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Energy efficient clustering protocol using hybrid bald eagle search optimization algorithm for improving network longevity in WSNs

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Abstract

The limited energy possessed by each individual sensor node makes the process of designing and developing an efficient wireless sensor network as a herculean task. The process of clustering and successive cluster head (CH) process with energy efficiency is essential for extending network lifetime and sustaining the existence of alive sensor nodes in the network. In this paper, a multi-objective optimization algorithm based on Hybrid Bald Eagle Search Optimization Algorithm (HBESAOA) is presented as the energy clustering solution for targeting on the process of extending the sensor nodes’ network lifetime. This multi-objective optimization HBESAOA algorithm included the fitness evaluating factors of energy, delay, distance, node centrality and node degree into account during the process of CH selection. It specifically adopted the well-balanced exploration and exploitation capabilities of Bald Eagle Search (BES), such that potential search process during clustering and subsequent CH selection is attained in the network. It is prevented with the potential of attaining maximized solution diversity with prevented premature convergence problem. The results of this HBESAOA scheme is identified to be significant in increasing the mean network lifetime by 26.78%, compared to the baseline CH selection schemes independent to the position of the sink node. The energy sustenance in the network is also predominantly improved by 21.98%, independent to the scalable increase in the number of sensor nodes on comparison with the competitive approaches used for evaluation.

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  1. Department of Information Technology, CVR College of Engineering, Mangalpally, Ranga Reddy District, Hyderabad, 501510, Telangana, India

    Sengathir Janakiraman

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Janakiraman, S. Energy efficient clustering protocol using hybrid bald eagle search optimization algorithm for improving network longevity in WSNs. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18155-6

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  • DOI: https://doi.org/10.1007/s11042-024-18155-6

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