Bi-level optimization for optimizing charging path and charging time in wireless rechargeable sensor networks

Abstract

In recent years, Wireless Rechargeable Sensor Networks (WRSNs) have adopted wireless energy transfer technology, which has emerged as a promising solution to the limited energy issues in traditional Wireless Sensor Networks (WSNs). Since the sensor’s lifetime is decided by the charging scheme of the Mobile Charger (MC), designing an effective charging algorithm is challenging. Although many efforts have been devoted to optimizing charging schemes in WRSNs, the studies still face several critical issues. Firstly, most previous studies assumed that the MC’s battery capacity is sufficient or unlimited, resulting in the MC can move and charges all sensors in a charging cycle. That may cause a long waiting time for energy-hurry sensors and significant exhaustion of the MC’s energy. Secondly, existing works often optimize the MC’s charging path, whereas the charging time has not been thoroughly considered. This work aims to solve the limitations above by optimizing both the charging path and charging time simultaneously under the MC ’s limited-energy constraint. Our objective is to minimize the energy depletion of sensor nodes. To this end, we leverage the advantage of the bi-level optimization approach and propose a charging algorithm with two levels: the charging path optimization at the upper level and the charging time optimization at the lower level. The proposed charging scheme combines Genetic Algorithm (GA) and Differential Evolutionary (DE) to identify the optimal charging path and time. We conducted extensive experiments to demonstrate the effectiveness of our charging scheme in comparison to the related studies.