Development of Swarm Intelligent Systems for MANET

By

Sharvani G S

Supervisor

Dr. T. M. Rangaswamy

A Thesis submitted to Avinashilingam University for Women,

Coimbatore-43

In partial fulfilment of the requirements for the award of the degree of

Doctor of Philosophy

in

Computer Science and Engineering

OCTOBER – 2012

Declaration

I hereby declare that the thesis entitled “Development of Swarm Intelligent

Systems for MANET”, submitted to the Department of Computer Science

and Engineering, Faculty of Engineering, Avinashilingam University for

Women, Coimbatore, for the award of Doctor of Philosophy in Computer

Science and Engineering is a record of original research work carried out

by me under the supervision and guidance of Dr T. M. Rangaswamy,

Professor, R V College of Engineering, Bangalore, Karnataka, India and

it has not formed the basis for the award of any Degree / Diploma / Associate

ship / Fellowship or any other similar title to any candidate of any university.

Signature of the Candidate

Certificate

This is to certify that the dissertation entitled “Development of Swarm

Intelligent Systems for MANET”, submitted to the Department of

Computer Science and Engineering, Faculty of Engineering, Avinashilingam

University for Women, Coimbatore, for the award of Doctor of Philosophy

in Computer Science and Engineering is a record of original research work

carried out by Sharvani G .S, during the period of her study (April 2009 –

October 2012) in the Department of Computer Science and Engineering,

Faculty of Engineering, Avinashilingam University for Women, Coimbatore,

under my supervision and guidance and the thesis has not formed the basis

for the award of any Degree / Diploma / Associate ship / Fellowship or any

other similar title to any candidate of any university.

Signature of the Guide

i

ACKNOWLEDGEMENT

I would like to thank my guide Dr. T M Rangaswamy for his

encouragement and thoughtful guidance. With his guidance and invaluable

suggestions this research work could be made successful.

I greatly acknowledge the Revered Chancellor, Vice-Chancellor for their

encouragement and support during this research work.

I am deeply indebted to Dr Ananth A G, Professor, R.V.College of

Engineering and Dr. S. C. Sharma, Vice Chancellor, Tumkur University for

constant inspiration, strength and moral support.

I wholeheartedly thank Dr S Satyanarayana, Principal, R.V.College of

Engineering, for his support and encouragement. I would also like to express

my thanks to Dr. N K Srinath, and Dr N K Cauvery, for providing the

required facilities and moral support during my research work.

I thank the management of Rashtreeya Shikshana Samithi Trust (RSST)

for their unstinted cooperation and encouragement to carry out my PhD

work.

I owe everything to my husband. Without his endless love and support I

wouldn’t have finished this work. I am grateful to my son, my parents, my

sister-in-law and my father-in-law for supporting me in every aspect.

I sincerely thank all my friends, colleagues and all people who have

supported me directly or indirectly in carrying out my research work and

preparation of the thesis.

Sharvani G.S

ii

ABSTRACT

Mobile Adhoc Network (MANET) is an infrastructure-less multi hop network built

dynamically for temporary connections. It is suitable for applications such as target

tracking, search and rescue operation, disaster relief, military applications etc.

However, the MANET architecture poses many challenges due to its dynamic

topology, mobility, error prone shared channel etc. Many protocols were developed

which aimed at optimizing the routes for data transmission while an attempt was

made to retain small message overhead and maximize the network lifetime.

It is found that the Swarm Intelligence (SI) inspired algorithms such as Ant Colony

Optimization (ACO) are better suited for highly adaptive networks like MANETs.

Biological ants at the time of food foraging, navigate their chosen path and deposit a

chemical called pheromone on the ground, there by establishing the trail. Thickness of

the trail attracts other ants to follow the path to reach the food source. Analogous to

this, packets in the networks are biased towards the highest pheromone value for its

destination while leaving a trail to its source. Laying a source pheromone along the

same trail increases the possibility of packets travelling the same path in the reverse

order to the source. To remove stale entries from the network, pheromone decreases

using decay techniques. Pheromone tuning has to be balanced in such a way that stale

entries are not retained and at the same time, good paths are not lost.

