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<p>Advancements in technology have enabled the deployment of Wireless Sensor Networks (WSNs) comprising compact, energy-constrained nodes for environmental monitoring. However, limited energy resources pose a significant challenge to network longevity. Clustering has emerged as a key technique to improve energy efficiency by reducing redundant data transmission and optimizing communication pathways. This paper proposes a novel clustering method utilizing the Starfish Optimization Algorithm (SFOA) to address challenges like uneven clustering and cluster head (CH) overloading. The proposed method incorporates dynamic strategy switching, a fitness function integrating residual energy, node density, and intra-cluster distances, and a re-clustering mechanism triggered by energy thresholds. Simulation results demonstrate that the proposed SFOA-based protocol improves network throughput by up to 18.60%, reduces the number of dead nodes by 24.30%, and increases residual energy retention by 21.70% compared to benchmark protocols such as CSO, MFOBSR, BOCSR, and ECBRO. These results establish the efficacy of the SFOA-based protocol in enhancing energy efficiency, network stability, and throughput for large-scale WSNs.</p>

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Starfish Optimization Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Yash Veer Singh,
  • Prasant Singh Yadav,
  • Shadab Khan,
  • Vinay Kumar Singh

摘要

Advancements in technology have enabled the deployment of Wireless Sensor Networks (WSNs) comprising compact, energy-constrained nodes for environmental monitoring. However, limited energy resources pose a significant challenge to network longevity. Clustering has emerged as a key technique to improve energy efficiency by reducing redundant data transmission and optimizing communication pathways. This paper proposes a novel clustering method utilizing the Starfish Optimization Algorithm (SFOA) to address challenges like uneven clustering and cluster head (CH) overloading. The proposed method incorporates dynamic strategy switching, a fitness function integrating residual energy, node density, and intra-cluster distances, and a re-clustering mechanism triggered by energy thresholds. Simulation results demonstrate that the proposed SFOA-based protocol improves network throughput by up to 18.60%, reduces the number of dead nodes by 24.30%, and increases residual energy retention by 21.70% compared to benchmark protocols such as CSO, MFOBSR, BOCSR, and ECBRO. These results establish the efficacy of the SFOA-based protocol in enhancing energy efficiency, network stability, and throughput for large-scale WSNs.