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Evolutionary algorithms for mobile ad hoc networks / / Bernabe Dorronsoro, University of Luxembourg, Patricia Ruiz, University of Luxembourg, Gregoire Danoy, University of Luxembourg, Yoann Pigne, University of Le Havre, Pascal Bouvry, University of Luxembourg
| Evolutionary algorithms for mobile ad hoc networks / / Bernabe Dorronsoro, University of Luxembourg, Patricia Ruiz, University of Luxembourg, Gregoire Danoy, University of Luxembourg, Yoann Pigne, University of Le Havre, Pascal Bouvry, University of Luxembourg |
| Autore | Dorronsoro Bernabé |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Computer society, IEEE, Wiley, , [2014] |
| Descrizione fisica | 1 online resource (238 p.) |
| Disciplina | 621.382/1201519625 |
| Collana | Nature-inspired computing series |
| Soggetto topico |
Mobile communication systems
Evolutionary computation Genetic algorithms |
| ISBN |
1-118-83202-7
1-118-83320-1 1-118-83201-9 |
| Classificazione | COM051300 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xiii -- PART I BASIC CONCEPTS AND LITERATURE REVIEW 1 -- 1 INTRODUCTION TO MOBILE AD HOC NETWORKS 3 -- 1.1 Mobile Ad Hoc Networks 6 -- 1.2 Vehicular Ad Hoc Networks 9 -- 1.2.1 Wireless Access in Vehicular Environment (WAVE) 11 -- 1.2.2 Communication Access for Land Mobiles (CALM) 12 -- 1.2.3 C2C Network 13 -- 1.3 Sensor Networks 14 -- 1.3.1 IEEE 1451 17 -- 1.3.2 IEEE 802.15.4 17 -- 1.3.3 ZigBee 18 -- 1.3.4 6LoWPAN 19 -- 1.3.5 Bluetooth 19 -- 1.3.6 Wireless Industrial Automation System 20 -- 1.4 Conclusion 20 -- References 21 -- 2 INTRODUCTION TO EVOLUTIONARY ALGORITHMS 27 -- 2.1 Optimization Basics 28 -- 2.2 Evolutionary Algorithms 29 -- 2.3 Basic Components of Evolutionary Algorithms 32 -- 2.3.1 Representation 32 -- 2.3.2 Fitness Function 32 -- 2.3.3 Selection 32 -- 2.3.4 Crossover 33 -- 2.3.5 Mutation 34 -- 2.3.6 Replacement 35 -- 2.3.7 Elitism 35 -- 2.3.8 Stopping Criteria 35 -- 2.4 Panmictic Evolutionary Algorithms 36 -- 2.4.1 Generational EA 36 -- 2.4.2 Steady-State EA 36 -- 2.5 Evolutionary Algorithms with Structured Populations 36 -- 2.5.1 Cellular EAs 37 -- 2.5.2 Cooperative Coevolutionary EAs 38 -- 2.6 Multi-Objective Evolutionary Algorithms 39 -- 2.6.1 Basic Concepts in Multi-Objective Optimization 40 -- 2.6.2 Hierarchical Multi-Objective Problem Optimization 42 -- 2.6.3 Simultaneous Multi-Objective Problem Optimization 43 -- 2.7 Conclusion 44 -- References 45 -- 3 SURVEY ON OPTIMIZATION PROBLEMS FOR MOBILE AD HOC NETWORKS 49 -- 3.1 Taxonomy of the Optimization Process 51 -- 3.1.1 Online and Offline Techniques 51 -- 3.1.2 Using Global or Local Knowledge 52 -- 3.1.3 Centralized and Decentralized Systems 52 -- 3.2 State of the Art 53 -- 3.2.1 Topology Management 53 -- 3.2.2 Broadcasting Algorithms 58 -- 3.2.3 Routing Protocols 59 -- 3.2.4 Clustering Approaches 63 -- 3.2.5 Protocol Optimization 64 -- 3.2.6 Modeling the Mobility of Nodes 65 -- 3.2.7 Selfish Behaviors 66 -- 3.2.8 Security Issues 67 -- 3.2.9 Other Applications 67 -- 3.3 Conclusion 68 -- References 69.
