ProTaR: Probabilistic Tag Retardation for Missing Tag Identification in Large-Scale RFID Systems

Abstract

Radio frequency identification (RFID) technology provides a promising solution to the problem of missing object identification in large-scale systems, such as warehouses and bookstores by employing RFID readers to communicate with numerous RFID tags, each of which is attached to a monitored object. To achieve prompt identification of missing tags/objects, extensive research is carried out while the transmission of expatiatory bits from each tag and the occurrence of substantial tag collisions critically degrade the time efficiency. Therefore, this motivates us to propose ProTaR, a probabilistic tag retardation-based protocol, which addresses the missing tag identification problem in a more time-efficient way than the prior work. Based on an improvement of conventional frame-slotted ALOHA algorithm, ProTaR aims at alleviating the tag collision problem and achieving compact tag transmissions. The novelty of ProTaR is manifested mainly in two aspects. ProTaR leverages a mask at a reader to distill partial bits from 96-bit identifier (ID) of each tag for the characterization of tag uniqueness. In this context, ProTaR averts the transmission of redundant bits from both the tags and reader. A bit vector is constructed by the reader to inform each tag of the transmissions of others. This idea successfully eliminates the tag collisions, and hence makes full utilization of tag responses. Experimental results validate that ProTaR achieves 100% identification accuracy regardless of missing tag ratio. Furthermore, extensive simulations present that ProTaR enables the time efficiency improvement of up to 88% compared with benchmarks while merely degrading the optimum by 15%.