Coverart for item
The Resource Evolutionary algorithms for mobile ad hoc networks, Bernabé Dorronsoro...[et al.], (electronic resource)

Evolutionary algorithms for mobile ad hoc networks, Bernabé Dorronsoro...[et al.], (electronic resource)

Label
Evolutionary algorithms for mobile ad hoc networks
Title
Evolutionary algorithms for mobile ad hoc networks
Statement of responsibility
Bernabé Dorronsoro...[et al.]
Contributor
Subject
Language
eng
Summary
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
Member of
Cataloging source
EBLCP
Dewey number
621.382/1201519625
Index
no index present
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
Dorronsoro, Bernabé
Series statement
Nature-Inspired Computing Series
http://library.link/vocab/subjectName
  • Mobile communication systems
  • Evolutionary computation
  • Genetic algorithms
Label
Evolutionary algorithms for mobile ad hoc networks, Bernabé Dorronsoro...[et al.], (electronic resource)
Instantiates
Publication
Note
Description based upon print version of record
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Color
multicolored
Contents
  • Wireless Access in Vehicular Environment (WAVE)
  • Protocol Optimization
  • 3.2.6.
  • Modeling the Mobility of Nodes
  • 3.2.7.
  • Selfish Behaviors
  • 3.2.8.
  • Security Issues
  • 3.2.9.
  • Other Applications
  • 3.3.
  • 1.2.2.
  • Conclusion
  • References
  • 4.
  • Mobile Networks Simulation
  • 4.1.
  • Signal Propagation Modeling
  • 4.1.1.
  • Physical Phenomena
  • 4.1.2.
  • Signal Propagation Models
  • Communication Access for Land Mobiles (CALM)
  • 4.2.
  • State of the Art of Network Simulators
  • 4.2.1.
  • Simulators
  • 4.2.2.
  • Analysis
  • 4.3.
  • Mobility Simulation
  • 4.3.1.
  • Mobility Models
  • 1.2.3.
  • 4.3.2.
  • State of the Art of Mobility Simulators
  • 4.4.
  • Conclusion
  • References
  • 5.
  • Proposed Optimization Framework
  • 5.1.
  • Architecture
  • 5.2.
  • C2C Network
  • Optimization Algorithms
  • 5.2.1.
  • Single-Objective Algorithms
  • 5.2.2.
  • Multi-Objective Algorithms
  • 5.3.
  • Simulators
  • 5.3.1.
  • Network Simulator: ns-3
  • 5.3.2.
  • 1.3.
  • Mobility Simulator: SUMO
  • 5.3.3.
  • Graph-Based Simulations
  • 5.4.
  • Experimental Setup
  • 5.5.
  • Conclusion
  • References
  • 6.
  • Broadcasting Protocol
  • Sensor Networks
  • 6.1.
  • The Problem
  • 6.1.1.
  • DFCN Protocol
  • 6.1.2.
  • Optimization Problem Definition
  • 6.2.
  • Experiments
  • 6.2.1.
  • Algorithm Configurations
  • 1.3.1.
  • 6.2.2.
  • Comparison of the Performance of the Algorithms
  • 6.3.
  • Analysis of Results
  • 6.3.1.
  • Building a Representative Subset of Best Solutions
  • 6.3.2.
  • Interpretation of the Results
  • 6.3.3.
  • Selected Improved DFCN Configurations
  • IEEE 1451
  • 6.4.
  • Conclusion
  • References
  • 7.
  • Energy Management
  • 7.1.
  • The Problem
  • 7.1.1.
  • AEDB Protocol
  • 7.1.2.
  • 1.3.2.
  • Optimization Problem Definition
  • 7.2.
  • Experiments
  • 7.2.1.
  • Algorithm Configurations
  • 7.2.2.
  • Comparison of the Performance of the Algorithms
  • 7.3.
  • Analysis of Results
  • 7.4.
  • Machine generated contents note:
  • IEEE 802.15.4
  • Selecting Solutions from the Pareto Front
  • 7.4.1.
  • Performance of the Selected Solutions
  • 7.5.
  • Conclusion
  • References
  • 8.
  • Network Topology
  • 8.1.
