Research On Mobile Charging Scheduling Scheme Of Wireless Rechargeable Sensor Network

In recent years,the Internet of Things(Io T)has been developing like a fire.Wireless Sensor Network(WSN),as its core,has been widely used in environmental monitoring,target tracking,medicine,science and other exploration fields.Sensor nodes are usually powered by batteries that are not easily replaceable,leading to the limited life of WSN.With the emergence of Wireless Power Transfer(WPT)technology,new potential has been brought to extend the network life of WSN.Equipped with special antennas and rechargeable batteries,sensor nodes can receive wireless charging services over a long distance.In theory,WSN can be constantly replenished and work forever.However,due to some practical limitations,such as charger battery capacity,WSN scale,charging distance and other factors,mobile charging often needs to be carefully designed to arrange charging tasks.Aiming at maximizing the charging utility,this thesis dispatches Mobile charging devices(MC)to provide charging services for sensor nodes in the network.(1)A one-to-many directional charging scheduling scheme is proposed for two-dimensional plane environment.The proposed scheme firstly extracts the directed coverage set existing in WSN,and then selects the maximal directional coverage subset of charging gain in the network with greedy idea until the full coverage of nodes.Then the charging position of MC is determined according to the directed coverage subset,and the driving trajectory of MC is planned.Finally,the charging time of each sensor node is allocated under the condition that the constraint is satisfied.(2)In view of the three-dimensional space environment,a charging scheduling scheme for bridge monitoring WSN using Unmanned Aerial Vehicle(UAV)is studied.In this scheme,the energy utilization of UAV was maximized by synergistic optimization of UAV flight trajectory and sensor energy allocation.An improved ant colony algorithm was proposed for UAV trajectory planning,and the convergence of the algorithm was accelerated by dynamically adjusting the enhancement factor and pheromone intensity value.On this basis,the charging energy optimization problem is transformed into the problem of maximizing the balanced energy distribution of the sensor,and the optimal charging time of each sensor node is obtained by using the quasi-Newton method.Finally,this thesis carries out a simulation experiment on the above scheduling scheme,and the experimental results show that compared with other charging schemes,the proposed scheduling algorithm has a better performance in prolonging the network life.