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<p>Energy efficiency is a fundamental concern in Wireless Sensor Networks (WSNs), which are widely deployed in applications such as environmental monitoring, military surveillance, and smart infrastructure. However, limited battery capacity poses a critical challenge, particularly in achieving optimal cluster head (CH) selection and routing. Traditional clustering methods often experience premature convergence, uneven energy distribution, and poor scalability. To address these limitations, this paper presents an Improved Snow Ablation Optimizer (ISAO), a biologically inspired optimization algorithm that mimics the natural processes of snow melting, refreezing, and avalanche drift for efficient CH selection. The proposed approach incorporates exploration, intensification, and diversity preservation strategies, along with a multi-objective fitness function that evaluates residual energy, communication distance, node density, energy consumption rate, and intra-cluster distance. Simulation results using a 100-node WSN setup reveal that ISAO consistently outperforms existing protocols such as CSO, CUWSN, MFOBR, EOCSR, and ECERO in terms of network lifetime, residual energy conservation, and data throughput. The ISAO framework achieves balanced energy utilization and significantly extends the operational lifespan of WSNs, offering a reliable and energy-efficient clustering solution.</p>

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ISAO: An Improved Snow Ablation Optimizer for Energy-Efficient Cluster Head Selection in Wireless Sensor Networks

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Yash Veer Singh,
  • Harikesh Singh,
  • Vinay Kumar Singh,
  • Pushpendra Singh,
  • Ashok Kumar Yadav

摘要

Energy efficiency is a fundamental concern in Wireless Sensor Networks (WSNs), which are widely deployed in applications such as environmental monitoring, military surveillance, and smart infrastructure. However, limited battery capacity poses a critical challenge, particularly in achieving optimal cluster head (CH) selection and routing. Traditional clustering methods often experience premature convergence, uneven energy distribution, and poor scalability. To address these limitations, this paper presents an Improved Snow Ablation Optimizer (ISAO), a biologically inspired optimization algorithm that mimics the natural processes of snow melting, refreezing, and avalanche drift for efficient CH selection. The proposed approach incorporates exploration, intensification, and diversity preservation strategies, along with a multi-objective fitness function that evaluates residual energy, communication distance, node density, energy consumption rate, and intra-cluster distance. Simulation results using a 100-node WSN setup reveal that ISAO consistently outperforms existing protocols such as CSO, CUWSN, MFOBR, EOCSR, and ECERO in terms of network lifetime, residual energy conservation, and data throughput. The ISAO framework achieves balanced energy utilization and significantly extends the operational lifespan of WSNs, offering a reliable and energy-efficient clustering solution.