Bio-Inspired Computation in Telecommunications - 1st Edition - ISBN: 9780128015384, 9780128017432

Bio-Inspired Computation in Telecommunications

1st Edition

Authors: Xin-She Yang Su Fong Chien T.O. Ting
eBook ISBN: 9780128017432
Paperback ISBN: 9780128015384
Imprint: Morgan Kaufmann
Published Date: 6th February 2015
Page Count: 348
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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.


Researchers in artificial intelligence, telecommunication engineers, computer scientists

Table of Contents

  • 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


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© Morgan Kaufmann 2015
Morgan Kaufmann
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About the Author

Xin-She  Yang

Xin-She Yang

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi’an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).

Affiliations and Expertise

School of Science and Technology, Middlesex University, UK

Su Fong Chien

Chien Su Fong is an associate professor of Engineering & Technology at Multimedia University Malaysia where his research focuses in networking, wireless communications and optical switching technology. He is a frequent presenter at the IEEE International Conference on Communications and a member of the Optical Society of America (OSA).

Affiliations and Expertise

Associate Professor of Engineering & Technology, Multimedia University, Selangor, Malaysia

T.O. Ting

T.O. Ting is currently a Lecturer with the Department of Electrical and Electronic Engineering, Xian Jiaotong-Liverpool University. He obtained his Ph.D in Electrical Engineering from The Hong Kong Polytechnic University. His current research interests focus on the application of Computational Intelligence techniques in Engineering Optimization. He has recently presented his research as an invited speaker at the IEEE Asia Pacific Conference on Circuits and Systems and The Asia-Pacific Conference on Communications.

Affiliations and Expertise

Lecturer, Department of Electrical and Electronic Engineering, Xian Jiaotong-Liverpool University, Jiangsu, China


"...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, Bio-Inspired Computation in Telecommunications

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