Research On Joint Optimization Method For Charging Performance Of Wireless Rechargeable Sensor Network Based On CUAV

At present,wireless rechargeable sensor networks have become a research hotspot at home and abroad.Due to the limited power that sensors can carry,and the mobile charging nodes also have problems such as high energy consumption and low joint optimization flexibility,how to design wireless rechargeable reasonably the node’s charging deployment optimization scheme is the essential to today’s research.This paper proposes a joint optimization method for the charging performance of wireless rechargeable sensor networks based on drones to improve the lifespan of wireless rechargeable sensor networks(WRSN).In this regard,this article first builds a CUAV deployment problem optimization model(CUAVDOP)in the wireless charging sensor network based on the network model,the wireless charging model,and UAV energy consumption model,and analyzes the NP-hard problem of the formulated CUAVDOP And proof.In addition,the LEACH protocol is introduced into the wireless rechargeable sensor network,and an improved firefly algorithm(IFA)is raised to solve the CUAVDOP for the CUAVDOP optimization problem.The specific operations are as follows:(1)Aiming at the deployment problem of unmanned aerial vehicle(CUAV)in wireless rechargeable sensor network,introduced and analyzed related system models,including WRSN network model,wireless charging model,UAV energy consumption model and LEACH protocol model A CUAV-based joint target optimization deployment solution(CUAVDOP)is proposed.The solution includes the selection of three optimization targets,the number of sensor nodes covered in the charging range of CUAVs,the charging efficiency,and the sports energy consumption of CUAVs.The three optimization goals are combined to maximize the charging efficiency while maximizing the number of sensor nodes that can be charged by CUAVs and minimize the energy consumption of CUAVs to obtain the best solution optimization effect of the model,And further analysis proved that the scheme is an NP-hard problem.In addition,this paper also further mathematically model the optimization problem based on LEACH protocol,mainly adding charging operation to LEACH protocol,which provides basic framework support for studying the deployment and optimization problems of CUAVs in WRSN.(2)Because the swarm intelligence optimization algorithm is more suitable for solving joint optimization problems,it is also a useful method to solve the NP-hard problem,through the analysis of various swarm intelligence optimization calculations,the firefly algorithm(FA)is chosen to solve the joint target optimization problem of CUAVDOP in this paper.In view of the disadvantages of the traditional firefly algorithm that is less attractive in large-scale scenarios,and in order to further improve the performance of the traditional firefly algorithm,this article proposes an improved firefly algorithm(IFA),in the IFA algorithm,not only for large-scale scenarios under the disadvantage of the weak attractiveness of the firefly,an improved dynamic attractiveness model is adopted to achieve the effect of adaptive adjustment of the attractiveness of the firefly according to the size of its search range,and based on the improved attractiveness model,by introducing a global optimal value the adaptive step size factor is adjusted to jointly affect the movement of the firefly,which solves the original algorithm’s slow convergence speed and weak global search ability,making it more suitable for the model CUAVDOP formulated in this article in terms of application scenarios and solution accuracy.The IFA algorithm not only improves the mutual attraction of fireflies in the early iterations,thereby improving the convergence speed of the algorithm,but also reduces the moving step length of the fireflies by improving the adaptive step size factor to avoid the algorithm from reaching the local optimum.Improve the global search capability and the accuracy of algorithm solving.(3)In both small-scale and large-scale application scenarios,the simulation experiment verifies the performance of the IFA algorithm.The results show that contrasted with other multi-objective optimization algorithms and single-objective group intelligent optimization algorithms,the IFA algorithm can effectively achieve the goal joint optimization.In addition,IFA’s algorithm performance is better than other algorithms,which can more effectively extend the network life cycle of WRSN.