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<p>Wireless Sensor Networks (WSNs) play a vital role in home automation, environmental monitoring, and industrial control domains. A critical challenge in WSNs is optimizing energy consumption while maintaining network coverage and connectivity. This study introduces a hybrid clustering method that leverages the Ordered Weighted Averaging (OWA) operator to enable dynamic and context-aware cluster head (CH) selection based on multiple decision criteria under uncertainty. Unlike traditional static or purely dynamic clustering strategies, the proposed method dynamically adjusts to real-time network conditions, offering improved adaptability and robustness. Two experimental scenarios were simulated to evaluate performance. In Case 1 (Sink at (50,&#xa0;50), 100 nodes), the proposed algorithm demonstrated superior energy conservation, maintaining 42 units of residual energy at round 200 compared to 18-35 units in competing algorithms. It also sustained more active nodes and CHs, with more stable energy depreciation trends across operational rounds. In Case 2 (Sink at (0,&#xa0;300), 200 nodes), the proposed method again outperformed benchmarks, achieving the highest remaining energy and the most extended network lifetime. Statistical validation using the Half Node Dead (HND) metric confirmed a mean HND of 492 rounds, compared to 431.7, 306.3, and 150.4 for the baseline algorithms. Overall, the proposed OWA-based clustering approach improves energy efficiency, enhances CH stability, and extends network lifetime by 10-12%, making it a promising solution for energy-constrained WSN deployments.</p>

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Ordered weighted averaging (OWA) Operator-based Static and Dynamic Clustering for Optimized Data Transmission in WSN

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Prince Rajpoot,
  • Ajeet Kumar,
  • Amit K. Singh,
  • Shivendu Mishra,
  • Shivendra Kumar Pandey,
  • Sharad Verma

摘要

Wireless Sensor Networks (WSNs) play a vital role in home automation, environmental monitoring, and industrial control domains. A critical challenge in WSNs is optimizing energy consumption while maintaining network coverage and connectivity. This study introduces a hybrid clustering method that leverages the Ordered Weighted Averaging (OWA) operator to enable dynamic and context-aware cluster head (CH) selection based on multiple decision criteria under uncertainty. Unlike traditional static or purely dynamic clustering strategies, the proposed method dynamically adjusts to real-time network conditions, offering improved adaptability and robustness. Two experimental scenarios were simulated to evaluate performance. In Case 1 (Sink at (50, 50), 100 nodes), the proposed algorithm demonstrated superior energy conservation, maintaining 42 units of residual energy at round 200 compared to 18-35 units in competing algorithms. It also sustained more active nodes and CHs, with more stable energy depreciation trends across operational rounds. In Case 2 (Sink at (0, 300), 200 nodes), the proposed method again outperformed benchmarks, achieving the highest remaining energy and the most extended network lifetime. Statistical validation using the Half Node Dead (HND) metric confirmed a mean HND of 492 rounds, compared to 431.7, 306.3, and 150.4 for the baseline algorithms. Overall, the proposed OWA-based clustering approach improves energy efficiency, enhances CH stability, and extends network lifetime by 10-12%, making it a promising solution for energy-constrained WSN deployments.