Collaborative Charging Scheduling of Hybrid Vehicles in Wireless Rechargeable Sensor Networks

Abstract

Wireless rechargeable sensor networks (WRSN) are utilized in environmental monitoring, traffic video surveillance, medical services, etc. In most existing schemes, WRSNs provide sustainable energy for sensor nodes by employing one or more wireless charging vehicles (WCVs). However, two essential drawbacks, regional limitations and traveling speed limitations, constrain these schemes when applied in hostile and large-scale environments. On the other hand, benefiting from the intrinsic flexibility, high flight speed, low cost, and small size of drones, some works have used drones to charge sensor nodes. However, suffering from limited battery capacities, it is also hard to only use drones in large-scale WRSNs. To overcome the drawbacks of WCVs and drones, we proposed a novelty wireless charging system that deploys WCV, WCV-carried drones, and wireless charging pads (pads) in a large-scale wireless sensor network. Based on this new wireless charging system, we first formulated a pad deployment problem for minimizing the total number of pads subject to each sensor in the pad region that only can be charged by drones. In this work, three near-optimal algorithms, i.e., greedy, K-mean, and static, for the pad deployment problem are proposed. Then, to form a sustainable WRSN, we elucidated the collaborative charging scheduling problem with the deadlines of sensors. To guarantee the maximum number of sensors to be charged before the deadlines, we also presented an approximation algorithm to find the collaborative charging scheduling of WCV and WCV-carried drones with the help of pads based on the three deployment pad schemes. Through extensive simulations, we demonstrate the effectiveness of the proposed deployment pad schemes. and that the number of pads obtained by the greedy and K-mean scheme was generally lower than that of the static scheme with respect to network density, WCV region, and flight range. Then, we also examined the proposed collaborative charging scheduling scheme by extensive simulations. The results were compared and showed the effectiveness of the proposed schemes in terms of lifetime, the percentage of nodes being charged in time, the average move time of drones, the percentage of nodes being charged late by the drones, and the charge efficiency of all vehicles under different traffic loads. Related statistical analyses showed that the percentage of nodes being charged in time and the percentage of nodes being charged late based on the greedy and K-mean schemes were slightly better than those of the static scheme, but the charge efficiency of drones of the static scheme was significantly superior to that of the K-mean scheme under a busy network.