An energy efficient cluster head selection in priority region aware wireless sensor networks using metaheuristic algorithms

The cluster head (CH) selection problem is one of the challenges posed by wireless sensor network (WSN) design, where nodes assume leadership roles. The primary objective of this problem is energy conservation, as becoming a CH requires high energy consumption. Therefore, optimizing the CH selection process is crucial. Despite numerous attempts to solve this problem, existing algorithms do not consider area prioritization, where certain regions such as industrial facilities with hazardous zones and military surveillance areas require special attention. This work first describes the standard CH selection problem in non-priority environments and then introduces priority region-aware WSNs. It then presents how energy-efficient CH selection using metaheuristics, with a priority- and energy-aware fitness function developed in this study, can be performed in such networks for the first time in the literature. The findings from comprehensive simulation-based experiments demonstrate the superior performance of both classical and state-of-the-art metaheuristic-driven approaches compared to the baseline Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm. Specifically, the Adaptive Differential Evolution with Optional External Archive (JADE) algorithm improves the performance of LEACH by up to 16% in terms of the total priority of transferred packets. Additionally, it can extend the lifetime of nodes in high-priority regions by up to 27% to 44%.