we can say that in the case of gossip1(p,k) as the probability increases the

remaining energy level is more or less similar to flooding. But in the case of AGP as

the probability is random the energy level remains stable. The comparison of three

protocols evidently shows that AGP holds a better improvement in energy preserving.

Figure 4.4: Average remaining energy of gossip1 (p,k) where Gp = 0.65,flooding

Gp = 1 and AGP comparison

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AVERAGE REMAINING ENERGY gossip1(p,k) Gp = 0.65AVERAGE REMAINING ENERGY Flooding Gp=1AVERAGE REMAINING ENERGY AGP Gp = random

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Figure 4.5: Average remaining energy of gossip1 (p,k) where Gp = 0.75,flooding Gp

= 1 and AGP comparison



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4.3.3 Average redundancy comparison

In this section, we compare the three protocols based on the number of

redundant packets received at the destination according to each algorithm. In the

current simulation, the average redundancy ratio is calculated as the ratio of duplicate

packets reached at the destination to the number of packets sent. Once this is

determined, the average redundancy ratio is plotted against the number of packets

sent. In the following graphs, it can be seen that the redundancy for flooding is the

highest since all nodes that receive the message re-broadcast it without any

constraints. Although this provides 100% delivery ratio, it is not a good practice to

achieve 100% delivery ratio with maximum loss of energy. Although gossiping

reduces the number of redundant messages by reducing the number of nodes

participating in the transmission, the redundancy factor is still high compared to AGP.

It can be seen that the average redundancy ratio for flooding is 5 i.e. for each packet

sent out from the source, the average number redundant messages being transmitted is

the same number. Similarly the gossip1(p,k) redundancy values varies from 2-5 based

on the gossip probability being specified.

However when we take a look at AGP, the average redundancy factor is

always maintained below 1. This is primarily because AGP limits the number of nodes

that take part in forwarding a message towards the sink. Moreover, sleep is

implemented twice apart from sleep probability one is after gossiping a node doubles

its RST and other is each node sleeps for once within every ‘n’ units of time.


36

Moreover a node can transmit one message it only once, hence the redundant

messages reaching these nodes are never propagated again. Thus, based on the above

graphs it can be seen that AGP has a better implementation to contain the number of

redundant packets thereby improving the lifetime of the network significantly.

Figure 4.8: Average redundancy of gossip1 (p,k) where Gp = 0.65, flooding Gp = 1

and AGP comparison

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AVERAGE REDUNDANCY gossip1(p,k) Gp = 0.65AVERAGE REDUNDANCY Flooding Gp = 1AVERAGE REDUNDANCY AGP Gp = random

AV

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and AGP comparison


and AGP comparison

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and AGP comparison

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4.3.4 Average latency comparison

In this section, we compare the average latencies of the data packets for the

three methodologies under discussion namely – gossip1 (p,k), flooding and the AGP.

In our simulations, the average latency is found for every variation in the number of

packets being sent from the source to the sink. Using these values, the graph is plotted

to project the variation in average latencies for an increase in the number of packets.

The above graphs show the values of average latencies for all the three methodologies

with the gossip probability being varied from 0.6-0.95 in the case of gossip1 (p,k), 1 in

the case of flooding and random in AGP.

The latency for a network is based on the path that the data packets have to

travel in order to reach the sink. Thus, if the distance travelled by the data packets is

lesser, then the latency of the network is also less. Furthermore, latency is also

affected by the redundancy in the network since latency increases with an increase in

the number of packets being transmitted.

From the above graphs, we can infer that the average latency for flooding is the

highest since more number of packets are exchanged from the source to destination. In

the case of gossip1(p,k) latency increases with an increase in the gossip probability.

The latency is almost as low as that of AGP when the gossip probability is 0.6 but it

increases drastically with an increase in the probability value. In the case of AGP the

latency is exponentially very low when compared to gossip1(p,k) and flooding. This is

because the total number of nodes participating in transmitting the message is


40

considerable less and before transmitting the message from one hop to the next hop

there is a proper path assigned by the sender node.