The principles of ACO are used for each packet flow in MANETs, resulting in

emergent routing behavior. Other additional benefits achieved from ACO are reduced

control overhead and efficient route maintenance. One of the major problems with

ACO is stagnation. This occurs when all ants try to follow the same path to reach the

destination (since there is more pheromone). This causes congestion when applied in

MANETs. In this case, simple implementations of ant algorithms for MANET are not

sufficient and some modifications in pheromone trails have to be made in order to

balance the deposit / decay of pheromone trails.

iii

To circumvent stagnation, Pheromone Control technique is adopted. There are

different ways to control the pheromone namely evaporation, aging, limiting and

smoothing pheromone. This research work focuses on evaporation as a Pheromone

control technique which avoids non-optimal paths formed due to stagnation. Present

investigations include study of various Pheromone decay models like Discrete,

Polynomial and Exponentiation Decay Models. The detailed analysis carried out

indicates that the Exponential Decay Model for the pheromone works better than the

Polynomial and Discrete Decay Models. This can be attributed to the large traces of

pheromone trails left which take longer time for selecting the best path.

The exponential decay model is proven to be better in terms of load distribution.

However, Exponential decay technique loses pheromone traces very fast leading to

loss of good solutions in MANETs. A new technique for Exponential Decay Model

which evaporates pheromone trails in a controlled manner is implemented. This

technique considers node stability of the neighboring node for fine tuning the

pheromone concentration. The stability of the node is calculated based on Node

Stability Factor ‘∆’. Where ‘∆’ is the ratio between the hello sent and hello replied by

a node to its neighbors. The Node Stability Factor indicates the link stability in

relation to the other paths towards the destination. A higher ratio indicates that the

neighbor node is more stable as compared to those with a lesser ratio. Using this

concept the evaporation of pheromone is fine- tuned i.e. pheromone is decayed faster

for less stable nodes, whereas pheromone decay is slower for stable nodes. This helps

in faster decrease of the pheromone content of faulty path.

By adopting efficient pheromone evaporation techniques, Modified Termite

Algorithm (MTA) has been developed and implemented on the MANET. The

implemented MTA is found to improve the network performance in terms of

throughput, end-to-end delay and routing overheads. Further the implemented MTA

on MANET also address QoS with efficient route maintenance by local route repair

strategy, which is observed to enhance the performance of the network in terms of

throughput, end to end delay and routing overheads.

iv

TABLE OF CONTENTS

ACKNOWLEDGMENT i

ABSTRACT ii

TABLE OF CONTENTS iv

INTERNATIONAL PUBLICATIONS viii

LIST OF FIGURES xi

LIST OF TABLES xiii

CHAPTER 1

INTRODUCTION 1

1.1 Wireless Networks 1

1.2 Ad-Hoc Wireless Networks 3

1.3 Major challenges of MANETs 4

1.4 Routing in MANETs 5

1.5 Quality of Service (QoS) 6

1.6 Social Insects and stigmergy 9

1.7 Swarm Intelligence 11

1.8 Basic principle of Swarm Intelligence 13

1.9 Swarm Intelligent Framework 13

1.10 Applications of Ant colony Approach for networks 14

1.11 Major categories of SI algorithms 16

1.12 Types of Swarm Intelligence based ACO algorithms for routing 18

CHAPTER 2

PROBLEM STATEMENT AND OBJECTIVES 20

2.1 Motivation 20

2.2 Problem Statement 21

2.3 Objectives 22

2.4 Organization of Thesis 23

CHAPTER 3

v

LITERATURE SURVEY 26

3.1 Biologically inspired routing algorithms 27

3.2 GPS based routing using ACO 29

3.3 Stagnation avoidance algorithms 30

3.4 QoS in MANETs using ACO 34

3.5 Local route repair strategy using prediction 36

CHAPTER 4

ANALYSIS OF PHEROMONE DECAY TECHNIQUES FOR ACO BASED

ROUTING

37

4.1 Design of the algorithm 39

4.2 Structure chart for pheromone update 41

4.3 Efficient fine tuning pheromone technique to alleviate stagnation problem 43

4.4 Results and Discussion 44

CHAPTER 5

FINE TUNING OF PHEROMONE CONCENTRATION FOR MODIFIED

TERMITE ALGORITHM (MTA)

