Research On On-demand Charging Optimization Method Based On Multi-node Charging Model

Recently,with the help of promising wireless power transfer technology,charging sensor nodes with limited battery energy in wireless rechargeable sensor networks(WRSNs)via mobile chargers has gained increasing attention from the research community.However,existing studies mainly focused on periodic charging schemes and single-node charging.Since the periodic charging scheme ignores the dynamic nature of the network,it usually results in the non-functionality of a large number of nodes and low charging performance.Besides,the charging efficiency for single-node is low and the network has poor scalability.To tackle these problems,the on-demand multi-node charging technique is proposed.However,the performance of existing ondemand multi-node charging is limited due to lack of consideration for charging fairness and the underutilization of the function of multi-node charging.Based on the multi-node charging model,this dissertation proposes two mobile charging scheduling optimization schemes for on-demand charging architecture.The main work of this dissertation is as follows:(1)The on-demand priority charging scheduling problem which is based on multinode charging model is studied,the optimization goal is to minimize the number of dead nodes and the energy consumption of the mobile charger.In this dissertation,a linear model is proposed for the energy consumption rate of sensor nodes based on the distance relationship between the sensor nodes and the base station.To reduce the selected number of stopping points,this dissertation proposes a centroid-based clustering heuristic algorithm due to the NP-hard nature of the problem.Under the constraints of the charging radius,the algorithm iteratively divides the request nodes into different clusters and place in the stopping points at the average value of the coordinates of all nodes in the cluster,to balance the charging time of each node in the cluster.During the path planning stage,in order to charge the node fairly,the priority of the cluster is calculated considering both the remaining life of the node and the distance between the stopping points and the mobile charger which helps to direct the mobile charger to select the next stopping points.Finally,the effectiveness of the algorithm is verified by a series of simulations.(2)Aiming at maximizing the charging energy efficiency,the on-demand passer-by charging scheduling problem is studied based on multi-node charging model.This dissertation is the first to consider allowing mobile chargers to simultaneously charge unrequested nodes within the range of coverage while charging the request nodes.In each charging round,the request nodes are only once and fully charged,and the unrequested nodes may partially charge multiple times until fully charged.To further reduce the number of groups,a more effective heuristic algorithm is proposed compared with the existing algorithms,and the correctness of the selection of the stopping points is proved.In this dissertation,the effect of low-efficiency grouping on charging performance is analyzed for the first time,and an effective strategy for splitting lowefficiency grouping is proposed to optimize the travel path locally.Charging performance is promoted by reducing energy consumption and charging more unrequested nodes.Finally,simulations evaluate the performance of the proposed algorithm and scheme in offline and on-demand scenarios,respectively.