Towards Perpetual Wireless Rechargeable Sensor Networks with Path Optimization of Mobile Chargers

Wireless Rechargeable Sensor Networks (WRSNs) are pivotal to providing sustainable power to an extensive array of recent technologies. Herein, energy replenishment of nodes takes place via Mobile chargers (MCs). However, optimizing their trajectories within the network is challenging, and finding a solution is hard. Most of the existing works have focused on the scheduling of the MC. Nonetheless, due to ignoring some important factors, such as charging time, energy consumption, and network coverage for optimized paths in dynamic environments, there is still room to improve network lifetimes. Optimization algorithms such as Ant colony optimization have proven to offer potential solutions. In this regard, we explore the Quantum Ant Colonization Optimization (QACO) algorithm as a sophisticated system merging quantum computing and ant behavior principles to determine optimal charging paths for the MC. The proposed scheme considers energy requirements, network topology, and communication protocols to enhance energy replenishment effectiveness. MCs can move around the network to boost their energy, but figuring out the best way for them to travel is crucial and objective of this paper. The experimental results show that QACO outperforms the competing algorithms in terms of various parameters.