Advanced Charger Placement Strategies in Sensor Networks Using Graph Theory and Evolutionary Algorithms

Abstract

Efficient recharging of sensors is essential to ensure uninterrupted operation across a wide range of applications, and the strategic placement of chargers plays a crucial role in achieving this objective. This article addresses the optimization of wireless sensor recharging by focusing on two key phases: determining the minimum number of chargers required and identifying their optimal placement. In the first phase, the minimum number of chargers is determined using the Grundy coloring algorithm (GCA). In the second phase, the blackhole algorithm is applied to optimally position the chargers, aiming to maximize coverage and minimize redundancy. The effectiveness of the proposed method was validated through simulation experiments. Performance comparisons were conducted between the blackhole algorithm, which achieved 98.15% coverage including Haar (95.85%), Daubechies 2 (95.50%), Biorthogonal (96.01%), Symlets 8 (95.98%) wavelets, and the raindrop algorithm (96.24%). The results indicate that the proposed algorithm outperforms these methods in terms of coverage efficiency and optimal charger deployment, highlighting its potential for significantly enhancing the recharging process in wireless sensor networks.