Evolutionary algorithms for mobile ad hoc networks

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Title:

Personal Author:

Dorronsoro, Bernabé. author

Series:

Nature-inspired computing series

Physical Description:

xiv, 222 pages : illustrations ; 25 cm.

ISBN:

9781118341131

Abstract:

"This comprehensive guide describes how evolutionary algorithms (EA) may be used to identify, model, and optimize day-to-day problems that arise for researchers in optimization and mobile networking. It provides efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field. It is an ideal book for researchers and students in the field of mobile networks"-- Provided by publisher.

Subject Term:

Mobile communication systems.

Evolutionary computation.

Added Author:

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010341667	TK6570.M6 D65 2014	Open Access Book	Book	Searching... Unknown

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Summary

Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking

Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks--each of these require a designer's keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking.

This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process--allowing designers to put some "intelligence" or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field.

Evolutionary Algorithms for Mobile Ad Hoc Networks :

Instructs on how to identify, model, and optimize solutions to problems that arise in daily research Presents complete and up-to-date surveys on topics like network and mobility simulators Provides sample problems along with solutions/descriptions used to solve each, with performance comparisons Covers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and more

Evolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.

Author Notes

Bernab Dorronsoro, PhD, earned his PhD in computer science from the University of Mlaga (Spain) in 2007. His main research interests include metaheuristics and mobile networks, among others.
Patricia Ruiz, PhD, earned her PhD in computer science at the University of Luxembourg and her degree in telecommunication engineering from the University of Mlaga (Spain).
Grgoire Danoy, PhD, earned his PhD from University of St Etienne (Ecole des Mines) or the optimization of real-world problems using co-evolutionary genetic algorithms, including topology management problems in mobile ad hoc networks.
Yoann Pign, PhD, obtained his PhD from the University of Le Havre, France, on Modelling and Processing Dynamic Graphs, Applications to Mobile Ad Hoc Networks.
Pascal Bouvry, PhD, earned his PhD in computer science from the University of Grenoble (INPG), France, in 1994.