4 MOBILE NETWORKS SIMULATION 79 -- 4.1 Signal Propagation Modeling 80 -- 4.1.1 Physical Phenomena 81 -- 4.1.2 Signal Propagation Models 85 -- 4.2 State of the Art of Network Simulators 89 -- 4.2.1 Simulators 89 -- 4.2.2 Analysis 92 -- 4.3 Mobility Simulation 93 -- 4.3.1 Mobility Models 93 -- 4.3.2 State of the Art of Mobility Simulators 96 -- 4.4 Conclusion 98 -- References 98 -- PART II PROBLEMS OPTIMIZATION 105 -- 5 PROPOSED OPTIMIZATION FRAMEWORK 107 -- 5.1 Architecture 108 -- 5.2 Optimization Algorithms 110 -- 5.2.1 Single-Objective Algorithms 110 -- 5.2.2 Multi-Objective Algorithms 115 -- 5.3 Simulators 121 -- 5.3.1 Network Simulator: ns-3 121 -- 5.3.2 Mobility Simulator: SUMO 123 -- 5.3.3 Graph-Based Simulations 126 -- 5.4 Experimental Setup 127 -- 5.5 Conclusion 131 -- References 131 -- 6 BROADCASTING PROTOCOL 135 -- 6.1 The Problem 136 -- 6.1.1 DFCN Protocol 136 -- 6.1.2 Optimization Problem Definition 138 -- 6.2 Experiments 140 -- 6.2.1 Algorithm Configurations 140 -- 6.2.2 Comparison of the Performance of the Algorithms 141 -- 6.3 Analysis of Results 142 -- 6.3.1 Building a Representative Subset of Best Solutions 143 -- 6.3.2 Interpretation of the Results 145 -- 6.3.3 Selected Improved DFCN Configurations 148 -- 6.4 Conclusion 150 -- References 151 -- 7 ENERGY MANAGEMENT 153 -- 7.1 The Problem 154 -- 7.1.1 AEDB Protocol 154 -- 7.1.2 Optimization Problem Definition 156 -- 7.2 Experiments 159 -- 7.2.1 Algorithm Configurations 159 -- 7.2.2 Comparison of the Performance of the Algorithms 160 -- 7.3 Analysis of Results 161 -- 7.4 Selecting Solutions from the Pareto Front 164 -- 7.4.1 Performance of the Selected Solutions 167 -- 7.5 Conclusion 170 -- References 171 -- 8 NETWORK TOPOLOGY 173 -- 8.1 The Problem 175 -- 8.1.1 Injection Networks 175 -- 8.1.2 Optimization Problem Definition 176 -- 8.2 Heuristics 178 -- 8.2.1 Centralized 178 -- 8.2.2 Distributed 179 -- 8.3 Experiments 180 -- 8.3.1 Algorithm Configurations 180 -- 8.3.2 Comparison of the Performance of the Algorithms 180. 8.4 Analysis of Results 183 -- 8.4.1 Analysis of the Objective Values 183 -- 8.4.2 Comparison with Heuristics 185 -- 8.5 Conclusion 187 -- References 188 -- 9 REALISTIC VEHICULAR MOBILITY 191 -- 9.1 The Problem 192 -- 9.1.1 Vehicular Mobility Model 192 -- 9.1.2 Optimization Problem Definition 196 -- 9.2 Experiments 199 -- 9.2.1 Algorithms Configuration 199 -- 9.2.2 Comparison of the Performance of the Algorithms 200 -- 9.3 Analysis of Results 202 -- 9.3.1 Analysis of the Decision Variables 202 -- 9.3.2 Analysis of the Objective Values 204 -- 9.4 Conclusion 206 -- References 206 -- 10 SUMMARY AND DISCUSSION 209 -- 10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks 211 -- 10.2 Performance of the Three Algorithmic Proposals 213 -- 10.2.1 Broadcasting Protocol 213 -- 10.2.2 Energy-Efficient Communications 214 -- 10.2.3 Network Connectivity 214 -- 10.2.4 Vehicular Mobility 215 -- 10.3 Global Discussion on the Performance of the Algorithms 215 -- 10.3.1 Single-Objective Case 216 -- 10.3.2 Multi-Objective Case 217 -- 10.4 Conclusion 218 -- References 218 -- INDEX 221. |
| Record Nr. | UNINA-9910139130603321 |
