Research On Energy-efficient Charging Vehicles Scheduling Optimization In Wireless Rechargeable Sensor Networks

Wireless Rechargeable Sensor Network(WRSN)uses wireless energy transmission technology to charge sensor nodes through mobile charging nodes(MCV),in order to extend the network lifetime of WRSN.Currently,more research work is focused on MCV deployment and path planning.Although good progress has been made,there are still problems such as low charging efficiency,high MCV energy consumption,and few joint optimization goals.How to design a multi-objective(such as MCV energy consumption,node coverage,and charging efficiency)joint optimization problem is the key to extending the network lifetime of WRSN.This article introduces the LEACH protocol in the conventional WSN into WRSN.The purpose is to increase the charging phase in the traditional LEACH protocol,and to provide a framework basis for studying MCV dynamic deployment optimization issues.Based on the wireless charging model,communication consumption model and mobile consumption model,a multi-objective optimization model(MCVDOS MaOP)for MCV dynamic deployment in WRSN network is evolved.The model includes four optimization goals:(1)increase the number of sensor nodes in the MCV charging range at the same time;(2)reduce the mobile energy consumption of MCV;(3)the sensor node with the smallest remaining energy is preferentially charged;(4)make MCV more Close to all sensor nodes within its coverage.It was further proved that MCVDOS MaOP is an NP-hard problem.According to the MCVDOS model,a single-objective linear weighted optimization problem and a multi-objective optimization problem are constructed.Corresponding improved algorithms are proposed to solve these two problems:The single-objective optimization solution in the MCVDOS MaOP scenario introduces a linear weighting method to transform a multi-objective optimization problem into a single-objective optimization problem.Because the swarm intelligence optimization algorithm has a better advantage in solving the single-objective hybrid optimization problem.FA is used to solve the single-objective optimization problem of MCVDOS.In order to further improve the efficiency of FA,a dynamic attractive factor and Step factor adjustment mechanism are introduced in IFA.On the one hand,in the early iteration,the convergence speed is improved by increasing the attraction between fireflies,on the other hand,in the later iterations,by reducing the moving step of fireflies,the optimal solution is avoided and the optimization oscillation is not caused,which improves the solution The accuracy.Experiments show that compared with other single-objective swarm intelligence optimization algorithms,IFA has a better convergence speed and better prolongs the network lifetime of WRSN in solving the single-objective optimization problem of MCVDOS.The multi-objective optimization solution in the MCVDOS MaOP scenario can effectively solve the problem of distortion of the evaluation results due to the strong subjectivity in the single-objective optimization solution.The multi-objective optimization solution is to introduce MOFA's firefly attractiveness operator and dynamic position update mechanism in INSGA-II.On the one hand,in the early stage of the iteration,the firefly attractiveness operator is used to bring individuals closer to the optimal solution,which improves the convergence speed of INSGA-II.On the other hand,at the later stage of the iteration,through the dynamic location update mechanism of Firefly,different solution angles are provided to avoid falling into a local optimal solution and improve the accuracy of the solution of the understanding set.Experiments show that the MCVDOS multi-objective optimization model can greatly extend the lifetime of the WRSN network;At the same time,INSGA-II improves the solution accuracy compared with other multi-objective optimization algorithms and singleobjective group optimization algorithms including IFA in solving the proposed MCVDOS MaOP problem,and has advantages in extending the network lifetime of WRSN.