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<p>In the realm of wireless sensor networks (WSN), clustering schemes stand out as effective approaches to optimize resource utilization, particularly in minimizing overhead and enhancing energy efficiency to extend network lifespan. Clustering optimisation that takes energy considerations into account is a difficult NP optimisation issue. In light of this difficulty, meta-heuristic optimisation algorithms stand out as a possible solution; they may greatly enhance energy efficiency, which would lead to the sustainability of the network. This article introduces a novel solution, the Hybrid African Buffalo and Remora Optimization Algorithm (HABROA), designed to achieve energy stability and prolong network lifetime through the judicious selection of cluster heads (CH) during the clustering process. HABROA is specifically tailored to navigate the intricate trade-off between exploitation and exploration rates, thereby facilitating the efficient identification of Cluster Head Candidates (CHS). This strategic balance contributes to the sustained longevity and energy stability of the network. The proposed HABROA model exhibits notable performance metrics, surpassing alternative methods. With a Mean Packet Delivery of 96%, a Mean Residual Energy of 0.130, and an End-to-End Delay of 0.76, the results demonstrate the efficacy of HABROA in achieving superior outcomes. This research significantly contributes to the field by presenting a robust approach for optimizing energy consumption and ensuring the prolonged functionality of WSN.</p>

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HABROA: Elevating wireless sensor networks through heterogeneous learning and advanced optimization strategies

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • A. Swaminathan,
  • R. Saravanan,
  • Baskar Kasi,
  • S. Ramaraju

摘要

In the realm of wireless sensor networks (WSN), clustering schemes stand out as effective approaches to optimize resource utilization, particularly in minimizing overhead and enhancing energy efficiency to extend network lifespan. Clustering optimisation that takes energy considerations into account is a difficult NP optimisation issue. In light of this difficulty, meta-heuristic optimisation algorithms stand out as a possible solution; they may greatly enhance energy efficiency, which would lead to the sustainability of the network. This article introduces a novel solution, the Hybrid African Buffalo and Remora Optimization Algorithm (HABROA), designed to achieve energy stability and prolong network lifetime through the judicious selection of cluster heads (CH) during the clustering process. HABROA is specifically tailored to navigate the intricate trade-off between exploitation and exploration rates, thereby facilitating the efficient identification of Cluster Head Candidates (CHS). This strategic balance contributes to the sustained longevity and energy stability of the network. The proposed HABROA model exhibits notable performance metrics, surpassing alternative methods. With a Mean Packet Delivery of 96%, a Mean Residual Energy of 0.130, and an End-to-End Delay of 0.76, the results demonstrate the efficacy of HABROA in achieving superior outcomes. This research significantly contributes to the field by presenting a robust approach for optimizing energy consumption and ensuring the prolonged functionality of WSN.