Research And Application Of RFID Antenna Optimal Deployment Based On Improved MSSA

Radio frequency identification(RFID)technology is a developing auto ID technology,which has the advantages of low cost,low power consumption and small shape.In large-scale RFID systems,it is usually necessary to deploy multiple antenna readers to ensure the normal operation of the system.When deploying RFID antenna readers,the influence of tag coverage,economic benefits,load balancing,reader collision and other factors should be considered,which makes the optimization of RFID antenna a NP-hard problem.There are still some unsolved problems in the existing RFID antenna optimal deployment algorithms when dealing with large-scale RFID systems,such as ensuring optimal coverage,ensuring load balance,avoiding reader collision,and ensuring economic benefits.In recent years,with the further development of swarm intelligence(SI),the optimization objectives in RFID optimal deployment are defined by setting optimization functions,and the optimal solution is obtained through swarm intelligence algorithm,which achieves good results.In this paper,an optimized deployment method of RFID antenna based on the improved multi-objective salp swarm algorithm is proposed.The separation operator in the elephant herding algorithm is used to improve the multi-objective salp swarm algorithm.The Pareto solution set is obtained and the optimal deployment solution of RFID antenna reader is obtained.The corresponding unmanned supermarket system is developed,and a complete unmanned supermarket platform is built on this basis.This paper's main research and innovations are as follows:1.A separation operator is introduced to optimize the multi-objective salp swarm algorithm: in the traditional multi-objective salp swarm algorithm,due to the chain search principle,the individual of the salp is easy to fall into the local suboptimal in the process of optimization.The separation operator in the elephant herding algorithm is added to change the search strategy of the individual with the worst fitness,which can avoid the algorithm from falling into the local suboptimal solution and Too fast convergence to get a suboptimal result can also improve the search ability and performance of the algorithm.2.The improved multi-objective salp swarm algorithm is applied to the optimal deployment of RFID antenna Reader: multi-objective salp swarm algorithm is essentially an algorithm for finding Pareto solution set,which does not need prior knowledge in the optimal deployment,avoids the interference of human subjective factors,and can obtain multiple optimal solutions.Compared with bat algorithm(BA-OM),particle swarm optimization(PSO)and bacterial foraging optimization(MC-BFO)with observer mechanism,the coverage rate of the proposed algorithm is increased by 33%,28% and 20%,respectively.Compared with the same type of hybrid firefly(HMOFA)algorithm for Pareto solution set,the load balancing is increased by 7.14%,the economic benefit is increased by 59.74%,and the reader interference is reduced by 34.04%.Although it costs a certain amount of time,it can get multiple solutions,which is basically consistent with the results of weighted coefficient method.It is a practical and good performance algorithm.3.On the basis of the above algorithm,JT-928 reader and STM32 single chip microcomputer are used as hardware platform.The backstage server uses Spring Boot and My Batis to connect mysql background database,and u C /OS-III system is installed on STM32 to process RFID reader data.The user can view consumption records through Android mobile app,and the unmanned supermarket system is designed and implemented with Java,MATLAB and C language.The main functions of the system include warehouse inventory,purchase of goods,query of consumption records,etc.,and the practicability and effectiveness of the specific functions of the unmanned supermarket system are tested in the real environment.