Full Coverage Charging And On-Demand Charging Schedule Of Multiple Wireless Mobile Chargers With Limited Energy In WRSN

Wireless Rechargeable Sensors Networks(WRSN)has drawn more and more attention from experts and scholars.And WRSN has become a hot spot in current research.WRSN breaks the performance bottleneck that restrict the widespread application and development of Wireless Sensor Networks(WSN).Some scholars have found that we can use multiple Wireless Mobile Chargers(WMCs)to replenish energy for sensor nodes through wireless energy transmission technology,that can effectively extend the life of WRSN.However,the current charging schedule algorithms less consider using multiple energy-limited WMCs in WRSN,in which sensor nodes are with charging time windows and charged on demand.We study two-dimensional WRSN in this thesis.Firstly,sensor nodes are with charging time windows.However,using single energy-limited WMC can not guarantee that all the sensor nodes work all the time.Thus we study how to use multiple WMCs to replenish energy for all the sensor nodes.During the charging process,the energy constraint of WMC and the time window of sensor nodes must be satisfied,and then we set energy supplement model and construct charging strategy.In this way,we get the charging schedule in a WRSN where sensor nodes are with time windows.Secondly,we set the OPT-1 to minimize the total cost of traveling,the number of WMCs and the deviation of time window during the charging process.This thesis designs a Genetic Algorithm Based on 2-Optimization(GAB20)to get the charging schedule.The simulation results show that the number of WMCs used in the charging process and the total cost are respectively about 13.2%and 22.7%lower than the greedy algorithm.We further study that the traveling energy and charging energy of WMC are separated and limited,and using multiple WMCs to replenish energy for certain critical sensor nodes that need to be charged in WRSN.Firstly,we select some critical sensor nodes according to their residual working time.These critical sensor nodes are correspond to different charging priorities.Each charging schedule round needs to meet the priority constraints of nodes and the energy constraints of the WMCs.Then we set the charging model to make single WMC charge as many critical sensor nodes as possible,so that charging schedule can make full use of each WMC.We set OPT-2 problem to minimize the total cost of charging energy and traveling energy.This thesis proposes Hybrid Genetic Simulated Annealing Algorithm(HGSA)to solve the corresponding charging schedule.The simulation results show that the total cost of HGSA is about 23.7%lower than that of GAB20.