Research On Information Collection Algorithm For Massive RFID Tags

RFID technology,as one of the key technologies of the Internet of Things,has been widely studied and applied,with the national attention to the development of the Internet of Things.Through the communication between reader and multi-tags,RFID can obtain product' information and monitor their number in real time,which brings great convenience to the supervision of goods in large-scale warehouse,supermarkets and other environments.However,in the process of goods supervision,not only need to obtain the information of goods entered in advance,but also need to monitor the status of goods in real time.By integrating micro-sensors,RFID can simultaneously monitor the status of goods and the state of surrounding environment.In large-scale warehouse and other large-scale RFID tag scenarios,the conflict between multiple tags will seriously affect the efficiency of RFID information collection.Therefore,this paper studies the information collection of massive RFID tags.The basic idea is to identify tags and collect their information through polling,hashing and Bloom.It takes too long to implement traditional polling,and the communication between reader and tags will produce serious conflicts.The Bloom and hash mapping for direct processing will lead to excessive transmission of redundant information and which wastes of time,and reduces the efficiency of execution.In view of the above situation,this paper systematically studies the large-scale RFID technology,compares and summarizes the existing international standards about RFID,then analyses and studies the basic working principle and parameter settings of massive tags information collection for readers.Finally,two kinds of information collection algorithms are proposed:(1)Massive RFID tags information classification collection algorithm based on partitions(ICC).ICC collects monitoring information of many kinds of tags at the same time.First,reader categorizes all tags.Then,it arranges fixed area matching single slot for each category tags at the same time,so as to achieve the purpose of collecting multi-type tags information at the same time.Because different kinds of tags do not interfere with each other,and different kinds of information can be transmitted in one frame at the same time,the efficiency of information collection is effectively improved.(2)Abnormal information collection algorithms based on stratified sampling(AIC).AIC is used to detect whether an product is in an abnormal state in real time,such as a large-scale storage environment.The number of abnormal tags is estimated by the joint estimation method of stratified sampling and tags slots response,so as to quickly identify abnormal tags andcollect their data.Because it avoids the communication between the reader and the normal tags,and provides the initial frame length accurately through joint estimation.AIC improves the efficiency and accuracy of information collection,so,the management efficiency of warehousing and other applications are effectively improving.Finally,the paper makes a rigorous theoretical analysis and a large number of experimental simulations for the proposed two algorithms.The result shows that the two algorithms can not only efficiently complete the task of information collection.At the same time,compared with other information collection algorithms that can achieve the same purpose,they have the advantages of low execution time and high accuracy.