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<p>As an integral constituent of the Internet of Things (IoT), wireless sensor networks (WSNs) are revolutionizing different aspects of everyday life through intelligent and cost-effective applications. The dual challenges of security and efficiency are the major concerns facing any WSN deployment. Clustering is widely recognized as one of the most effective strategies for enhancing the WSN lifespan. While cluster heads (CHs) serve a pivotal role by managing data aggregation and communication, if CHs are compromised, the integrity of the collected data is lost, posing a significant risk to the network’s reliability and effectiveness. This work proposes an enhanced walrus optimizer-based secure clustering approach (EWOSCA) for IoT-based WSNs under adversarial contexts. An enhanced walrus optimizer (EWO) is developed to address the key shortcomings of the original WO. It incorporates an adaptive inertia weight strategy to better balance exploration and exploitation, a transverse crossover mechanism to tackle premature stagnation by enhancing diversity and generating high-quality solutions, and a colony predation strategy (CPS) to tackle the limited adaptability by dynamically refining the search process using the best-performing solutions. The EWOSCA approach adapts EWO and prioritizes the selection of secure, reliable, and energy-efficient CHs. Furthermore, an Adaptive weighted average function, AwE(), is devised and utilized while designing the fitness function for adapting the algorithm in response to varying network conditions over time. Simulation results reveal that EWOSCA can effectively handle varying rates of malicious or compromised nodes and surpasses recent schemes in terms of effective clustering, energy efficiency, reliability, and overall WSN lifetime.</p>

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EWOSCA: an enhanced walrus optimizer-based secure clustering approach for IoT-based WSNs under adversarial contexts

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Ahmed M. Khedr,
  • Pravija Raj P. V.,
  • Reham R. Mostafa

摘要

As an integral constituent of the Internet of Things (IoT), wireless sensor networks (WSNs) are revolutionizing different aspects of everyday life through intelligent and cost-effective applications. The dual challenges of security and efficiency are the major concerns facing any WSN deployment. Clustering is widely recognized as one of the most effective strategies for enhancing the WSN lifespan. While cluster heads (CHs) serve a pivotal role by managing data aggregation and communication, if CHs are compromised, the integrity of the collected data is lost, posing a significant risk to the network’s reliability and effectiveness. This work proposes an enhanced walrus optimizer-based secure clustering approach (EWOSCA) for IoT-based WSNs under adversarial contexts. An enhanced walrus optimizer (EWO) is developed to address the key shortcomings of the original WO. It incorporates an adaptive inertia weight strategy to better balance exploration and exploitation, a transverse crossover mechanism to tackle premature stagnation by enhancing diversity and generating high-quality solutions, and a colony predation strategy (CPS) to tackle the limited adaptability by dynamically refining the search process using the best-performing solutions. The EWOSCA approach adapts EWO and prioritizes the selection of secure, reliable, and energy-efficient CHs. Furthermore, an Adaptive weighted average function, AwE(), is devised and utilized while designing the fitness function for adapting the algorithm in response to varying network conditions over time. Simulation results reveal that EWOSCA can effectively handle varying rates of malicious or compromised nodes and surpasses recent schemes in terms of effective clustering, energy efficiency, reliability, and overall WSN lifetime.