  • The Problem
  • 1.3.3.
  • 8.1.1.
  • Injection Networks
  • 8.1.2.
  • Optimization Problem Definition
  • 8.2.
  • Heuristics
  • 8.2.1.
  • Centralized
  • 8.2.2.
  • Distributed
  • ZigBee
  • 8.3.
  • Experiments
  • 8.3.1.
  • Algorithm Configurations
  • 8.3.2.
  • Comparison of the Performance of the Algorithms
  • 8.4.
  • Analysis of Results
  • 8.4.1.
  • Analysis of the Objective Values
  • 1.3.4.
  • 8.4.2.
  • Comparison with Heuristics
  • 8.5.
  • Conclusion
  • References
  • 9.
  • Realistic Vehicular Mobility
  • 9.1.
  • The Problem
  • 9.1.1.
  • 6LoWPAN
  • Vehicular Mobility Model
  • 9.1.2.
  • Optimization Problem Definition
  • 9.2.
  • Experiments
  • 9.2.1.
  • Algorithms Configuration
  • 9.2.2.
  • Comparison of the Performance of the Algorithms
  • 9.3.
  • 1.3.5.
  • Analysis of Results
  • 9.3.1.
  • Analysis of the Decision Variables
  • 9.3.2.
  • Analysis of the Objective Values
  • 9.4.
  • Conclusion
  • References
  • 10.
  • Summary And Discussion
  • Bluetooth
  • 10.1.
  • A New Methodology for Optimization in Mobile Ad Hoc Networks
  • 10.2.
  • Performance of the Three Algorithmic Proposals
  • 10.2.1.
  • Broadcasting Protocol
  • 10.2.2.
  • Energy-Efficient Communications
  • 10.2.3.
  • Network Connectivity
  • 1.3.6.
  • 10.2.4.
  • Vehicular Mobility
  • 10.3.
  • Global Discussion on the Performance of the Algorithms
  • 10.3.1.
  • Single-Objective Case
  • 10.3.2.
  • Multi-Objective Case
  • 10.4.
  • Conclusion
  • Wireless Industrial Automation System
  • References
  • 1.4.
  • 1.
  • Conclusion
  • References
  • 2.
  • Introduction To Evolutionary Algorithms
  • 2.1.
  • Optimization Basics
  • 2.2.
  • Evolutionary Algorithms
  • 2.3.
  • Basic Components of Evolutionary Algorithms
  • Introduction To Mobile Ad Hoc Networks
  • 2.3.1.
  • Representation
  • 2.3.2.
  • Fitness Function
  • 2.3.3.
  • Selection
  • 2.3.4.
  • Crossover
  • 2.3.5.
  • Mutation
  • 1.1.
  • 2.3.6.
  • Replacement
  • 2.3.7.
  • Elitism
  • 2.3.8.
  • Stopping Criteria
  • 2.4.
  • Panmictic Evolutionary Algorithms
  • 2.4.1.
  • Generational EA
  • Mobile Ad Hoc Networks
  • 2.4.2.
  • Steady-State EA
  • 2.5.
  • Evolutionary Algorithms with Structured Populations
  • 2.5.1.
  • Cellular EAs
  • 2.5.2.
  • Cooperative Coevolutionary EAs
  • 2.6.
  • Multi-Objective Evolutionary Algorithms
  • 1.2.
  • 2.6.1.
  • Basic Concepts in Multi-Objective Optimization
  • 2.6.2.
  • Hierarchical Multi-Objective Problem Optimization
  • 2.6.3.
  • Simultaneous Multi-Objective Problem Optimization
  • 2.7.
  • Conclusion
  • References
  • 3.
  • Vehicular Ad Hoc Networks
  • Survey On Optimization Problems For Mobile Ad Hoc Networks
  • 3.1.
  • Taxonomy of the Optimization Process
  • 3.1.1.
  • Online and Offline Techniques
  • 3.1.2.
  • Using Global or Local Knowledge
  • 3.1.3.
  • Centralized and Decentralized Systems
  • 3.2.
  • 1.2.1.
  • State of the Art
  • 3.2.1.
  • Topology Management
  • 3.2.2.
  • Broadcasting Algorithms
  • 3.2.3.
