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A Survey of Recent Particle Swarm Optimization (PSO)-Based Clustering Approaches to Energy Efficiency in Wireless Sensor Networks

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Application of Machine Learning and Deep Learning Methods to Power System Problems

Part of the book series: Power Systems ((POWSYS))

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Abstract

Wireless sensor networks (WSNs) include a set of spatially dedicated sensors to observe and record the physical conditions (e.g., wind, temperature, pressure, etc.) of the target area and transmit the collected data to a central location. WSNs have been gaining an overwhelming interest for industrial applications due to their relatively low-cost and simple frameworks. In particular, they have also been recognized as a promising tool in power systems. Despite a growing number of successful applications, one of the crucial limitations is the energy shortages of sensor nodes. One of the most preferred ways to deal with this issue is to apply clustering, which is used to divide sensors into groups for efficient usage of limited energy resources. In the literature, considerable attempts can be observed to deal with the energy shortages of WSNs using clustering approaches. However, it is not possible to see a work that specifically seeks to reflect the profile of particle swarm optimization (PSO)-based clustering approaches in this field. This chapter, therefore, aims to survey PSO-based approaches proposed to deal with energy efficiency in WSNs.

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Authors and Affiliations

  1. Department of Software Engineering, Mehmet Akif Ersoy University, Burdur, Turkey

    Emrah Hancer

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  1. Emrah Hancer
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Correspondence to Emrah Hancer .

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Editors and Affiliations

  1. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Morteza Nazari-Heris

  2. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Somayeh Asadi

  3. Department of Energy Technology Aalborg University, Aalborg, Denmark

    Behnam Mohammadi-Ivatloo

  4. Deakin University, Geelong, VIC, Australia

    Moloud Abdar

  5. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Houtan Jebelli

  6. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Milad Sadat-Mohammadi

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Hancer, E. (2021). A Survey of Recent Particle Swarm Optimization (PSO)-Based Clustering Approaches to Energy Efficiency in Wireless Sensor Networks. In: Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H., Sadat-Mohammadi, M. (eds) Application of Machine Learning and Deep Learning Methods to Power System Problems. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-77696-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-77696-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77695-4

  • Online ISBN: 978-3-030-77696-1

  • eBook Packages: EnergyEnergy (R0)

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