Research On The Charging Efficiency Maximization Strategy Of UAV-enabled Wireless Rechargeable Sensor Network

In wireless rechargeable sensor networks,the network is often interrupted due to energy constraints and communication quality problems in complex environments.UAV has the characteristics of high mobility,and can carry wireless charging technology to charge other devices.Therefore,UAV can be used to assist wireless rechargeable sensor network to complete the work and maintain the stability of the network.At the same time,how to let UAV effectively charge sensors in the network is also a challenge.This paper proposes a scenario in which UAV is used to charge and transmit data for the Internet of Things devices(Io TDs).The UAV starts from the starting point,charges and transmits data for the nearby Io TDs through different hovering points,and flies back to the starting point after ensuring that all Io TDs are serviced.In this process,the UAV needs to select the appropriate hover points and the order of accessing the hover points to minimize the time spent by the UAV to complete all tasks,and also consider the energy consumption and service cost of the UAV,as follows:1.The single objective problem USTOP of UAV scheduling and trajectory optimization and the multi-objective problem SUSTOP of solar UAV scheduling and trajectory optimization are proposed.In the USTOP,UAV flies at a fixed altitude and charges and transmits data for all Io TDs.The goal of this problem is to minimize the total time spent by UAVs.In the SUSTOP problem,solar UAV is used as charging and data transmission equipment and can charge itself through solar energy.The UAV is set to be highly variable,allowing the UAV to balance the charging of Io TDs and its own charging.The goal of this problem is to minimize the total time spent by the UAV,the energy consumption of the UAV(the difference between the actual energy consumption and the energy obtained by solar energy)and the service cost(the flight distance of the UAV).Through analysis,it is proved that the two problems are NP-hard problems.2.In order to solve the USTOP,an improved simulated annealing algorithm(ISA)is proposed,which adds the variable size mechanism of independent variables,conflict resolution mechanism and hybrid evolutionary sequence method on the basis of the standard algorithm.In the USTOP,the number of UAV hovering points is variable,the solution space of 3D coordinates of hovering points is continuous,and the solution space of hovering point access sequence is discrete.The variable size mechanism of independent variables solves the problem that the solution dimension needs to change,and the hybrid evolutionary sequence method solves the problem that the solution space is a combination of continuous and discrete.In the face of the problem that it is difficult to find a feasible solution,the conflict resolution mechanism can quickly find the solution that meets the problem limit,and will also change the dimension of the solution.3.For the SUSTOP,an improved multi-objective grasshopper optimization algorithm(IMOGOA)is proposed.The conflict resolution mechanism is added to ensure the feasibility of the solution.The mutation cross selection mechanism and the adaptive hybrid evolutionary sequence method are added to make the population of the algorithm more diverse.It is easy to jump out of the local optimal solution by expanding the search range.The greedy method is added to initialize the access sequence to reduce the time for the algorithm to find the optimal solution.4.ISA and IMOGOA are verified by simulation experiments,and experimental data of different scales are used.The results show that the ISA and IMOGOA have better calculation results than other algorithms,and have better performance for the two problems proposed.