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Comparison between Genetic Algorithm and PSO for Wireless Sensor Networks

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Smart Computing and Informatics

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 77))

Abstract

One of the most promising algorithms for network optimization is the particle swarm optimization (PSO) and genetic algorithm (GA). The paper is about comparing these two as applied to wireless sensor networks. If a sink is placed at a longer distance from the sensors then the battery life (energy) drains faster, and it reduces the life of the network. Our analysis shows that optimized clustering technique of sensors can minimize the communication distance and can help to increase the network stability. GA and PSO can optimize the cluster formation of sensors. Simulation results have shown us that PSO performs better than GA for clustering algorithms in wireless sensor networks.

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Authors and Affiliations

  1. Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

    Pritee Parwekar & S. Vani Mounika

  2. GITAM University, Visakhapatnam, India

    Sireesha Rodda

Authors
  1. Pritee Parwekar
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  2. Sireesha Rodda
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  3. S. Vani Mounika
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Correspondence to Pritee Parwekar .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

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Parwekar, P., Rodda, S., Vani Mounika, S. (2018). Comparison between Genetic Algorithm and PSO for Wireless Sensor Networks. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Computing and Informatics . Smart Innovation, Systems and Technologies, vol 77. Springer, Singapore. https://doi.org/10.1007/978-981-10-5544-7_39

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  • DOI: https://doi.org/10.1007/978-981-10-5544-7_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5543-0

  • Online ISBN: 978-981-10-5544-7

  • eBook Packages: EngineeringEngineering (R0)

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