Figure 4.12: Average latency of gossip1 (p,k) where Gp = 0.65, flooding Gp = 1 and

AGP comparison


AGP comparison

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Average Latency gossip1(p,k) Gp = 0.65Average Latency Flooding Gp = 1Average Latency AGP Gp = random

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AGP comparison


AGP comparison

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4.3.5 Average number of messages exchanged comparison

In this section, we compare between the different protocols based on the

number of messages exchanged for each transmission. The total number of messages

exchanged in the network affects the parameters like energy efficiency, latency, and

redundancy. In the case of gossip1(p,k) and flooding , the number of messages

exchanged is very high since almost all the nodes in the network participate in

forwarding the message. Although gossip1(p,k) reduces the number of forwarding

nodes based on probability, in some cases when given gossip probability is high there

is a very high probability that all the nodes participate. Thus from the graphs we can

infer that the total number of messages exchanged in gossip1(p,k) and flooding is

twice that of AGP. This is because total number of participating nodes is directly

proportional to the number of messages exchanged. When the number of nodes

participating is high the messages exchanged is also high and vice versa.


43

Figure 4.16: Average number of messages exchanged of gossip1 (p,k) where Gp =

0.65, flooding Gp = 1 and AGP comparison



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AVERAGE MESSAGE EXCHANGED gossip1(p,k) Gp = 0.65AVERAGE MESSAGE EXCHANGED Flooding Gp = 1AVERAGE MESSAGE EXCHANGED AGP Gp = random

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4.3.6 Total number of nodes participated comparison

In this section, we compare the results of parameter total number of nodes

participated in a data transmission. It can be seen that flooding and gossip1(p,k)

algorithms have almost all the nodes in the network participating in every

transmission. This reduces the energy efficiency by a large amount. Flooding and

gossip1(p,k) algorithms have no mechanism to choose a forwarding candidate to

transmit the messages to the sink. However, since AGP incorporates the sleep protocol

and chooses only a few forwarding candidates to transmit messages, the number of

nodes participating in any transmission is half the number when compared to the other

two protocols. This helps improve the energy efficiency, latency and redundancy of

the network.

Figure 4.20: Total number of nodes participated for gossip1 (p,k) where Gp =0.60-


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NODES PARTICIPATED gossip1(p,k) Gp = 0.6 to 0.95NODES PARTICIPATED FLOODING Gp = 1NODES PARTICIPATED AGP Gp = random

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46

4.3.7 Average delivery ratio comparison

Based on the graphs of delivery ratio it is evident that when the probability is

high the number of messages delivered at the sink is also high. It can be seen that the

delivery ratio for the flooding algorithm is at 100% irrespective of the number of

packets being generated at the source. When the probability at which the node

transmits is high there is a higher chance that more number of message are exchanged

between the nodes which improves the delivery ratio immensely but at the cost of very

low energy efficiency. In the case of gossip1(p,k) the delivery ratio increases as the

gossip probability increases. As the delivery ratio increases the energy of the node will

considerably reduce. A high delivery ratio can be achieved only when sufficient

amount of messages are transmitted and this would in turn reduce the energy level of

the node. Hence it is very difficult to achieve a good moderation between these two

parameters. There should be a trade-off if we had to improve either of the two

parameters.

However if we consider the delivery ratio of AGP it also achieves 68%

delivery ratio on an average which is moderate when compared to the delivery ratio

other two protocols. The delivery ratio of AGP is comparatively low because we limit

nearly half the number of nodes from participating in the transmission of messages

and moreover we introduce sleep probability during which the node is inactive. The

node does not receive any messages when it is asleep. There might be a message loss

due to this sleep protocol. Thus AGP achieves moderate delivery ratio with very high

energy efficiency.