  • Routing Protocols
  • 3.2.4.
  • Clustering Approaches
  • 3.2.5.
Control code
ocn876514078
Dimensions
unknown
Extent
1 online resource (238 p.)
File format
unknown
Form of item
online
Isbn
9781118832011
Isbn Type
(electronic bk.)
Level of compression
unknown
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)876514078
Label
Evolutionary algorithms for mobile ad hoc networks, Bernabé Dorronsoro...[et al.], (electronic resource)
Publication
Note
Description based upon print version of record
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Color
multicolored
Contents
  • Wireless Access in Vehicular Environment (WAVE)
  • Protocol Optimization
  • 3.2.6.
  • Modeling the Mobility of Nodes
  • 3.2.7.
  • Selfish Behaviors
  • 3.2.8.
  • Security Issues
  • 3.2.9.
  • Other Applications
  • 3.3.
  • 1.2.2.
  • Conclusion
  • References
  • 4.
  • Mobile Networks Simulation
  • 4.1.
  • Signal Propagation Modeling
  • 4.1.1.
  • Physical Phenomena
  • 4.1.2.
  • Signal Propagation Models
  • Communication Access for Land Mobiles (CALM)
  • 4.2.
  • State of the Art of Network Simulators
  • 4.2.1.
  • Simulators
  • 4.2.2.
  • Analysis
  • 4.3.
  • Mobility Simulation
  • 4.3.1.
  • Mobility Models
  • 1.2.3.
  • 4.3.2.
  • State of the Art of Mobility Simulators
  • 4.4.
  • Conclusion
  • References
  • 5.
  • Proposed Optimization Framework
  • 5.1.
  • Architecture
  • 5.2.
  • C2C Network
  • Optimization Algorithms
  • 5.2.1.
  • Single-Objective Algorithms
  • 5.2.2.
  • Multi-Objective Algorithms
  • 5.3.
  • Simulators
  • 5.3.1.
  • Network Simulator: ns-3
  • 5.3.2.
  • 1.3.
  • Mobility Simulator: SUMO
  • 5.3.3.
  • Graph-Based Simulations
  • 5.4.
  • Experimental Setup
  • 5.5.
  • Conclusion
  • References
  • 6.
  • Broadcasting Protocol
  • Sensor Networks
  • 6.1.
  • The Problem
  • 6.1.1.
  • DFCN Protocol
  • 6.1.2.
  • Optimization Problem Definition
  • 6.2.
  • Experiments
  • 6.2.1.
  • Algorithm Configurations
  • 1.3.1.
  • 6.2.2.
  • Comparison of the Performance of the Algorithms
  • 6.3.
  • Analysis of Results
  • 6.3.1.
  • Building a Representative Subset of Best Solutions
  • 6.3.2.
  • Interpretation of the Results
  • 6.3.3.
  • Selected Improved DFCN Configurations
  • IEEE 1451
  • 6.4.
  • Conclusion
  • References
  • 7.
  • Energy Management
  • 7.1.
  • The Problem
  • 7.1.1.
  • AEDB Protocol
  • 7.1.2.
  • 1.3.2.
  • Optimization Problem Definition
  • 7.2.
  • Experiments
  • 7.2.1.
  • Algorithm Configurations
  • 7.2.2.
  • Comparison of the Performance of the Algorithms
  • 7.3.
  • Analysis of Results
  • 7.4.
  • Machine generated contents note:
  • IEEE 802.15.4
  • Selecting Solutions from the Pareto Front
  • 7.4.1.
  • Performance of the Selected Solutions
  • 7.5.
  • Conclusion
  • References
  • 8.
  • Network Topology
  • 8.1.
  • The Problem
  • 1.3.3.
  • 8.1.1.
  • Injection Networks
  • 8.1.2.
  • Optimization Problem Definition
  • 8.2.
  • Heuristics
  • 8.2.1.
  • Centralized
  • 8.2.2.
  • Distributed
  • ZigBee
  • 8.3.
  • Experiments
  • 8.3.1.
  • Algorithm Configurations
  • 8.3.2.
  • Comparison of the Performance of the Algorithms
  • 8.4.
