Research On Group Scheduling In Concurrent Wireless Charging

With the development of wireless charging technology in recent years,wireless charging technology is considered as a promising solution to address the energy limitation problem for wireless sensor networks(WSNs).Through the movement of one and more mobile chargers in wireless sensor network,the sensor nodes through which the charger passes will be charged one by one.However,the deployment terrain of many actual wireless sensor networks is not suitable for the movement of the charger(such as structural health monitoring based on wireless sensor networks).In this case,we need to deploy a number of static chargers to work at the same time in order to charge the wireless sensor network in which the nodes are dispersed,which is called concurrently wireless charging.Since there are wireless interference between these chargers,this kind of interference will result in the enhancement of the signal in some regions,and the signal in some areas will cancel each other,we call the nonlinear superposition charging effect.Therefore,we need to schedule these chargers,so that the sensor nodes in different locations are able to obtain efficient charging energy to meet the communication needs of sensor networks.In this paper,we first establish the concurrent charging model,which reveals the inherent mechanism of nonlinear superimposed charging in concurrently wireless charging.Based on this model,we formulate the concurrent charging scheduling problem(CCSP).The purpose of this problem is to minimize the total length of all sensor nodes by scheduling the working state of the charger.After proving the NP-hardness of CCSP,we propose two efficient greedy algorithms,and give the approximation ratio of one of them.We also design the genetic algorithm for the CCSP.After simulation verification,both the two greedy algorithms' performances are very close to that of the designed genetic algorithm(GA)which performs almost as well as a brute force search algorithm at small network and charger scale.However,the running time of the two greedy algorithms is far lower than that of the GA.We conduct extensive experiments and specially implemented a testbed for wireless chargers.Compared to non-scheduling method and single-charging method in many placement,the results verified the good performance of the proposed algorithms.