Energy-efficient clustering in wireless sensor networks using multi-objective genetic algorithm with adaptive parameter

Muhammad Ejaz (), Muhammad Asim (), Gui Jinsong (), Samia Allaoua Chelloug () and Ahmed A. Abd El-Latif ()
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Muhammad Ejaz: Central South University
Muhammad Asim: Prince Sultan University
Gui Jinsong: Central South University
Samia Allaoua Chelloug: Princess Nourah bint Abdulrahman University
Ahmed A. Abd El-Latif: Prince Sultan University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2026, vol. 89, issue 1, No 1, 21 pages

Abstract: Abstract Wireless Sensor Networks (WSNs) play a vital role in modern digital infrastructure, enabling critical applications in environmental monitoring, industrial automation, healthcare, and smart cities. However, existing WSN clustering approaches suffer from three major limitations: they address optimization objectives in isolation rather than holistically, lack adaptive capabilities to handle dynamic network conditions, and fail to effectively balance trade-offs between energy efficiency, coverage quality, and network lifetime. This research aims to develop a comprehensive clustering optimization framework that simultaneously addresses multiple network performance metrics while providing dynamic adaptation capabilities. We propose the Multi-Objective Genetic Algorithm with Adaptive Parameters (MOGAA), integrating four key components: adaptive parameter control system, predictive energy consumption model, comprehensive fitness evaluation framework, and specialized genetic operators designed for WSN clustering optimization. Experimental results demonstrate significant improvements: 44.44% increase in energy efficiency compared to LEACH, 41.67% extension in network lifetime, and 20.40% improvement in throughput. MOGAA maintains optimal cluster distribution $$(mean: 9.0 nodes, \sigma : 3.95)$$ ( m e a n : 9.0 n o d e s , σ : 3.95 ) with exceptional coverage stability (coefficient of variation: 0.021) across various network configurations. These results have significant implications for real-world WSN deployments, particularly applications requiring long-term autonomous operation. MOGAA’s ability to maintain balanced performance across multiple objectives while adapting to network dynamics makes it valuable for critical monitoring applications and large-scale sensor networks.

Keywords: Wireless sensor networks; Clustering algorithms; Genetic algorithms; Energy efficiency; Adaptive parameters; Multi-objective optimization (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/s11235-025-01351-6

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