47

Figure 4.21: Average delivery ratio for gossip1 (p,k) where Gp =0.65, flooding Gp =

1 and AGP comparison



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4.3.8 Simulation results changing the fixed time intervals

4.3.8.1 Comparison of average remaining energy for fixed intervals 10, 20 and

30 in AGP

Figure 4.25: Average remaining energy of AGP for Periodic Time = 10, 20 & 30

From the above graph it is evident that when a node sleeps for 0.1ms for every

10ms the average remaining energy of the nodes is high when compared to the fixed

time intervals 20ms and 30ms. This is because nodes sleep less frequently in the case

of 20ms and 30ms due to which large number of nodes participate in forwarding a

message. In order to conserve more energy it is essential that nodes sleep more

frequently thereby reducing the number of forwarding candidates. Hence in this thesis

we simulate using 10ms as the fixed time interval and achieve relatively good average

remaining energy when compared to other two protocols.

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AVERAGE REMAINING ENERGY AGP PERIOD = 10AVERAGE REMAINING ENERGY AGP PERIOD = 20AVERAGE REMAINING ENERGY AGP PERIOD = 30

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4.3.8.2 Comparison of average redundancy for fixed intervals 10, 20 and 30 in

AGP

Figure 4.26: Average redundancy of AGP for Periodic Time = 10, 20 & 30

Average redundancy of a transmission is high when more number of messages

is exchanged from source to destination. It is essential that we reduce the average

redundancy in order to conserve energy. In this thesis we analyzed the behavior of

average redundancy by varying the periodic time as 10ms, 20ms and 30ms. As the

time increases more number of nodes happens to participate in the transmission

thereby resulting in large number of message exchanges. In order to control this and

achieve less average redundancy we choose to simulate using 10ms instead of 20ms or

30ms.

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4.3.8.3 Comparison of average latency for fixed intervals 10, 20 and 30 in

AGP

Figure 4.27: Average latency of AGP for Periodic Time = 10, 20 & 30

In the case of latency it is essential that packet transmission consume less

amount of time as latency is directly proportional to energy. When packets sent take

more time to reach the destination in a dense network we can assume that more

number of messages are being exchanged which indirectly leads to more energy loss.

Moreover as the network is highly dense it does not affect the latency though nodes

sleep frequently in the case of 10ms. But when we consider 20ms and 30ms latency is

relatively high as more number of nodes participates in a message transmission. Hence

we decide to use 10ms as the periodic time for the nodes to wake up and choose the

random sleep time. This helps us achieve a comparative delivery ratio with less

latency, less redundancy and more remaining energy.

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4.3.8.4 Comparison of average number of messages exchanged for fixed

intervals 10, 20 and 30 in AGP

Figure 4.28: Average number of messages exchanged in AGP for Periodic Time = 10,

20 & 30

Total number of messages exchanged for a transmission increases when more

number of nodes participate in the transmission. Therefore in order to conserve more

energy it is essential to limit the number of forwarding candidates. By limiting the

forwarding candidates we make the nodes sleep instead of staying idle. Hence if the

nodes sleep frequently within shorter time span it is possible to preserve more energy

with less number of forwarding candidates. Thus we opt 10ms as the periodic time

interval as in the case of 20ms and 30ms the number message exchanges are

comparatively high.

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4.3.8.5 Comparison of total number of nodes participated for fixed intervals

10, 20 and 30 in AGP

Figure 4.29: Total number of nodes participated in AGP for Periodic Time = 10, 20 &

30

In all the existing works almost all the nodes participate in a message

transmission thereby consuming large amount of energy. Thus in AGP we reduce the

number of nodes participating by selecting forwarding candidates through source

based approach and making the remaining nodes sleep. From the above graph we can

infer that total number of nodes participating is comparatively high in the case of

20ms and 30ms whereas in the case of 10ms as the nodes sleep frequently it

considerably limits some of the nodes from forwarding the message. Hence in order to

achieve energy efficiency we choose to simulate using 10ms.