Dorronsoro Bernabé
|
||
| Hoboken, New Jersey : , : Computer society, IEEE, Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Evolutionary algorithms for mobile ad hoc networks / / Bernabe Dorronsoro, University of Luxembourg, Patricia Ruiz, University of Luxembourg, Gregoire Danoy, University of Luxembourg, Yoann Pigne, University of Le Havre, Pascal Bouvry, University of Luxembourg
| Evolutionary algorithms for mobile ad hoc networks / / Bernabe Dorronsoro, University of Luxembourg, Patricia Ruiz, University of Luxembourg, Gregoire Danoy, University of Luxembourg, Yoann Pigne, University of Le Havre, Pascal Bouvry, University of Luxembourg |
| Autore | Dorronsoro Bernabé |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Computer society, IEEE, Wiley, , [2014] |
| Descrizione fisica | 1 online resource (238 p.) |
| Disciplina | 621.382/1201519625 |
| Collana | Nature-inspired computing series |
| Soggetto topico |
Mobile communication systems
Evolutionary computation Genetic algorithms |
| ISBN |
1-118-83202-7
1-118-83320-1 1-118-83201-9 |
| Classificazione | COM051300 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xiii -- PART I BASIC CONCEPTS AND LITERATURE REVIEW 1 -- 1 INTRODUCTION TO MOBILE AD HOC NETWORKS 3 -- 1.1 Mobile Ad Hoc Networks 6 -- 1.2 Vehicular Ad Hoc Networks 9 -- 1.2.1 Wireless Access in Vehicular Environment (WAVE) 11 -- 1.2.2 Communication Access for Land Mobiles (CALM) 12 -- 1.2.3 C2C Network 13 -- 1.3 Sensor Networks 14 -- 1.3.1 IEEE 1451 17 -- 1.3.2 IEEE 802.15.4 17 -- 1.3.3 ZigBee 18 -- 1.3.4 6LoWPAN 19 -- 1.3.5 Bluetooth 19 -- 1.3.6 Wireless Industrial Automation System 20 -- 1.4 Conclusion 20 -- References 21 -- 2 INTRODUCTION TO EVOLUTIONARY ALGORITHMS 27 -- 2.1 Optimization Basics 28 -- 2.2 Evolutionary Algorithms 29 -- 2.3 Basic Components of Evolutionary Algorithms 32 -- 2.3.1 Representation 32 -- 2.3.2 Fitness Function 32 -- 2.3.3 Selection 32 -- 2.3.4 Crossover 33 -- 2.3.5 Mutation 34 -- 2.3.6 Replacement 35 -- 2.3.7 Elitism 35 -- 2.3.8 Stopping Criteria 35 -- 2.4 Panmictic Evolutionary Algorithms 36 -- 2.4.1 Generational EA 36 -- 2.4.2 Steady-State EA 36 -- 2.5 Evolutionary Algorithms with Structured Populations 36 -- 2.5.1 Cellular EAs 37 -- 2.5.2 Cooperative Coevolutionary EAs 38 -- 2.6 Multi-Objective Evolutionary Algorithms 39 -- 2.6.1 Basic Concepts in Multi-Objective Optimization 40 -- 2.6.2 Hierarchical Multi-Objective Problem Optimization 42 -- 2.6.3 Simultaneous Multi-Objective Problem Optimization 43 -- 2.7 Conclusion 44 -- References 45 -- 3 SURVEY ON OPTIMIZATION PROBLEMS FOR MOBILE AD HOC NETWORKS 49 -- 3.1 Taxonomy of the Optimization Process 51 -- 3.1.1 Online and Offline Techniques 51 -- 3.1.2 Using Global or Local Knowledge 52 -- 3.1.3 Centralized and Decentralized Systems 52 -- 3.2 State of the Art 53 -- 3.2.1 Topology Management 53 -- 3.2.2 Broadcasting Algorithms 58 -- 3.2.3 Routing Protocols 59 -- 3.2.4 Clustering Approaches 63 -- 3.2.5 Protocol Optimization 64 -- 3.2.6 Modeling the Mobility of Nodes 65 -- 3.2.7 Selfish Behaviors 66 -- 3.2.8 Security Issues 67 -- 3.2.9 Other Applications 67 -- 3.3 Conclusion 68 -- References 69.