  • Analysis of Results
  • 8.4.1.
  • Analysis of the Objective Values
  • 1.3.4.
  • 8.4.2.
  • Comparison with Heuristics
  • 8.5.
  • Conclusion
  • References
  • 9.
  • Realistic Vehicular Mobility
  • 9.1.
  • The Problem
  • 9.1.1.
  • 6LoWPAN
  • Vehicular Mobility Model
  • 9.1.2.
  • Optimization Problem Definition
  • 9.2.
  • Experiments
  • 9.2.1.
  • Algorithms Configuration
  • 9.2.2.
  • Comparison of the Performance of the Algorithms
  • 9.3.
  • 1.3.5.
  • Analysis of Results
  • 9.3.1.
  • Analysis of the Decision Variables
  • 9.3.2.
  • Analysis of the Objective Values
  • 9.4.
  • Conclusion
  • References
  • 10.
  • Summary And Discussion
  • Bluetooth
  • 10.1.
  • A New Methodology for Optimization in Mobile Ad Hoc Networks
  • 10.2.
  • Performance of the Three Algorithmic Proposals
  • 10.2.1.
  • Broadcasting Protocol
  • 10.2.2.
  • Energy-Efficient Communications
  • 10.2.3.
  • Network Connectivity
  • 1.3.6.
  • 10.2.4.
  • Vehicular Mobility
  • 10.3.
  • Global Discussion on the Performance of the Algorithms
  • 10.3.1.
  • Single-Objective Case
  • 10.3.2.
  • Multi-Objective Case
  • 10.4.
  • Conclusion
  • Wireless Industrial Automation System
  • References
  • 1.4.
  • 1.
  • Conclusion
  • References
  • 2.
  • Introduction To Evolutionary Algorithms
  • 2.1.
  • Optimization Basics
  • 2.2.
  • Evolutionary Algorithms
  • 2.3.
  • Basic Components of Evolutionary Algorithms
  • Introduction To Mobile Ad Hoc Networks
  • 2.3.1.
  • Representation
  • 2.3.2.
  • Fitness Function
  • 2.3.3.
  • Selection
  • 2.3.4.
  • Crossover
  • 2.3.5.
  • Mutation
  • 1.1.
  • 2.3.6.
  • Replacement
  • 2.3.7.
  • Elitism
  • 2.3.8.
  • Stopping Criteria
  • 2.4.
  • Panmictic Evolutionary Algorithms
  • 2.4.1.
  • Generational EA
  • Mobile Ad Hoc Networks
  • 2.4.2.
  • Steady-State EA
  • 2.5.
  • Evolutionary Algorithms with Structured Populations
  • 2.5.1.
  • Cellular EAs
  • 2.5.2.
  • Cooperative Coevolutionary EAs
  • 2.6.
  • Multi-Objective Evolutionary Algorithms
  • 1.2.
  • 2.6.1.
  • Basic Concepts in Multi-Objective Optimization
  • 2.6.2.
  • Hierarchical Multi-Objective Problem Optimization
  • 2.6.3.
  • Simultaneous Multi-Objective Problem Optimization
  • 2.7.
  • Conclusion
  • References
  • 3.
  • Vehicular Ad Hoc Networks
  • Survey On Optimization Problems For Mobile Ad Hoc Networks
  • 3.1.
  • Taxonomy of the Optimization Process
  • 3.1.1.
  • Online and Offline Techniques
  • 3.1.2.
  • Using Global or Local Knowledge
  • 3.1.3.
  • Centralized and Decentralized Systems
  • 3.2.
  • 1.2.1.
  • State of the Art
  • 3.2.1.
  • Topology Management
  • 3.2.2.
  • Broadcasting Algorithms
  • 3.2.3.
  • Routing Protocols
  • 3.2.4.
  • Clustering Approaches
  • 3.2.5.
Control code
ocn876514078
Dimensions
unknown
Extent
1 online resource (238 p.)
File format
unknown
Form of item
online
Isbn
9781118832011
Isbn Type
(electronic bk.)
Level of compression
unknown
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)876514078

Library Locations

    • InternetBorrow it
      Albany, Auckland, 0632, NZ
Processing Feedback ...