Research On Charging Scheduling Strategy Based On Maximizing Energy Utilization Of Wireless Charging Vehicles

In a wireless rechargeable sensor network,the energy carried by a sensor node is limited,and its power determines the life of the network.Naturally,how to charge a node becomes a key issue for the long-term operation of the sensor network.In early research,Hamiltonian loops were mainly used to plan charging paths,but such path planning ignored the urgency of node charging,resulting in the death of a large number of nodes in need of charging,and it was not suitable for large-scale networks.Different from the previous path planning,this paper comprehensively considers the node location and the urgency of node charging to study the wireless charging car scheduling problem in large-scale networks.Based on whether the number of charging nodes is known,the charging in offline and online scenarios is designed separately.Scheduling strategy.In the offline scenario,the number of charging nodes and the corresponding remaining power are known.This paper first uses a cluster analysis algorithm to solve the problem of the allocation between wireless charging cars and charging nodes,and then designs a wireless charging car based on maximizing energy utilization The comprehensive weight scheduling algorithm reduces the number of deaths of network nodes.The node design and the urgency of node charging are considered in the algorithm design,which provides a new idea for mobile charging.In this paper,a simulation experiment is used to compare the comprehensive weighted scheduling strategy that maximizes the energy utilization of wireless charging vehicles with the optimal moving vehicle number algorithm(NMV)based on the minimum spanning tree.Through comparison of experimental data,it is found that the comprehensive weight scheduling algorithm can more effectively use wireless charging cars than the NMV algorithm,and reduce the number of network node deaths.In the online scenario,this paper uses the transition probability of the working state of nodes and the energy consumption of the nodes in different working states to to establish an energy consumption model based on the Markov chain,and then predicts the number of charging nodes that are below the power threshold at any time.And the corresponding energy consumption,finally use the comprehensive weight scheduling algorithm to plan the path,solve the charging scheduling problem in theonline scenario,and carry out the feasibility analysis was carried out through simulation experiments.