4 MOBILE NETWORKS SIMULATION 79 -- 4.1 Signal Propagation Modeling 80 -- 4.1.1 Physical Phenomena 81 -- 4.1.2 Signal Propagation Models 85 -- 4.2 State of the Art of Network Simulators 89 -- 4.2.1 Simulators 89 -- 4.2.2 Analysis 92 -- 4.3 Mobility Simulation 93 -- 4.3.1 Mobility Models 93 -- 4.3.2 State of the Art of Mobility Simulators 96 -- 4.4 Conclusion 98 -- References 98 -- PART II PROBLEMS OPTIMIZATION 105 -- 5 PROPOSED OPTIMIZATION FRAMEWORK 107 -- 5.1 Architecture 108 -- 5.2 Optimization Algorithms 110 -- 5.2.1 Single-Objective Algorithms 110 -- 5.2.2 Multi-Objective Algorithms 115 -- 5.3 Simulators 121 -- 5.3.1 Network Simulator: ns-3 121 -- 5.3.2 Mobility Simulator: SUMO 123 -- 5.3.3 Graph-Based Simulations 126 -- 5.4 Experimental Setup 127 -- 5.5 Conclusion 131 -- References 131 -- 6 BROADCASTING PROTOCOL 135 -- 6.1 The Problem 136 -- 6.1.1 DFCN Protocol 136 -- 6.1.2 Optimization Problem Definition 138 -- 6.2 Experiments 140 -- 6.2.1 Algorithm Configurations 140 -- 6.2.2 Comparison of the Performance of the Algorithms 141 -- 6.3 Analysis of Results 142 -- 6.3.1 Building a Representative Subset of Best Solutions 143 -- 6.3.2 Interpretation of the Results 145 -- 6.3.3 Selected Improved DFCN Configurations 148 -- 6.4 Conclusion 150 -- References 151 -- 7 ENERGY MANAGEMENT 153 -- 7.1 The Problem 154 -- 7.1.1 AEDB Protocol 154 -- 7.1.2 Optimization Problem Definition 156 -- 7.2 Experiments 159 -- 7.2.1 Algorithm Configurations 159 -- 7.2.2 Comparison of the Performance of the Algorithms 160 -- 7.3 Analysis of Results 161 -- 7.4 Selecting Solutions from the Pareto Front 164 -- 7.4.1 Performance of the Selected Solutions 167 -- 7.5 Conclusion 170 -- References 171 -- 8 NETWORK TOPOLOGY 173 -- 8.1 The Problem 175 -- 8.1.1 Injection Networks 175 -- 8.1.2 Optimization Problem Definition 176 -- 8.2 Heuristics 178 -- 8.2.1 Centralized 178 -- 8.2.2 Distributed 179 -- 8.3 Experiments 180 -- 8.3.1 Algorithm Configurations 180 -- 8.3.2 Comparison of the Performance of the Algorithms 180. 8.4 Analysis of Results 183 -- 8.4.1 Analysis of the Objective Values 183 -- 8.4.2 Comparison with Heuristics 185 -- 8.5 Conclusion 187 -- References 188 -- 9 REALISTIC VEHICULAR MOBILITY 191 -- 9.1 The Problem 192 -- 9.1.1 Vehicular Mobility Model 192 -- 9.1.2 Optimization Problem Definition 196 -- 9.2 Experiments 199 -- 9.2.1 Algorithms Configuration 199 -- 9.2.2 Comparison of the Performance of the Algorithms 200 -- 9.3 Analysis of Results 202 -- 9.3.1 Analysis of the Decision Variables 202 -- 9.3.2 Analysis of the Objective Values 204 -- 9.4 Conclusion 206 -- References 206 -- 10 SUMMARY AND DISCUSSION 209 -- 10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks 211 -- 10.2 Performance of the Three Algorithmic Proposals 213 -- 10.2.1 Broadcasting Protocol 213 -- 10.2.2 Energy-Efficient Communications 214 -- 10.2.3 Network Connectivity 214 -- 10.2.4 Vehicular Mobility 215 -- 10.3 Global Discussion on the Performance of the Algorithms 215 -- 10.3.1 Single-Objective Case 216 -- 10.3.2 Multi-Objective Case 217 -- 10.4 Conclusion 218 -- References 218 -- INDEX 221. |
| Record Nr. | UNINA-9910828431103321 |
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Dorronsoro Bernabé
|
||
| Hoboken, New Jersey : , : Computer society, IEEE, Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||