development of swarm intelligent systems for...
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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
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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
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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
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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
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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.
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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.
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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
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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
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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
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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
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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
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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).
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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
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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
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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
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