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

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 optimizat...

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Vydáno v:Telecommunication systems Ročník 89; číslo 1; s. 1
Hlavní autoři: Ejaz, Muhammad, Asim, Muhammad, Jinsong, Gui, Chelloug, Samia Allaoua, El-Latif, Ahmed A. Abd
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer Nature B.V 01.03.2026
Témata:
Adaptation
Adaptive algorithms
Blockchain
Clustering
Coefficient of variation
Communication
Comparative analysis
Digital twins
Energy consumption
Energy efficiency
Environmental monitoring
Genetic algorithms
Multiple objective analysis
Optimization
Optimization techniques
Parameters
Performance measurement
Predictive control
Sensors
Wireless sensor networks
ISSN:1018-4864, 1572-9451
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Shrnutí: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:9.0nodes,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.
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ISSN:1018-4864
1572-9451
DOI:10.1007/s11235-025-01351-6