Research On RFID Collision Optimization Algorithms Based On ALOHA

So many great advantages that drive the growth of Radio Frequency Identification(RFID).Making RFID particularly attractive for applications in retail,inventory management,and supply-chain management.RFID systems are becoming Ubiquitous.one of the problems that we must solve is the collision between tags which lowers the efficiency of the RFID system.One of the popular anti-collision algorithms is ALOHA-type algorithms,which are simple and shows good performance when the number of tags to read.How to estimate the number of RFID tags and the occurrence probability of capture effect is very important for a dynamic frame length ALOHA RFID system with capture effect.The estimation would relate to setting an optimal frame length,which makes tag identification achieve higher efficiency.Under large-scale tags identification environment,the number of tags may be much greater than an initial frame length.In this scenario,existing estimates do not work well.In this article,we propose two novel estimation methods for the normal-scale and large-scale tags identification.New-Vogt and EC-EDFSA,respectively.The proposed methods could adjust the initial frame length matched to the number of tags from the idle slots in the frame.The advantage of the ED-EDFSA is to work better even when the number of tags is much greater.Numerical results show that,the proposed methods have lower estimation errors under the large-scale tag identification.After setting an optimal frame length from the estimated results of the proposed methods,furthermore,we could obtain higher identification efficiency.