Uneven Clustering in Wireless Sensor Networks: A Systematic Review

Abstract

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes powered by batteries with limited energy, making efficient energy management a fundamental challenge for ensuring reliable network operation and extending network lifetime. To address this challenge, researchers have proposed various optimization techniques, among which clustering has emerged as one of the most effective approaches for improving energy efficiency and reducing communication overhead. Cluster-based WSNs offer several advantages over traditional network architectures, including enhanced bandwidth utilization, balanced energy consumption, improved network scalability, reduced routing complexity, increased link reliability, lower transmission delay, and greater network stability. Clustering techniques are generally classified into even clustering and uneven clustering. However, even clustering often suffers from the hotspot problem, where cluster heads located near the base station consume energy more rapidly due to increased data forwarding responsibilities, leading to premature node failure and reduced network lifetime. Uneven clustering addresses this issue by creating clusters of varying sizes, enabling more balanced energy consumption among cluster heads and minimizing communication load. This approach effectively mitigates the hotspot problem, improves load balancing, enhances network reliability, and prolongs the operational lifetime of WSNs. This paper presents a comprehensive review of uneven clustering techniques, discussing their design principles, classification, key characteristics, and recent advancements. Furthermore, various uneven clustering algorithms are systematically analyzed and compared based on their energy efficiency, load balancing capability, communication performance, and overall effectiveness in extending the lifespan of wireless sensor networks.