Synthetic on-demand charging optimization with ADCMA for energy conservation in WRSN

Abstract

Recently, the research community has shown a growing interest in using mobile chargers to recharge the energy supply of sensor nodes in wireless rechargeable sensor network (WRSN). Mobile energy chargers offer a more dependable energy source compared to devices that harvest dynamic energy from the surrounding environment. This research introduces a synthetic on-demand charging optimization approach using Adaptive Crossover Mayfly Algorithm (ADCMA) to enhance the charging performance in WRSN. The genetic operator of roulette wheel selection is employed to choose possibly valuable solutions for crossover and the objective is to reduce the overall energy consumption of the system and the mobile charger's total distance traveled, while simultaneously enhancing its vacation time ratio. Unlike prior systems that required the mobile charger to visit and charge all nodes in every cycle, this strategy simply requires the mobile charger to visit a subset of nodes. The proposed ADCMA is also tested on 46 benchmark functions and applied to the on-demand charging model by comparing it with other well-known seven algorithms. The mathematical results demonstrate that the ADCMA effectively decreases the overall energy consumption and distance traveled by the mobile charging device, while still maintaining performance. This algorithm is particularly advantageous for real-world networks as it allows for the network to remain operational with reduced complexity compared to other approaches.