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4.3.8.6 Comparison of average delivery ratio for fixed intervals 10, 20 and 30

in AGP

Figure 4.30: Average delivery ratio of AGP for Periodic Time = 10, 20 & 30

The delivery ratio of AGP is analyzed by varying the fixed time interval. It is

clearly evident that average delivery ratio increases as the fixed time period increases.

The delivery ratio in the case of 20ms and 30ms is relatively high because total

number of nodes participating in the transmission is high. Though there is a

comparative increase in the delivery ratio with the increase in time all the other

parameters have an inverse affect. On an average 10ms, 20ms and 30ms show a

delivery ratio of 68%, 69% and 71%. As the increase in delivery ratio has a very slight

impact and also considering rest of the parameters to achieve energy efficiency we

choose to simulate results for 10ms.

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

DELIVERY RATIO AGP PERIOD = 10



DE

LIV

ER

Y R

AT

IO



55

4.4 Summary

In this section, we summarize our simulation results for various parameters to

get an outlook on the advantages and disadvantages of the three protocols. We analyze

the results based on the six parameters described above by varying the number of

packets sent.

Based on the simulation results of average remaining energy we infer that the

remaining energy values of gossip1(p,k) is similar to the values of AGP when the

gossip probability is around 0.60 and as the gossip probability increases the graph

becomes a linear graph similar to flooding. When we compare the results of Flooding

with AGP we can infer that around 12% more energy is conserved in AGP whereas

when compared to gossip1(p,k) AGP conserves around 3-10% more energy for a

range of gossip probabilities 0.60 to 0.95. Hence AGP comparatively conserves more

energy than the other two existing protocols.

The parameters average redundancy and the average number of messages

exchanged also follow a pattern similar to the remaining energy. We can say that

redundancy and number of messages exchanged are directly proportional to each

other. So when the number of message exchanged between the nodes increases the

redundancy at the sink automatically increases. In the case of flooding all the nodes re-

transmit the messages the number of times it receives. Hence it is very evident that it

has very high message exchanges between the nodes and in turn very high

redundancy. In the case of gossip1(p,k) the number of messages exchanged reduces as

transmission is based on certain specified probability. In the case of AGP we reduce


56

the number of nodes gossiping the message and there is no specific probability that

each node should use. In comparison with AGP, flooding populates 70% more

messages and gossip1(p,k) populates 40% more messages. Therefore, average

redundancy of flooding is 5 times that of AGP and for gossip1(p,k) it (2-5) times

higher than AGP which varies according to gossip probability (0.60-0.95).

The simulation results of average latency imply that flooding takes a very high

working time and gossip1(p,k) also follows a similar latency trend when the gossip

probability gets closer to 1. The latency of AGP is almost negligible when compared

to gossip1(p,k) or flooding and is exponentially lower than either of the two protocols.

For example in the case of transmitting 100 packets from the source to destination

flooding and gossip1(p,k) take around 65 times and 58 times more time than AGP

respectively. The parameter total number of nodes participated in forwarding the

message in the case of AGP is significantly less when compared to flooding and

gossip1(p,k). As more number of messages are populated in the case of flooding and

gossip1(p,k) at one instance most likely all the nodes participate in forwarding the

message. But in the case of AGP as the forwarding candidates are selected and also

gossip probability is random only two thirds of the total nodes participate in

transmitting.

Finally, as we know delivery ratio of flooding is always 100% as all the nodes

participate in forwarding the message. Similarly, in the case of gossip1(p,k) gossip

probability is directly proportional to delivery ratio that is lesser the gossip probability

lesser the delivery ratio and vice versa. In the case of AGP as we limit the number of


57

nodes participating and introduce sleep probability the delivery ratio is comparatively

less. AGP also achieves 68% delivery ratio on an average which is moderate when

compared to the other two protocols.


58

CHAPTER V

CONCLUSION AND FUTURE WORK

In this chapter, we summarize our work and list the possible advancements for

future work.

