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<p>Wireless Sensor Networks (WSNs) play a vital role in applications ranging from smart cities to environmental monitoring, yet their performance is often limited by inefficient cluster head (CH) selection. This paper introduces OCHSAT, a novel clustering framework that integrates Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to achieve robust and adaptive CH selection. Unlike prior Multi-Attribute Decision-Making (MADM)-based approaches, OCHSAT dynamically considers residual energy, spatial centrality, and distance to the base station, ensuring balanced energy consumption and scalability. Extensive simulations demonstrate that OCHSAT significantly improves network performance, extending lifetime by up to 68%, reducing delay by 42%, and enhancing throughput and reliability by up to 58% and 79%, respectively, compared to state-of-the-art protocols. These results are statistically validated (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(p &lt; 0.05\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </math></EquationSource> </InlineEquation>), underscoring OCHSAT’s robustness. By enabling more sustainable and scalable WSN operations, OCHSAT contributes to applications aligned with global goals, including smart cities, clean water monitoring, and climate action.</p>

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Optimizing cluster heads for energy efficiency in WSNs using MADM approaches

  • 期刊论文

  • 发表日期：

    2026年1月26日

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  • Ali Kadhum M. Al-Qurabat,
  • Hadeel Majid Lateef,
  • Ali Z. K Matloob,
  • Alyaa Kadhim Mohammed

摘要

Wireless Sensor Networks (WSNs) play a vital role in applications ranging from smart cities to environmental monitoring, yet their performance is often limited by inefficient cluster head (CH) selection. This paper introduces OCHSAT, a novel clustering framework that integrates Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to achieve robust and adaptive CH selection. Unlike prior Multi-Attribute Decision-Making (MADM)-based approaches, OCHSAT dynamically considers residual energy, spatial centrality, and distance to the base station, ensuring balanced energy consumption and scalability. Extensive simulations demonstrate that OCHSAT significantly improves network performance, extending lifetime by up to 68%, reducing delay by 42%, and enhancing throughput and reliability by up to 58% and 79%, respectively, compared to state-of-the-art protocols. These results are statistically validated ( \(p < 0.05\) p < 0.05 ), underscoring OCHSAT’s robustness. By enabling more sustainable and scalable WSN operations, OCHSAT contributes to applications aligned with global goals, including smart cities, clean water monitoring, and climate action.