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Bio-Inspired Computation in Telecommunications,

Edition 1

Editors: By Xin-She Yang, Su Fong Chien and T.O. Ting

Publication Date: 06 Feb 2015

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Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students.

About the author

By Xin-She Yang, School of Science and Technology, Middlesex University, UK; Su Fong Chien, Associate Professor of Engineering & Technology, Multimedia University, Selangor, Malaysia and T.O. Ting, Lecturer, Department of Electrical and Electronic Engineering, Xian Jiaotong-Liverpool University, Jiangsu, China

Preface
List of Contributors
Chapter 1: Bio-Inspired Computation and Optimization: An Overview
- Abstract
- 1.1 Introduction
- 1.2 Telecommunications and optimization
- 1.3 Key challenges in optimization
- 1.4 Bio-inspired optimization algorithms
- 1.5 Artificial neural networks
- 1.6 Support vector machine
- 1.7 Conclusions
Chapter 2: Bio-Inspired Approaches in Telecommunications
- Abstract
- 2.1 Introduction
- 2.2 Design problems in telecommunications
- 2.3 Green communications
- 2.4 Orthogonal frequency division multiplexing
- 2.5 OFDMA model considering energy efficiency and quality-of-service
- 2.6 Conclusions
Chapter 3: Firefly Algorithm in Telecommunications
- Abstract
- 3.1 Introduction
- 3.2 Firefly algorithm
- 3.3 Traffic characterization
- 3.4 Applications in wireless cooperative networks
- 3.5 Concluding remarks
Chapter 4: A Survey of Intrusion Detection Systems Using Evolutionary Computation
- Abstract
- Acknowledgments
- 4.1 Introduction
- 4.2 Intrusion detection systems
- 4.3 The method: evolutionary computation
- 4.4 Evolutionary computation applications on intrusion detection
- 4.5 Conclusion and future directions
Chapter 5: VoIP Quality Prediction Model by Bio-Inspired Methods
- Abstract
- 5.1 Introduction
- 5.2 Speech quality measurement background
- 5.3 Modeling methods
- 5.4 Experimental testbed
- 5.5 Results and discussion
- 5.6 Conclusions
Chapter 6: On the Impact of the Differential Evolution Parameters in the Solution of the Survivable Virtual Topology-Mapping Problem in IP-Over-WDM Networks
- Abstract
- 6.1 Introduction
- 6.2 Problem formulation
- 6.3 DE algorithm
- 6.4 Illustrative example
- 6.5 Results and discussion
- 6.6 Conclusions
Chapter 7: Radio Resource Management by Evolutionary Algorithms for 4G LTE-Advanced Networks
- Abstract
- 7.1 Introduction to radio resource management
- 7.2 LTE-A technologies
- 7.3 Self-organization using evolutionary algorithms
- 7.4 EAs in LTE-A
- 7.5 Conclusion
Chapter 8: Robust Transmission for Heterogeneous Networks with Cognitive Small Cells
- Abstract
- 8.1 Introduction
- 8.2 Spectrum sensing for cognitive radio
- 8.3 Underlay spectrum sharing
- 8.4 System Model
- 8.5 Problem formulation
- 8.6 Sparsity-enhanced mismatch model (SEMM)
- 8.7 Sparsity-enhanced mismatch model-reverse DPSS (SEMMR)
- 8.8 Precoder design using the SEMM and SEMMR
- 8.9 Simulation results
- 8.10 Conclusion
Chapter 9: Ecologically Inspired Resource Distribution Techniques for Sustainable Communication Networks
- Abstract
- 9.1 Introduction
- 9.2 Consumer-resource dynamics
- 9.3 Resource competition in the NGN
- 9.4 Conditions for stability and coexistence
- 9.5 Application for LTE load balancing
- 9.6 Validation and results
- 9.7 Conclusions
Chapter 10: Multiobjective Optimization in Optical Networks
- Abstract
- 10.1 Introduction
- 10.2 Multiobjective optimization
- 10.3 RWA Problem
- 10.4 WCA Problem
- 10.5 p-Cycle protection
- 10.6 Conclusions
Chapter 11: Cell-Coverage-Area Optimization Based on Particle Swarm Optimization (PSO) for Green Macro Long-Term Evolution (LTE) Cellular Networks
- Abstract
- Acknowledgment
- 11.1 Introduction
- 11.2 Related works
- 11.3 Mechanism of proposed cell-switching scheme
- 11.4 System model and problem formulation
- 11.5 PSO algorithm
- 11.6 Simulation results and discussion
- 11.7 Conclusion
Chapter 12: Bio-Inspired Computation for Solving the Optimal Coverage Problem in Wireless Sensor Networks: A Binary Particle Swarm Optimization Approach
- Abstract
- Acknowledgments
- 12.1 Introduction
- 12.2 Optimal coverage problem in WSN
- 12.3 BPSO for OCP
- 12.4 Experiments and comparisons
- 12.5 Conclusion
Chapter 13: Clonal-Selection-Based Minimum-Interference Channel Assignment Algorithms for Multiradio Wireless Mesh Networks
- Abstract
- 13.1 Introduction
- 13.2 Problem formulation
- 13.3 Clonal-Selection-Based algorithms for the channel assignment problem
- 13.4 Performance evaluation
- 13.5 Concluding remarks
Index

Title Reviews

"...reading this book will broaden your horizons with regard to how one could solve optimization problems by applying bio-inspired algorithms, with particular emphasis on telecommunications networks...It could be used for courses related to telecommunications, as well as for courses related to advanced algorithmics."—Computing Reviews

ISBN: 9780128015384

Page Count: 348

Retail Price (USD) :

Yang, Swarm Intelligence and Bio-Inspired Computation, Elsevier, 9780124051638, May 2013 450 pgs., $125.00
Yang, Nature-Inspired Optimization Algorithms, Elsevier, 9780124167438, Mar 2013, 300 pgs, $74.96

Researchers in artificial intelligence, telecommunication engineers, computer scientists