5.1 Conclusion

The adaptive gossip based sleep protocol is designed to conserve the energy of

the WSN as much as possible without significant data losses. Hence, we need to

moderate between energy efficiency and the delivery ratio of the network. In order to

achieve this, the AGP incorporates the sleep protocol with the gossiping protocol to

minimize energy consumption. The AGP also selects only a few selective forwarding

candidates to forward the data packets down to the sink at each node thereby reducing

the number of nodes participating in the data transmission. This also reduces the

number of redundant packets that are transmitted in the network further conserving the

energy of the network. The main purpose of implementing these concepts in the AGP

was to overcome the existing drawbacks of flooding and gossiping protocols.

The nodes in the network are randomly deployed and the sink is fixed at (0,0).

The source node is chosen such that the data packets have to travel the entire network

to reach the destination. The sender node selects a few neighboring nodes alone to act

as forwarding candidates and assigns the rest of the neighboring nodes to sleep. These

sleeping nodes do not take part in forwarding the data during the sleep period and

hence conserves a lot of energy by not staying awake. The forwarding candidates

forward data based on a gossip probability that is randomly generated and the process


59

is continued until all the messages reach the sink. Furthermore, each node sleeps at a

random time period once every ‘n’ clock ticks. This further improves the energy

efficiency of the network.

Based on the simulation results, it can be seen that the AGP algorithm meets

the design criteria it was meant for. The energy efficiency of the adaptive gossip

protocol is comparatively better than gossip1(p,k) and flooding algorithms in most of

the parameters considered. Furthermore, the number of redundant messages, number

of nodes participating in the transmission and the number of messages exchanged in

the network is also considerably reduced when compared to the other two protocols.

There is also a significant improvement in the latency of the network when comparing

AGP with gossip1(p,k) and flooding algorithms. The delivery ratio is comparatively

less than gossip1(p,k) and flooding algorithm due to the reduction in number of

redundant messages and reducing the number of nodes participating in the

transmission. However, the gain in energy efficiency far outweighs the drop in the

delivery ratio of the system. Hence it can be seen that AGP is a more energy efficient

protocol that has numerous advantages over gossip1(p,k) and flooding such as -

Lower Latency, Lesser number of redundant messages, lesser number of nodes

participating in the transmission, lesser number of messages exchanged and a longer

network lifetime.


60

5.2 Future work

In future, we plan to further improve the existing protocol to increase the

delivery ratio of the system. We know it is very difficult to achieve both high energy

efficiency and high delivery ratio in a network. In order to improve the delivery ratio

of the current network it is essential to modify the network setup to have multiple

sinks instead of one. In that case each node might deliver the packets to the nearest

available sink. In this case, the delivery ratio of the network should increase

substantially while maintaining the same energy efficiency that has been achieved

now. We also plan to test the network with multiple sources working together so that

multiple events are generated at the same time. This will also include dealing with

collisions among packets and arbitration for nodes. The future work can also include

implementing the same network in a mobile sensor network environment and simulate

results. In a mobile sensor network the neighboring nodes will keep changing every

time the node moves. Thus in future we plan to work on the above targets one after the

other to simulate and analyze the results.


61

REFERENCES

[1] Zygmunt J. Haas, Joseph Y. Halpern, Li Li; “Gossip-Based Ad Hoc Routing”;

Proceedings of INFOCOM, 2002.

[2] Ananth V. Kini, Vilas Veeraraghavan, Nikhil Singhal, Steven Weber Drexel;

“SmartGossip: An Improved Randomized Broadcast Protocol for Sensor Networks” in

the Fifth International Conference, 2006.

[3] Christopher L. Barrett, Stephan J. Eidenbenz, Lukas Kroc, Madhav Marathe,

James P. Smith; “Parametric Probabilistic Sensor Network Routing” in WSNA, 2003.

[4] Xiaobing Hou, David Tipper; “Gossip-Based Sleep Protocol (GSP) for Energy

Efficient Routing in Wireless Ad Hoc Networks” in Wireless Communications and

Networking Conference, 2004.

