The Research On Missing Tag Detection Algorithm In Large Scale RFID System

With the rapid development of technologies such as block chain,Internet of things,artificial intelligence,and sharing economy,Radio Frequency IDentification(RFID)tag as a kind of data acquisition terminal and storage-side has entered more and more people's attention.RFID has a very good application prospects in smart homes,smart logistics,unmanned supermarket,shared warehousing and other aspects.However,less people involved in these above scenarios,some integrity issues and machine failure problems cannot be discovered in a timely manner,resulting unnecessary economic losses.Therefore,it is important to effectively detect missing tag events.The existing missing tag detection algorithm cannot detect lost tags accurately,timely,and quickly,and there is also a problem of privacy leakage.In order to solve the above problems,this paper focuses on the research of unlicensed supermarkets and shared warehouse management in the Internet of Things environment and conduct on the detection of missing tags in large-scale RFID systems.Firstly,the requirement analysis of missing tag detection algorithm is taken as the entry point,at the same time the necessity of missing tag detection is clarified.Then two basic algorithms and the most commonly used hash function and Bloom filter in RFID system are introduced.These two basic algorithms make a theoretical foundation for the following algorithm.Through analyzing the Trusted Reader Protocol(TRP),Iterative ID-free Protocol(IIP),and RFID monitoring protocol with UNexpected tags(RUN),we make adequate preparation for the proposed algorithm.Secondly,the Grouped Sampling Bloom filter-based Missing tag Detection Protocol(GSBMDP)proposed in this paper takes into account the existence of undesired tags,it uses grouping to narrow the search range and reduce the time for query operations.Then,the “compressed” sampling Bloom filter is used to filter out unwanted tags,and the missing tags are accurately found through identity testing.Soon after,theoretical analysis is performed to determine the optimal parameters to minimize the detection time and satisfy the required reliability.Then we do some simulations respectively from the effects of the sampling Bloom filter,the impact of the number of missing tags,the influence of the unexpected tag number,the influence of the threshold deviation and so on.The simulation results show that the GSBMDP is significantly better than other advanced technologies.Finally,due to GSBMDP and other missing detection tag algorithms did not consider the privacy protection issues,a privacy protection-based missing tag detection algorithm is proposed.This algorithm can accurately and quickly detect missing tags without causing privacy leakage.The Enhanced Missing Tag Detection Based on Framed ALOHA(EMTDFA)algorithm has been improved in two aspects on the basis of Missing Tag Detection Based on Framed ALOHA(MTDFA)algorithm.MTDFA directly constructs a virtual occlusion tag for missing tag detection,while EMTDFA first uses packet technology to divide the entire tag set into three subsets,and then processes the tags in each group separately in different ways.In this way,the execution time of the protocol is greatly reduced and the detection accuracy is improved.A large number of simulation results in different situations show that the EMTDFA execution time is much lower than the MTDFA.