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E-raamat: Evolutionary Algorithms for Mobile Ad Hoc Networks

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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 networkseach of these require a designers 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 processallowing 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.
Preface xiii
Part I Basic Concepts And Literature Review 1(104)
1 Introduction To Mobile Ad Hoc Networks
3(24)
1.1 Mobile Ad Hoc Networks
6(3)
1.2 Vehicular Ad Hoc Networks
9(5)
1.2.1 Wireless Access in Vehicular Environment (WAVE)
11(1)
1.2.2 Communication Access for Land Mobiles (CALM)
12(1)
1.2.3 C2C Network
13(1)
1.3 Sensor Networks
14(6)
1.3.1 IEEE 1451
17(1)
1.3.2 IEEE 802.15.4
17(1)
1.3.3 ZigBee
18(1)
1.3.4 6LoWPAN
19(1)
1.3.5 Bluetooth
19(1)
1.3.6 Wireless Industrial Automation System
20(1)
1.4 Conclusion
20(1)
References
21(6)
2 Introduction To Evolutionary Algorithms
27(22)
2.1 Optimization Basics
28(1)
2.2 Evolutionary Algorithms
29(3)
2.3 Basic Components of Evolutionary Algorithms
32(4)
2.3.1 Representation
32(1)
2.3.2 Fitness Function
32(1)
2.3.3 Selection
32(1)
2.3.4 Crossover
33(1)
2.3.5 Mutation
34(1)
2.3.6 Replacement
35(1)
2.3.7 Elitism
35(1)
2.3.8 Stopping Criteria
35(1)
2.4 Panmictic Evolutionary Algorithms
36(1)
2.4.1 Generational EA
36(1)
2.4.2 Steady-State EA
36(1)
2.5 Evolutionary Algorithms with Structured Populations
36(3)
2.5.1 Cellular EAs
37(1)
2.5.2 Cooperative Coevolutionary EAs
38(1)
2.6 Multi-Objective Evolutionary Algorithms
39(5)
2.6.1 Basic Concepts in Multi-Objective Optimization
40(2)
2.6.2 Hierarchical Multi-Objective Problem Optimization
42(1)
2.6.3 Simultaneous Multi-Objective Problem Optimization
43(1)
2.7 Conclusion
44(1)
References
45(4)
3 Survey On Optimization Problems For Mobile Ad Hoc Networks
49(30)
3.1 Taxonomy of the Optimization Process
51(2)
3.1.1 Online and Offline Techniques
51(1)
3.1.2 Using Global or Local Knowledge
52(1)
3.1.3 Centralized and Decentralized Systems
52(1)
3.2 State of the Art
53(15)
3.2.1 Topology Management
53(5)
3.2.2 Broadcasting Algorithms
58(1)
3.2.3 Routing Protocols
59(4)
3.2.4 Clustering Approaches
63(1)
3.2.5 Protocol Optimization
64(1)
3.2.6 Modeling the Mobility of Nodes
65(1)
3.2.7 Selfish Behaviors
66(1)
3.2.8 Security Issues
67(1)
3.2.9 Other Applications
67(1)
3.3 Conclusion
68(1)
References
69(10)
4 Mobile Networks Simulation
79(26)
4.1 Signal Propagation Modeling
80(9)
4.1.1 Physical Phenomena
81(4)
4.1.2 Signal Propagation Models
85(4)
4.2 State of the Art of Network Simulators
89(4)
4.2.1 Simulators
89(3)
4.2.2 Analysis
92(1)
4.3 Mobility Simulation
93(5)
4.3.1 Mobility Models
93(3)
4.3.2 State of the Art of Mobility Simulators
96(2)
4.4 Conclusion
98(1)
References
98(7)
Part II Problems Optimization 105(116)
5 Proposed Optimization Framework
107(28)
5.1 Architecture
108(2)
5.2 Optimization Algorithms
110(11)
5.2.1 Single-Objective Algorithms
110(5)
5.2.2 Multi-Objective Algorithms
115(6)
5.3 Simulators
121(6)
5.3.1 Network Simulator: ns-3
121(2)
5.3.2 Mobility Simulator: SUMO
123(3)
5.3.3 Graph-Based Simulations
126(1)
5.4 Experimental Setup
127(4)
5.5 Conclusion
131(1)
References
131(4)
6 Broadcasting Protocol
135(18)
6.1 The Problem
136(4)
6.1.1 DFCN Protocol
136(2)
6.1.2 Optimization Problem Definition
138(2)
6.2 Experiments
140(2)
6.2.1 Algorithm Configurations
140(1)
6.2.2 Comparison of the Performance of the Algorithms
141(1)
6.3 Analysis of Results
142(8)
6.3.1 Building a Representative Subset of Best Solutions
143(2)
6.3.2 Interpretation of the Results
145(3)
6.3.3 Selected Improved DFCN Configurations
148(2)
6.4 Conclusion
150(1)
References
151(2)
7 Energy Management
153(20)
7.1 The Problem
154(5)
7.1.1 AEDB Protocol
154(2)
7.1.2 Optimization Problem Definition
156(3)
7.2 Experiments
159(2)
7.2.1 Algorithm Configurations
159(1)
7.2.2 Comparison of the Performance of the Algorithms
160(1)
7.3 Analysis of Results
161(3)
7.4 Selecting Solutions from the Pareto Front
164(6)
7.4.1 Performance of the Selected Solutions
167(3)
7.5 Conclusion
170(1)
References
171(2)
8 Network Topology
173(18)
8.1 The Problem
175(3)
8.1.1 Injection Networks
175(1)
8.1.2 Optimization Problem Definition
176(2)
8.2 Heuristics
178(2)
8.2.1 Centralized
178(1)
8.2.2 Distributed
179(1)
8.3 Experiments
180(3)
8.3.1 Algorithm Configurations
180(1)
8.3.2 Comparison of the Performance of the Algorithms
180(3)
8.4 Analysis of Results
183(4)
8.4.1 Analysis of the Objective Values
183(2)
8.4.2 Comparison with Heuristics
185(2)
8.5 Conclusion
187(1)
References
188(3)
9 Realistic Vehicular Mobility
191(18)
9.1 The Problem
192(7)
9.1.1 Vehicular Mobility Model
192(4)
9.1.2 Optimization Problem Definition
196(3)
9.2 Experiments
199(3)
9.2.1 Algorithms Configuration
199(1)
9.2.2 Comparison of the Performance of the Algorithms
200(2)
9.3 Analysis of Results
202(4)
9.3.1 Analysis of the Decision Variables
202(2)
9.3.2 Analysis of the Objective Values
204(2)
9.4 Conclusion
206(1)
References
206(3)
10 Summary And Discussion
209(12)
10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks
211(2)
10.2 Performance of the Three Algorithmic Proposals
213(2)
10.2.1 Broadcasting Protocol
213(1)
10.2.2 Energy-Efficient Communications
214(1)
10.2.3 Network Connectivity
214(1)
10.2.4 Vehicular Mobility
215(1)
10.3 Global Discussion on the Performance of the Algorithms
215(3)
10.3.1 Single-Objective Case
216(1)
10.3.2 Multi-Objective Case
217(1)
10.4 Conclusion
218(1)
References
218(3)
Index 221
BERNABÉ DORRONSORO, PHD, earned his PhD in computer science from the University of Málaga (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 Málaga (Spain).

GRÉGOIRE DANOY, PHD, earned his PhD from University of St Etienne (Ecole des Mines) on the optimization of real-world problems using co-evolutionary genetic algorithms, including topology management problems in mobile ad hoc networks.

YOANN PIGNÉ, 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.
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