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Clustering Optimization for WSN Based on Nature-Inspired Algorithms

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Nature-Inspired Computation in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 637))

Abstract

This chapter presents a set of newly proposed swarm intelligent methods and applies these method in the domain of wireless sensor network (WSN) for the purpose of cluster head selection. Life time of WSNs is always the main performance goal. Cluster head (CH) selection is one of the factors affecting the life time of WSNs and hence it is a very promising area of research. Swarm-intelligence is a very hot area of research which mimics natural behavior to solve optimization problems. This chapter formulates the CH selection problem as an optimization problem and tackles this problem using different emergent swarm optimizers. The proposed formulation is assessed using different performance indicators and is compared against one of the very common CH selection methods namely LEACH.

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Author information

Authors and Affiliations

  1. Faculty of Computer Studies, Arab Open University, Cairo, Egypt

    Marwa Sharawi

  2. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Eid Emary

Authors
  1. Marwa Sharawi
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  2. Eid Emary
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Corresponding author

Correspondence to Marwa Sharawi .

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Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

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Sharawi, M., Emary, E. (2016). Clustering Optimization for WSN Based on Nature-Inspired Algorithms. In: Yang, XS. (eds) Nature-Inspired Computation in Engineering. Studies in Computational Intelligence, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-30235-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-30235-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30233-1

  • Online ISBN: 978-3-319-30235-5

  • eBook Packages: EngineeringEngineering (R0)

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