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Energy-efficient clustering in wireless sensor networks using multi-objective genetic algorithm with adaptive parameter
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AI
DOI：10.1007/s11235-025-01351-6.png)
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Abstract
En 中文
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,sigma:3.95)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(mean: 9.0 nodes, \sigma : 3.95)$$\end{document} 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
Journal
IF:
2.3
Papers:
161
Citations:
2.0K
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