55

5.1 Termite Hill Building process 55

5.2 Modified Termite Algorithm (MTA) 56

5.3 Efficient stagnation avoidance technique 61

5.4 Results and Discussion 63

CHAPTER 6

MTA IMPLEMENTATION ON MANET WIT QOS SPECIFIED

EFFICIENT LOCAL ROUTE REPAIR STRATEGY

69

6.1 Modified Termite Algorithm (MTA) with QoS 70

6.2 Efficient route maintenance by predictive preemptive local route repair

strategy

71

6.3 Results and discussion 76

vi

CHAPTER 7

DEVELOPMENT OF MTA AND MANET IMPLEMENTATION 83

7.1 Structured Chart of Modified Termite Algorithm 83

7.2 Component Design 88

7.3 Pheromone Updation 97

7.4 Implementation 98

CHAPTER 8

CONCLUSION AND FUTURE WORK 102

8.1 Conclusion 102

8.2 Future work 103

REFERNCES 105

GLOSSARY 123

APPENDIX-A 124

APPENDIX-B 126

vii

INTERNATIONAL PUBLICATIONS

INTERNATIONAL JOURNALS

1. Sharvani G S, Dr. A G Ananth and Dr T M Rangaswamy,” Analysis of

different pheromone decay techniques for ACO based routing in ad hoc

wireless networks”, 2012 (Accepted by International Journal of Computer

Applications with Impact factor 0.814).

2. Sharvani G S, Dr. A G Ananth and Dr T M Rangaswamy,” Efficient

stagnation avoidance for MANETs with local repair strategy using Ant Colony

Optimization”, International Journal of Distributed and Parallel Systems

(IJDPS), Vol 3, No 5, pp 123-137, September 2012.

3. Sharvani G S, Dr. A G Ananth and Dr T M Rangaswamy,” Ant Colony

Optimization based Modified Termite Algorithm (MTA) with efficient

stagnation avoidance strategy for MANETs”, International Journal on

Applications of Graph Theory in Wireless Ad hoc Networks and Sensor

Networks (GRAPH-HOC) Vol 4, No 2/3, pp 39-50, September 2012.

4. Sharvani G S , Dr T M Rangaswamy,” Efficient Pheromone Adjustment

Techniques in ACO for Ad Hoc Wireless Network” Intl Journal of Computer

Applications Vol 44-No 6 Pp 29-32, April 2012 (impact factor .0.835).

5. G.R. Smitha , G.S. Sharvani and Dr. T M Rangaswamy,” QoS-Novel

Multipath Routing Protocol for Mobile Ad-Hoc Networks”, Journal of

Wireless Communication, CIIT Online: ISSN 0974-9640 Vol 3 No 10, Pp

719-723, (impact factor 0.572).

6. Sharvani.G.S, T.M.Rangaswamy,” Efficient Packet Delivery approach for Ad-

hoc wireless networks”, International Journal on CS&IT

viii

CSCP/vol1/cscp0104 DOI: 10.5121/csit.2011.1104, Pp 42-49 2011

(Cited by 1).

7. Sharvani.G.S, T.M.Rangaswamy,” Development of swarm Intelligent systems

for MANETs”, International Journal on Recent Trends in Engineering &

Technology, Vol. 05, No. 01, ACEEE, , Pp 163-165 ,Mar 2011.

8. Sharvani GS, Dr.T.M.Rangaswamy, Sudarshan M H, Keerthi Kiran H

P,Pavankumar V V and Thandeep G B,” Resource reservation using

bandwidth in MANETS for swarm intelligence algorithm: Termite”, Journal

of Telecommunications, Volume 3, ISSUE 2, ISSN 2042-8839, PP 54-58,

July 2010.

9. Sharvani.G.S, N.K.Cauvery, T.M.Rangaswamy,” Adaptive routing algorithm

for MANETs: Termite”, I International Journal of Next-Generation

Networks (IJNGN),Vol.1, No.1, ISSN : 0975-7023(online), ISSN : 0975-7252

( Print ), Pp 38-43 , December 2009 , (Cited by 3).

10. Sharvani.G.S, N.K.Cauvery,” Types of MANETs: A Survey”, CIIT

International Journal of Wireless Communication Print: ISSN 0974-9756 &

Online : ISSN 0974-9640, Pp 89-93 2009 May (impact factor 0.572).

INTERNATIONAL CONFERENCES

1. Sharvani G S , Vinay Kumar Kolli, “An ACO-Based Efficient Stagnation

Avoidance Methodology for MANETS” R. Maringanti et al. (eds.),

Proceedings of Ninth International Conference on Wireless Communication

and Sensor Networks, IIIT Allahabad, Lecture Notes in Electrical

Engineering 299, Pp 125-132, December-2013 DOI: 10.1007/978-81-322-

1823-4_12, © Springer India 2014

2. Sharvani.G.S, Dr.T.M.Rangaswamy, Aayush Goel, Ajith B, Binod Kumar and

Manish Kumar,” Providing Qos using Predictive Premptive based Local

ix

route strategy”, International Conference on Computer Science and

Information Technology COSIT 2011. Proceeding Published by Springer

LNCS Book Chapter Advances in Networks and communications ISSN 1865-

0929) , Pp 55-61,Jan 2011.

