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Gaussian Mixture Model-Based Clustering for Energy Saving in WSN

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Computing Science, Communication and Security (COMS2 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1861))

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Abstract

In wireless sensor networks, nodes have limited access to energy sources and must make efficient use of what they have. Energy consumption may be decreased and network life can be prolonged via the process of clustering. To reduce the network’s power consumption and increase its lifespan, we used a new clustering technique in this work. Centralized cluster formation and decentralized cluster heads form the basis of this stage of clustering. Clusters are determined via a centralized Gaussian mixture model (GMM) technique, and once they are generated, they don’t change. After that, it chooses which cluster heads (CHs) should spin. Inside those clusters to minimize energy consumption prior to the data transmission phase to the base station (BS), taking into account the varying quantities of energy in the nodes. Thus, the proposed approach not only effectively addresses the energy consumption problem, but also significantly lengthens the lifespan of the network. The results demonstrate the following ways in which the suggested method lessens the burden on network resources. It increases network lifetime by 301%, 131%, and 122%, decreases energy consumption by 20.53%, 6.14%, and 5%, and increases throughput by 47%, 9%, and 4% when compared to the Flat, FUCA, and FCMDE protocols.

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Author information

Authors and Affiliations

  1. Electrical Engineering Technical College, Department of Computer Technical Engineering, Middle Technical University (MTU), Al Doura, 10022, Baghdad, Iraq

    Mohammed Saleh Mutar & Dalal Abdulmohsin Hammood

  2. Technical Instructors Training Institute, Middle Technical University, Baghdad, Iraq

    Seham Ahmed Hashem

Authors
  1. Mohammed Saleh Mutar
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  2. Dalal Abdulmohsin Hammood
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  3. Seham Ahmed Hashem
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Corresponding author

Correspondence to Dalal Abdulmohsin Hammood .

Editor information

Editors and Affiliations

  1. Ganpat University, Mehsana, India

    Nirbhay Chaubey

  2. Kerala University of Digital Sciences, Innovation and Technology, Trivandrum, India

    Sabu M. Thampi

  3. Taylor's University, Subang Jaya, Malaysia

    Noor Zaman Jhanjhi

  4. Ganpat University, Mehsana, India

    Satyen Parikh

  5. Ganpat University, Mehsana, India

    Kiran Amin

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Mutar, M.S., Hammood, D.A., Hashem, S.A. (2023). Gaussian Mixture Model-Based Clustering for Energy Saving in WSN. In: Chaubey, N., Thampi, S.M., Jhanjhi, N.Z., Parikh, S., Amin, K. (eds) Computing Science, Communication and Security. COMS2 2023. Communications in Computer and Information Science, vol 1861. Springer, Cham. https://doi.org/10.1007/978-3-031-40564-8_9

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  • DOI: https://doi.org/10.1007/978-3-031-40564-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40563-1

  • Online ISBN: 978-3-031-40564-8

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