Research On Data Collection Algorithm Of Wireless Rechargeable Sensor Network Based On Multi-Objective Fireworks Optimization

Energy problem has always been a key issue in wireless sensor networks.In recent years,with the development of wireless energy transmission technology,the application of wireless charging technology in wireless sensor networks provides an effective research method to solve the problem of energy shortage,this kind of network is called wireless chargeable sensor network,At present,the research on wireless rechargeable sensor networks mainly focuses on the charging path planning problem of wireless mobile charger.There are not many researches on path planning for mobile charger with energy replenishment and data collection,and the researches on joint energy replenishment and data collection is to determine the charging path of mobile charger first,and then design the data collection strategy of the sensor node based on the charging path of mobile charger.In view of the shortcomings of the existing research,this thesis considers the impact of energy replenishment and data collection on the path planning of mobile charger,for two scenarios where the energy consumption rate and the data generation rate are fixed and changed,two multi-objective path planning models are established respectively,and two multi-objective optimization algorithm are proposed respectively.In the case that the energy consumption rate and data generation rate of the sensor node are fixed,the mobile charger carries enough energy to simultaneously perform energy replenishment and data collection in the network.In order to maximize the average lifetime of sensor nodes in the network and the amount of data collected by mobile charger,a multi-objective optimization model of joint energy replenishment and data collection is established.In order to solve the multi-objective optimization problem,this thesis proposes a grid-based Multi-Objective Discrete Fireworks Algorithm(MODFA)based on discrete fireworks and multi-objective continuous fireworks.The algorithm uses grid partition to filter Pareto solution sets,which is less computational than other algorithms.In the simulation experiment,multiple sets of network examples are set up.In the comparison of MODFA,NSGA-?,SPEA-? and MOEA/D algorithms,it is found that MODFA is superior to the other three algorithms in solving performance,and the Pareto solution set obtained has better distribution and convergence.In the case that the energy consumption rate and data generation rate of the sensor node are dynamic,and the data storage space of sensor nodes is limited,the least squares support vector machine method is used to perform regression prediction on the existing data,and the energy consumption rate and data generation rate of each sensor node are obtained.In this thesis,the multi-objective path model of joint energy replenishment and data collection is established by maximizing the average life of sensor nodes and the amount of data collected by the mobile charger and minimizing the data loss of sensor nodes in the network.Aiming at this multi-objective optimization problem,this thesis proposes a grid-based Multi-Objective Cooperative Fireworks Algorithm(MOCFA)based on MODFA and coevolutionary algorithm.In order to compare the performance of the algorithm,this thesis compares the MOCFA,MODFA,NSGA-? and SPEA-? algorithms with various performance indicators.The simulation results show that the MOCFA algorithm is superior to other algorithms in all aspects of solving performance.