A Systematic Literature Analysis on Dynamic Clustering Algorithms for WSN Lifetime Extension
Keywords:
Wireless Sensor Network, Dynamic Clustering, network life extension, Energy efficiency, Cluster Head (CH)Abstract
Wireless Sensor Networks (WSNs) are severely constrained by node energy resources, making network lifetime extension a major challenge. Clustering is recognized as a fundamental technique for energy conservation by reducing long-distance data transmission, where nodes are organized into groups and elect a Cluster Head (CH) for data aggregation. However, WSNs operate in dynamic environments—with constantly changing topologies and energy levels—making static clustering algorithms fail to adapt efficiently. Therefore, Dynamic Clustering Algorithms emerge as a smarter solution, allowing for periodic adjustment of CHs and cluster members based on real-time conditions such as residual energy levels, inter-node distances, and cluster density. This Systematic Literature Analysis (SLR) aims to critically identify, analyze, and synthesize current research on dynamic algorithms specifically designed for WSN lifetime extension. Specifically, this review will explore innovative algorithms that integrate advanced techniques such as fuzzy logic for uncertain decision-making, machine learning for network condition prediction, and swarm-based optimization for adaptive CH selection. We will review key performance metrics, including First Node Dead (FND) lifetime improvement, total network lifetime, and energy load balance across the network. The results of this SLR are expected to provide a comprehensive overview of the state of the art, clearly highlighting the strengths and weaknesses of various dynamic clustering approaches, and identifying the most promising gaps and future research directions to guide the development of more energy-efficient and reliable clustering protocols for next-generation WSNs.
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