An efficient charging strategy for wireless sensor networks based on saturation degree and Enhanced Grey Wolf optimization.

Abstract

Wireless sensor networks play a vital role in a wide range of modern applications, from environmental monitoring to industrial automation. A key challenge in maintaining the long-term functionality of these networks lies in effective energy management, where recharging sensors is often more practical and economical than frequent battery replacement. One critical aspect of this process is the optimal placement of chargers to ensure maximum sensor coverage while minimizing deployment costs. This paper presents a hybrid optimization framework that combines graph-theoretical concepts-specifically the Degree of Saturation approach-with the Enhanced Grey Wolf Optimization algorithm to solve the charger placement problem in WSNs. The Degree of Saturation method identifies independent groups of sensors to reduce the number of chargers required, while Enhanced Grey Wolf Algorithm determines their optimal spatial positions to ensure efficient energy replenishment. Extensive simulations demonstrate the superiority of the proposed method over conventional techniques. Compared to wavelet-based approaches such as Haar (83%), Daubechies 2 (85%), Biorthogonal (86%), and Symlets 8 (85%), as well as evolutionary algorithms like Raindrop (87%) and Blackhole (91%), the proposed Enhanced Grey Wolf Optimization-based method achieves a significantly higher efficiency of 97%. These results highlight the robustness and effectiveness of the proposed approach for real-world Wireless sensor networks deployment.