3. Sharvani.G.S, Dr.T.M.Rangaswamy, N.K Cauvery,” Bandwidth Efficient

routing in Swarm Intelligence”, ICISD 2011 Vallabha Vidyanagar,

Gujarat,ISBN No. 978-1-6123-3002, Pp 29-33, Jan 2011.

4. Sharvani.G.S, Dr.T.M. Rangaswamy, “QoS Methodologies in MANETs”,

International Conference on Computer Applications ICCA-2010,

Pondicherry, Pp 203-209, Dec 24-27 2010.

5. Sharvani.G.S, N.K.Cauvery, T.M.Rangaswamy,” Different types of swarm

Intelligence algorithms for routing”, International conference on Advances

in Recent Technologies in Communication and computing(ARTCOM) Pp

604-609,Oct-2009, Kottyam, Kerala,ISBN: 978-0-7695-3845-7

Proceedingspublished and Indexed by IEEE explorer. (Cited by 5).

x

LIST OF FIGURES

Fig 1.1 Cellular networks 2

Fig 1.2 Adhoc Wireless Networks 2

Fig 1.3 QoS in MANETS with Delay as a parameter 7

Fig 1.4 Cross Layer Architecture 9

Fig 1.5 Schematic diagram of termite nest 10

Fig 1.6 Food foraging behavior in ants [13] 11

Fig 4.1 Structured chart for Pheromone Update. 41

Fig 4.2 (a) Pheromone intensity vs. period (b) Probability of path selection vs period

45

Fig 4.3 (a) Pheromone intensity vs. period (b) Probability of path selection vs. period

46

Fig 4.4 (a) Pheromone intensity vs period (b) Probability of path selection vs period

48

Fig 4.5 Analysis of different decay techniques 49

Fig 4.6 (a) Pheromone intensity vs period (b) Probability of path selection vs period

52

Fig 4.7(a) Pheromone intensity vs period (b) Probability of path selection vs period

52

Fig 4.8 PDR versus Combined view of decay rates 53

Fig 5.1 Flowchart of Termite Hill building process 56

Fig 5.2 Context Diagram - MANETs using MTA 57

Fig 5.3 Phases of MANETs using MTA 58

Fig 5.4 ‘S’ node neighborhood 59

Fig 5.5 Data structure maintained at ‘S’ node 61

Fig 5.6 Throughput Vs Packet size Analysis for 30 nodes 64

Fig 5.7 Throughput Vs Packet size Analysis for 50 nodes 64

Fig 5.8 End-to-End Delay (ms) Vs Packet size Analysis for 30 nodes 65

Fig 5.9 End to End Delay (ms) Vs Packet size Analysis for 50 nodes 66

Fig 5.10 Routing Overhead Vs Packet size Analysis for 30 nodes 67

xi

Fig 5.11 Routing Overhead Vs Packet size Analysis for 50 nodes 68

Fig 6.1 Sequence diagram of route discovery with QoS 71

Fig 6.2 Failure Handling Module in MTA 72

Fig 6.3 Failure Recovery Module in MTA 74

Fig 6.4 Throughput Vs No of exhausted Nodes 77

Fig 6.5 Throughput Vs No of Exhausted Nodes 78

Fig 6.6 End to End Delay (ms) Vs No of Exhausted Nodes 79

Fig 6.7 End to End Delay (ms) Vs No of Exhausted Nodes 79

Fig 6.8 Routing Overhead Vs No of Exhausted Nodes 80

Fig 6.9 Routing Overhead Vs No of Exhausted Nodes 81

Fig 7.1 Structure Chart for Termite Routing System 84

Fig 7.2 Structure Chart for Route Discovery 84

Fig 7.3 Structure Chart for Route Maintenance 86

Fig 7.4 Structure Chart for Route Failure 87

Fig 7.5 Main components of the system 88

Fig 7.6 Data transfer flow representation 95

Fig 7.7 Pheromone updation 97

xii

LIST OF TABLES

Table 3.1 Stagnation avoidance survey 30

Table 4.1 Pheromone table for node ‘Vi’ 40

Table 4.2 Probability of path selection for different decay techniques 48

Table 4.3 Probability of path selection for different stability factor 50

Table 5.1 Example Routing Table at Node ‘S’ 59

Table 5.2 Node Stability Factor of Neighbor Nodes at Node ‘S’ 62

Table 5.3 Decay Factor for Different Ratio 62

Table 7.1 Data Packet 100

Table 7.2 Route Request / Reply / DataAck 100

Table 7.3 Hello sent / Hello Reply 100