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Minimizing the Energy Consumption of WSN Using Noble SMOWA-GA Algorithm

Author

Listed:
  • Sudip Kumar De

    (Asansol Engineering College, Asansol, India)

  • Avishek Banerjee

    (Asansol Engineering College, Asansol, India)

  • Koushik Majumder

    (Maulana Abul Kalam Azad University of Technology, West Bengal, India)

  • Samiran Chattopadhyay

    (Institute for Advancing Intelligence, TCG Crest and Jadavpur University, West Bengal, India)

Abstract

In this paper, the authors have concentrated on the practical application of optimization problems related to the minimization of the energy consumption of WSN. Here a noble algorithm called Self-adaptive Multi-Objective Weighted Approach-Genetic Algorithm (SMOWA-GA) is proposed to resolve the optimization problem. A multi-objective optimization problem was chosen as the subject of this research. The main objective of the paper is to propose and apply different WSN node deployment strategies to design an efficient Wireless Sensor Network to minimize the energy consumption of the whole WSN. The statistical analysis also has been carried out on the obtained data of the optimization techniques. To analyze the obtained result a statistical tool, Wilcoxon rank-sum test has been used. The Wilcoxon rank-sum test assists in determining whether the population chosen for the experiment (SMOWA-GA) is accurate. The statistical analysis also will help the reader to gather a detailed analysis of obtained data from the Multi-objective energy-efficient optimization problem.

Suggested Citation

  • Sudip Kumar De & Avishek Banerjee & Koushik Majumder & Samiran Chattopadhyay, 2022. "Minimizing the Energy Consumption of WSN Using Noble SMOWA-GA Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-22, January.
    • Handle: RePEc:igg:jamc00:v:13:y:2022:i:1:p:1-22
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    References listed on IDEAS

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    1. Theodoros Gevezes & Leonidas Pitsoulis, 2015. "A greedy randomized adaptive search procedure with path relinking for the shortest superstring problem," Journal of Combinatorial Optimization, Springer, vol. 29(4), pages 859-883, May.
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