[5] Pradeep Kyasanur, Romit Roy, Choudhury Indranil Gupta; “Smart Gossip: An

Adaptive Gossip-based Broadcasting Service for Sensor Networks”, in Mobile Adhoc

and Sensor Systems (MASS), 2006.

[6] David Braginsky, Deborah Estrin; “Rumor Routing Algorithm for Sensor

Networks”, WSNA, 2002.

[7] S. Murthy, J. J. Garcia-Luna-Aceves; “An efficient routing protocol for wireless

networks,” ACM Balzer Mobile Networks and Applications Journal, 1996.

[8] B. Karp, H. T. Kung; “Greedy perimeter stateless routing (GPSR) for wireless

networks”, in Mobile Computing and Networking (MobiCom), 2000.

[9] S-Y Ni, Y-C Tseng, Y-S Chen, J-P Sheu; “The broadcast storm problem in a

mobile ad hoc network”, in Mobicom, 1999.

[10] W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan; “Energy efficient

communication protocol for wireless microsensor networks”, in IEEE Hawaii

International Conference on System Sciences, 2000.

[11] Y. Xu, J. Heildemann, D. Estrin; “Geography-informed energy conservation for

ad hoc routing”, in proceeding of ACM SIGMOBILE, 2001.


62

[12] Intanagonwiwat C., Govindan R., Estrin D.; “Directed Diffusion: A Scalable and

Robust Communication Paradigm for Sensor Networks” in Mobile Computing and

Networks (MobiCOM), 2000.

[13] Vamsi Paruchuri, Shivakumar Basavaraju, Arjan Durresi, Rajgopal Kannan;

“Random Asynchronous Wakeup Protocol” in BroadNets 2004.

[14] B. Chen, K. Jamieson, H. Balakrishnan, R. Morris; “SPAN: an energy-efficient

coordination algorithm for topology maintenance in ad hoc wireless networks” in

MOBICOM, 2001.

[15] C. Schurgers, V. Tsiatsis, S. Ganeriwal, M. Srivastava; “Optimizing sensor

networks in the energy-latency-density design space” in IEEE Transactions on Mobile

Computing, 2002.

[16] X. Yang, N. H. Vaidya; “Wakeup scheme for sensor networks: Achieving

balance between energy saving and endto-end delay” in Technical report, CSL, 2003.

[17] C. E. Perkins, E. M. Royer; “Ad-hoc on-demand distance vectorrouting,” in IEEE

Workshop on Mobile Computing Systems and Applications, 1999.

[18] Z. Shi, H. Shen; Adaptive Gossip-based Routing Algorithm in IPCCC, 2004.


63

APPENDIX A

CODE IMPLEMENTATION

In this appendix, we present the implementation of AGP using the CSIM. This section

describes an overview of the functions and modules that have been created, executed

and tested for AGP.

void init()

// initializations

//network topology

node[0].x=0;

node[0].y=0;

fprintf(fp1,"%d \n",node[0].x);

fprintf(fp2,"%d \n",node[0].y);

fprintf(fp3,"x=%d y=%d node 0 \n",node[0].x,node[0].y);

for(i=1;i<NUMNODES;i++)

{

if(tcount>=20)

{

tempx1=tempx1+xdist;

tempx2=tempx2+xdist;

tcount=0;

}

randx=uniform(tempx1,tempx2);

randy=uniform(0,NETWORKY);

node[i].x=randx;

fprintf(fp1,"%d \n",node[i].x);

node[i].y=randy;

fprintf(fp2,"%d \n",node[i].y);

fprintf(fp3,"x=%d y=%d node %d \n",node[i].x,node[i].y,i);

tcount++;

}

printf("\nNetwork Established!!!\n");

//The network of dimensions 250x200 was created with 500 nodes deployed in it.

Every 10 units in the x-axis have 20 nodes randomly deployed.


