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Efficient Unknown Tag Detection in Large-Scale RFID Systems With Unreliable Channels | IEEE Journals & Magazine | IEEE Xplore

Efficient Unknown Tag Detection in Large-Scale RFID Systems With Unreliable Channels


Abstract:

One of the most important applications of radio frequency identification (RFID) technology is to detect unknown tags brought by new tagged items, misplacement, or counter...Show More

Abstract:

One of the most important applications of radio frequency identification (RFID) technology is to detect unknown tags brought by new tagged items, misplacement, or counterfeit tags. While unknown tag identification is able to pinpoint all the unknown tags, probabilistic unknown tag detection is preferred in large-scale RFID systems that need to be frequently checked up, e.g., real-time inventory monitoring. Nevertheless, most of the previous solutions are neither efficient nor reliable. The communication efficiency of former schemes is not well optimized due to the transmission of unhelpful data. Furthermore, they do not consider characteristics of unreliable wireless channels in RFID systems. In this paper, we propose a fast and reliable method for probabilistic unknown tag detection, white paper (WP) protocol. The key novelty of WP is to build a new data structure of composite message that consists of all the informative data from several independent detection synopses; thus it excludes useless data from communication. Furthermore, we employ packet loss differentiation and adaptive channel hopping techniques to combat unreliable backscatter channels. We implement a prototype system using USRP software-defined radio and WISP tags to show the feasibility of this design. We also conduct extensive simulations and comparisons to show that WP outperforms previous methods. Compared with the state-of-the-art protocols, WP achieves more than 2× performance gain in terms of time-efficiency when all the channels are assumed free of errors and the number of tags is 10000, and achieves up to 12× success probability gain when the burstiness is more than 80%.
Published in: IEEE/ACM Transactions on Networking ( Volume: 25, Issue: 4, August 2017)
Page(s): 2528 - 2539
Date of Publication: 15 May 2017

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