| RFID (Radio Frequency Identification) technology is an importantlytechnological development rising in the1990’s and changes human consumptionand habits. As RFID technology has gradually mature, it is receiving more andmore attentions both in logistics, intelligent entrance guard system, localization,identification and tracing of external target and so on. Its advantages have beenincreasingly valued by all the pepole around the country. And the developmentof RFID technology has received enormous attention from various areas.The popular products that the numbers of the products exceeded a certainthreshold have the obvious seasonal characteristic. During the process of sellingproducts, marketers often want to know the preference of customers and find thecomparatively popular products to determine the marketing strategy in the nearfuture. Because of different peoples’ different needs and personal preferencesand other factors, the popular products can change in real time, but notinvariable. Therefore, study how to continuously adjust product and track salespicture in real time has become increasingly important. The emergency of RFIDtechnology, however, perfectly meets the demand of this real-time. In order tobetter scale in future, it simply require attaching the RFID tags on the things thatneed to be identified, and timely find out the best-selling products via thecorresponding algorithms.The current algorithms which identify the popular categories must scan all tags within readers ’ scope to identify popular categories. And these algorithmsare most interested in time efficiency with little regard for energy efficiency. Inorder to solve the above questions, Reserve Threshold-Based Scheme (RTBS)and Positive Threshold-Based Scheme (PTBS) are proposed to identify popularcategories in this paper. These two algorithms aim at time-efficiently andenergy-efficiently identifying popular categories in a lot of tags. Thesealgorithms are unlikely to apply this case which finds the popular categories byscanning a subset of all tags and firstly consider the energy problem in theprocess of identification as one of the major contributions.First, RTBS and PTBS algorithms find the specified set of tags in the alltags. Secondly, these algorithms find the sampled tags by Hash functions.Thirdly, for the identification of popular categories, this paper can acquirerelevant information and dispose them through the communication betweenreader and tags. The difference is RTBS uses the reversal way of thinking andPTBS uses the positive way of thinking. And in the search phase, RTBS uses thebaseline (Naming Mechanism) that broadcasts tags’category IDs; PTBS takesthe intersection of two Bloom filter. Both theoretical analysis and simulationresults demonstrate that RTBS and PTBS can identify popular categories andhave the advantages of time efficiency and energy efficiency. |