64

void discovery(int n)

{

create("discovery");

printf("\n discovering network at time %lf",clock);

int temp1;

msg *m;

//creating message and populating message details

m = (msg *)do_malloc(sizeof(struct msg));

m->from = n;

m->type = 1;

m->hop_count=0;

hold(TRANS_TIME);

for(temp1=0;temp1<node[n].onehopcount;temp1++)

{

m->to=node[n].onehoplist[temp1];

printf("\n node:%d , loop:%d, value of m->to:%d",n,temp1,m->to);

send(node[m->to].input,(long)m);

}

}

//Create the process discovery in order to gather the one-hop neighbors of each node.

This is essential when we populate the D2D messages from the sink to determine the

front node, rear node and neighboring node list.

void sync(int n)

{

create("sync");

int i,j,rtime;

int temp1;

msg *m3;

hold(10);

while(1)

{

node[i].slept_once=0;

m3 = (msg *)malloc(sizeof(struct msg));

m3->from = n;

m3->type = 3;

m3->data=node[n].myenergy;

m3->hop_count=0;

m3->eventno=0;

for(temp1=0;temp1<node[n].frontcount;temp1++)

{


65

m3->to=node[n].frontlist[temp1];

send(node[m3->to].input,(long)m3);

}

for(temp1=0;temp1<node[n].nbrcount;temp1++)

{

m3->to=node[n].nbrlist[temp1];

send(node[m3->to].input,(long)m3);

}

rtime = (unsigned int)(rand()*n) % SYNTIME;

hold(rtime);

if(node[i].slept_once!=1&& n!=nodeselected)

{

node[n].state = 0;

hold(0.1);

node[n].state = 1;

node[n].myenergy -= S_ENERGY;

hold(SYNTIME - rtime -0.1

}

}

//In the Sync function the network the network has periodic intervals of 10ms. All the

nodes decide to sleep at any random time 0.1 unit within 10 unit of fixed time. This 0.1

unit is chosen using the formula (rand()*n) % SYNTIME where we generate a random

number, multiply with the node number and then take a modulus of 10 for the obtained

value. After sleep each node stays awake until sync period. All nodes send its energy

information to the front nodes and neighboring during every sync period.

void random_event() //generating an even at random node

{

create("event");

int i,events=0,j=0;

printf("\nIn Random event\n");

FILE *fp

while(events<1) //Creating a single event

{

printf("\nBefore hold\n");

hold(50.0);

int nodeselected = 0;

nodeselected = 486;

event_node = nodeselected;

events++;

printf("\nEvent generated at node:%d time:%lf, event

number:%d\n",nodeselected,clock,events);

for(i=1; i<=NO_OF_PACKETS; i++)


66

{

send_msg(nodeselected,nodeselected,0,i,events,1,0);

hold(TRANS_TIME);

}

}

}

//This function is to generate a random event at any node. When the random event is

triggered the node sends specified number of packets to the destination through the

intermediate nodes.

void send_msg(int n, int src, int hop_cnt, int msg_id, int event_no, int fc, int fs)

{

//INCLUDE GOSSIP PROB//////

ifc = (float)(1.0/fc);

ifs = (float)(1.0/(node[n].uniquecount));

gossip_prob = pow(ifc,ifs);

//The node calculates the gossip probability before forwarding the message to its

neighboring nodes. The variable uniquecount has the count of both and rear nodes

and neighboring nodes.

for(i = 0; i < fccounter; i++)

{

if(node[n].uniqueenergy[fclist[i]] >= avg_unique_energy)

{

fc++;

} //if greater than avg energy

}///for

fs = fccounter - fc;

///////////////////////////

m->fc = fc;

m->fs = fs;

if(fccounter==1)

gossip_prob=1;


67

for(i=0; i<fccounter; i++)

{

prob = uniform(0,0.5);

if(prob <= gossip_prob)

{

send=1;

m->to = node[n].uniquelist[fclist[i]];

n1 = m->to;

node[n1].node_status = m->node_status;

if(m->from == src &&

node[event_node].ev[event_no][msg_id] == 0)

{

node[event_node].ev[event_no][msg_id] =

time(NULL);

