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<p>Energy-efficient coverage remains a critical challenge in wireless sensor networks (WSNs), particularly under probabilistic sensing models and resource-constrained environments. To address this, we propose a novel sleep scheduling algorithm that integrates node utility with a priority strategy for critical coverage targets. Our approach begins by constructing a hierarchical disjoint cover set (H-DCS) to reduce computational complexity and decouple global coverage constraints. We then introduce a utility-prioritized key target optimization (UPKO) framework, which dynamically balances node residual energy against coverage contribution, while ensuring that targets with minimal predicted lifetime are prioritized. The integrated algorithm, termed UCTF-SS, selectively activates a subset of nodes to maintain full coverage while maximizing network lifetime. Extensive simulations across multiple network scales and parameter settings demonstrate that UCTF-SS significantly outperforms existing methods, including MUA-WPT and GA-based scheduling, in terms of energy consumption, coverage sustainability, and network longevity. The proposed method also exhibits strong scalability and adaptability to large-scale deployments, offering a practical and efficient solution for energy-aware WSN operations.</p>

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Optimizing sleep scheduling in wireless sensor networks via node utility and critical target prioritization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

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  • Jingxue Wu,
  • Songling Tian,
  • Xiaoqian Qi,
  • Zhuoke Cai,
  • Peng Wang

摘要

Energy-efficient coverage remains a critical challenge in wireless sensor networks (WSNs), particularly under probabilistic sensing models and resource-constrained environments. To address this, we propose a novel sleep scheduling algorithm that integrates node utility with a priority strategy for critical coverage targets. Our approach begins by constructing a hierarchical disjoint cover set (H-DCS) to reduce computational complexity and decouple global coverage constraints. We then introduce a utility-prioritized key target optimization (UPKO) framework, which dynamically balances node residual energy against coverage contribution, while ensuring that targets with minimal predicted lifetime are prioritized. The integrated algorithm, termed UCTF-SS, selectively activates a subset of nodes to maintain full coverage while maximizing network lifetime. Extensive simulations across multiple network scales and parameter settings demonstrate that UCTF-SS significantly outperforms existing methods, including MUA-WPT and GA-based scheduling, in terms of energy consumption, coverage sustainability, and network longevity. The proposed method also exhibits strong scalability and adaptability to large-scale deployments, offering a practical and efficient solution for energy-aware WSN operations.