Dynamic Charging Scheduling and Path Planning Scheme for Multiple MC-enabled On-demand Wireless Rechargeable Sensor Networks

With the advancement of wireless energy transfer, Wireless Rechargeable Sensor Networks (WRSNs) have become increasingly popular for efficiently charging sensor nodes. In WRSNs, determining the charging schedule for Mobile Chargers (MCs) is critical for reducing maintenance costs and improving charging efficiency. This is termed the Charging Scheduling Problem (CSP), which is proven to be NP-hard in nature. The existing schemes lack a comprehensive approach to determine the optimal number of MCs and often set a fixed charging threshold for the sensor nodes, degrading charging efficiency in dynamic networks. Additionally, relying on a single MC is unrealistic and impractical for large-scale networks, necessitating a more advanced charging strategy. Thus, this study proposes a dynamic and multi-node charging scheduling scheme named Partitioning-based Charging Schedule for Multiple Mobile Chargers (PCSMMC). The PCSMMC utilizes the traffic load of sensor nodes and energy load of MCs to estimate the optimal number of MCs and computes the progressive threshold for sensor nodes to improve the charging efficiency. Moreover, potential sojourn locations are determined and multiple network factors are integrated into a multi-attribute decision-making process to achieve an efficient charging scheduling and path planning scheme for multiple MCs. The objective of PCSMMC is to enhance the survivability rate of sensor nodes and decrease the traveling path followed by MCs to charge the sensor nodes within the network. Empirical simulation results confirm the superiority of PCSMMC in terms of charging response time, survivability rate, energy utilization efficiency, and network lifetime by a significant margin when compared to alternative approaches.