Unveiling Efficient Partial Charging Schedules for Wireless Rechargeable Sensor Networks Using Novel Aquila Optimization Approach

Abstract

There are many potential uses for wireless rechargeable sensor networks (WRSNs), making them an important and exciting field of research. Extending the network’s lifespan is challenging because of the sensors’ short battery life. However, developing appropriate charging schedules for mobile charging vehicles (MCVs) is a difficult problem. These charging schedule designs can have an influence on WRSNs overall consumption of energy and lifetime. We address the challenge of minimizing travel energy for MCVs in WRSNs. Our proposed solution includes a priority-based charging schedule that balances MCV travel time and charging time effectively. Additionally, we offer a method for selecting charging energy levels to conduct partial charges aiming to prolong the network’s lifespan. We have also incorporated the remaining lifetime of sensor nodes (SNs) as a crucial factor in mitigating the occurrence of dead SNs in the network. In this article, we partition the requested SNs into several partitions and assign an MCV to each region using the Aquila Optimization meta-heuristic approach. A heuristic-based partial charging method is proposed. We compare the outcome of our proposed technique with several other existing algorithms. The outcomes of the simulation indicate that our suggested method performs better than the others. Additionally, an analysis of variance and a post hoc analysis are carried out. We demonstrate, through comprehensive simulations and hypothesis testing, that the proposed scheme increases the number of replenished sensor nodes up to 36.36% and the charging utility up to 97.82% while decreasing the charging time and the number of dead sensor nodes up to 54.16% and 85.86%, respectively.