}

node[n].exchange[m->eventno]++;

hold(TRANS_TIME);

//printf("energy = %lf",node[n].myenergy);

node[n].myenergy = node[n].myenergy - T_ENERGY;

//printf("energy = %lf",node[n].myenergy);

//DOUBLE SLEEP TIME!!!!

if(n!=nodeselected)

{

node[n].state = 0;

node[n].slept_once=1;

hold(T_DELAY);

node[n].state = 1;

}

}

}

}

//The node verifies if the energy of each node in the uniquelist other than the nodes

already forwarded is greater than the average remaining energy obtained. If this

condition is satisfied the node updates the Fc list else updates the Fs list. The node

then generates a random probability compares this probability with the calculated

probability value. If the generated value is less than the calculated value then the node

decides to gossip the message else it decides not to gossip. After gossiping the

message node decides to sleep for T_DELAY in order to conserve some energy.


68

void node_recv(int n) // to receieve messages into the mailbox

{

switch(s1)

{

case 1://distance to destination message

send=0;

//printf("\nnoded2d %d

mdata:%d",node[n].d2d,(int)m->hop_count);

if(node[n].d2d==0&&n!=0)//if distance to

destination field is not set and node is not sink

{

node[n].d2d=(int)m->hop_count+1;

node[n].rearlist[node[n].rearcount]=m->from;

node[n].rearcount++;

node[n].uniquelist[node[n].uniquecount]=m->from;

node[n].uniquecount++;

send=1;

}

else if(node[n].d2d==(int)m->hop_count&&

node[n].d2d!=0)

{// neighbour node

if(n!=0)

{

node[n].nbrlist[node[n].nbrcount]=m-

>from;

node[n].nbrcount++;



}

}

else if( (node[n].d2d-(int)m->hop_count)==1)// rear

{

node[n].rearlist[node[n].rearcount]=m->from;

node[n].rearcount++;




69

}

else if((node[n].d2d-(int)m->hop_count)==-

1)//front node

{

node[n].frontlist[node[n].frontcount]=m-

>from;

node[n].frontcount++;

}

//If we send the message of type 1 it is for calculating the D2D. At the initial

deployment the sink populates one hop messages to its one-hop neighbors appending

the message with a distance value 0. The receiving nodes update its D2D value by

adding one to the received value. If the current node’s D2D value and the received

value are the same the neighboring node list is updated. If the difference of current

node’s value and received value is equal to 1 then the node is added to the rear list

and if the difference is -1 it is added to the front node list.

Case2:

else

{

if(node[n].node_status == 1)

{

send_msg(n, m->src, m->hop_count, m-

>msg_id, m->eventno, m->fc, m->fs);

}

else if(node[n].node_status == 0)

{

// CALCULATE SLEEP PROB

ifs = (float)(1.0/node[n].myenergy);

sprobability = pow(ifs,

(1.0/node[n].uniquecount));

prob = uniform(0,0.5);

if(prob <= sprobability)

{

node[n].state = 0;

node[n].slept_once=1;

hold(sprobability);

node[n].state = 1;


70

}

}

}

break;

//If the node receives the message of type 2 and the node is chosen as the sleep

candidate then the node calculates the sleep probability. The sleep probability is

calculated using the remaining energy and number of neighboring nodes of the

current node. The node then generates a random probability and compares this

probability with the calculated probability value. If the generated value is less than

the calculated value then the node decides to sleep else it decides not to sleep.

case 3://energy information of other nodes

tempid=m->from;

for(i=0;i<node[n].uniquecount;i++)

{

if(tempid==node[n].uniquelist[i])

node[n].uniqueenergy[i]=m->data;

}

break;

default:

printf("\n\n\n\n\n\n\???BUG???________default

case should not be executed\n\n\n\n\n\n ");

break;

}

}

break;

}//switch close

}//while close

//This is message type 3 which send energy information to the neighboring nodes and

rear nodes periodically.

copyright 2011, deepika murali

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