A Novel Data Collection and Partial Charging Scheme Using Multiple Mobile Vehicles in Wireless Rechargeable Sensor Networks

In wireless rechargeable sensor networks (WRSNs), due to data collection and charging, energy efficiency is a crucial factor because it affects the network’s reliability and performance. Wireless energy transfer (WET) and wireless data collection (WDC) using separate vehicles are two promising new methods for improving the energy efficiency of sensors. We combine WET and WDC on a single mobile vehicle (MV) to charge and collect data from the sensors by traversing specified sojourn locations within the WRSNs. The objective of this work is to maximize data collection per unit of energy and reduce the number of inactive sensors. In order to accomplish this, we propose a novel scheme, namely DCPCS, that includes two heuristics and two optimization algorithms. We utilized the circle-covering algorithm to determine sojourn locations based on energy consumption rates and the location of sensors. The ant lion optimization (ALO) algorithm partitions the network into the minimum number of regions, assigns a MV to each region, and ensures balanced total energy consumption among the MVs. The ALO algorithm utilizes the first heuristic algorithm to validate the regions for the MVs. Subsequently, the discrete grey wolf optimization algorithm determines the optimal order of the sojourn locations for data collection and charging by a MV within a region. To accomplish our objectives, we propose a heuristic data collection and partial charging strategy to determine the sojourn times of the MVs at the sojourn locations. We conduct simulations and compare the results of the proposed DCPCS scheme with three existing schemes. The simulation outcomes demonstrate that our proposed scheme outperforms the other schemes in terms of various performance metrics.