Preface	p. xiii
Part I Basic Concepts and Literature Review	p. 1
1 Introduction to Mobile Ad Hoc Networks	p. 3
1.1 Mobile Ad Hoc Networks	p. 6
1.2 Vehicular Ad Hoc Networks	p. 9
1.2.1 Widen Access in Vehicular Environment (WAVE)	p. 11
1.2.2 Communication Access for Laud Mobiles (CALM)	p. 12
1.2.3 C2C Network	p. 13
1.3 Sensor Networks	p. 14
1.3.1 LBEEI451	p. 17
1.3.2 IEEE 802.15.4	p. 17
1.3.3 ZigBee	p. 18
1.3.4 6LoWPAN	p. 19
1.3.5 Bluetooth	p. 19
1.3.6 Wireless Industrial Automation System	p. 20
1.4 Conclusion	p. 20
References	p. 21
2 Introduction to Evolutionary Algorithms	p. 27
2.1 Optimization Basics	p. 28
2.2 Evolutionary Algorithms	p. 29
2.3 Basic Components of Evolutionary Algorithms	p. 32
2.3.1 Representation	p. 32
2.3.2 Fitness Function	p. 32
2.3.3 Selection	p. 32
2.3.4 Crossover	p. 33
2.3.5 Mutation	p. 34
2.3.6 Replacement	p. 35
2.3.7 Elitism	p. 35
2.3.8 Stopping Criteria	p. 35
2.4 Panmictic Evolutionary Algorithms	p. 36
2.4.1 Generational EA	p. 36
2.4.2 Steady-State EA	p. 36
2.5 Evolutionary Algorithms with Structured Populations	p. 36
2.5.1 Cellular EAs	p. 37
2.5.2 Cooperative Revolutionary EAs	p. 38
2.6 Multi-Objective Evolutionary Algorithms	p. 39
2.6.1 Basic Concepts in Multi-Objective Optimization	p. 40
2.6.2 Hierarchical Multi-Objective Problem Optimization	p. 42
2.6.3 Simultaneous Multi-Objective Problem Optimization	p. 43
2.7 Conclusion	p. 44
References	p. 45
3 Survey on Optimization Problems for Mobile Ad Hoc Networks	p. 49
3.1 Taxonomy of the Optimization Process	p. 51
3.1.1 Online and Offline Techniques	p. 51
3.1.2 Using Global or Local Knowledge	p. 52
3.1.3 Centralized and Decentralized Systems	p. 52
3.2 State of the Art	p. 53
3.2.1 Topology Management	p. 53
3.2.2 Broadcasting Algorithms	p. 58
3.2.3 Routing Protocols	p. 59
3.2.4 Clustering Approaches	p. 63
3.2.5 Protocol Optimization	p. 64
3.2.6 Modeling the Mobility of Nodes	p. 65
3.2.7 Selfish Behaviors	p. 66
3.2.8 Security Issues	p. 67
3.2.9 Other Applications	p. 67
3.3 Conclusion	p. 68
References	p. 69
4 Mobile Networks Simulation	p. 79
4.1 Signal Propagation Modeling	p. 80
4.1.1 Physical Phenomena	p. 81
4.1.2 Signal Propagation Models	p. 85
4.2 State of the Art of Network Simulators	p. 89
4.2.1 Simulators	p. 89
4.2.2 Analysis	p. 92
4.3 Mobility Simulation	p. 93
4.3.1 Mobility Models	p. 93
4.3.2 State of the Art of Mobility Simulators	p. 96
4.4 Conclusion	p. 98
References	p. 98
Part II Problems Optimization	p. 105
5 Proposed Optimization Framework	p. 107
5.1 Architecture	p. 108
5.2 Optimization Algorithms	p. 110
5.2.1 Single-Objective Algorithms	p. 110
5.2.2 Multi-Objective Algorithms	p. 115
5.3 Simulators	p. 121
5.3.1 Network Simulator: ns-3	p. 121
5.3.2 Mobility Simulator: SUMO	p. 123
5.3.3 Graph-Based Simulations	p. 126
5.4 Experimental Setup	p. 127
5.5 Conclusion 131 References	p. 131
6 Broadcasting Protocol	p. 135
6.1 The Problem	p. 136
6.1.1 DFCN Protocol	p. 136
6.1.2 Optimization Problem Definition	p. 138
6.2 Experiments	p. 140
6.2.1 Algorithm Configurations	p. 140
6.2.2 Comparison of the Performance of the Algorithms	p. 141
6.3 Analysis of Results	p. 142
6.3.1 Building a Representative Subset of Best Solutions	p. 143
6.3.2 Interpretation of the Results	p. 145
6.3.3 Selected Improved DFCN Configurations	p. 148
6.4 Conclusion	p. 150
References	p. 151
7 Energy Management	p. 153
7.1 The Problem	p. 154
7.1.1 AEDB Protocol	p. 154
7.1.2 Optimization Problem Definition	p. 156
7.2 Experiments	p. 159
7.2.1 Algorithm Configurations	p. 159
7.2.2 Comparison of the Performance of the Algorithms	p. 160
7.3 Analysis of Results	p. 161
7.4 Selecting Solutions from the Pareto Front	p. 164
7.4.1 Performance of the Selected Solutions	p. 167
7.5 Conclusion	p. 170
References	p. 171
8 Network Topology	p. 173
8.1 The Problem	p. 175
8.1.1 Injection Networks	p. 175
8.1.2 Optimization Problem Definition	p. 176
8.2 Heuristics	p. 178
8.2.1 Centralized	p. 178
8.2.2 Distributed	p. 179
8.3 Experiments	p. 180
8.3.1 Algorithm Configurations	p. 180
8.3.2 Comparison of the Performance of the Algorithms	p. 180
8.4 Analysis of Results	p. 183
8.4.1 Analysis of the Objective Values	p. 183
8.4.2 Comparison with Heuristics	p. 185
8.5 Conclusion	p. 187
References	p. 188
9 Realistic Vehicular Mobility	p. 191
9.1 The Problem	p. 192
9.1.1 Vehicular Mobility Model	p. 192
9.1.2 Optimization Problem Definition	p. 196
9.2 Experiments	p. 199
9.2.1 Algorithms Configuration	p. 199
9.2.2 Comparison of the Performance of the Algorithms	p. 200
9.3 Analysis of Results	p. 202
9.3.1 Analysis of the Decision Variables	p. 202
9.3.2 Analysis of the Objective Values	p. 204
9.4 Conclusion	p. 206
References	p. 206
10 Summary and Discussion	p. 209
10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks	p. 211
10.2 Performance of the Three Algorithmic Proposals	p. 213
10.2.1 Broadcasting Protocol	p. 213
10.2.2 Energy-Efficient Communications	p. 214
10.2.3 Network Connectivity	p. 214
10.2.4 Vehicular Mobility	p. 215
10.3 Global Discussion on the Performance of the Algorithms	p. 215
10.3.1 Single-Objective Case	p. 216
10.3.2 Multi-Objective Case	p. 217
10.4 Conclusion	p. 218
References	p. 218
Index	p. 221

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