Sampling bloom filter-based detection of unknown RFID tags

Liu, Xiulong, Qi, Heng, Li, Keqiu, Stojmenovic, Ivan, Liu, Alex X., Shen, Yanming, Qu, Wenyu and Xue, Weilian 2015, Sampling bloom filter-based detection of unknown RFID tags, IEEE transactions on communications, vol. 63, no. 4, pp. 1432-1442, doi: 10.1109/TCOMM.2015.2402660.

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Title Sampling bloom filter-based detection of unknown RFID tags
Author(s) Liu, Xiulong
Qi, Heng
Li, Keqiu
Stojmenovic, Ivan
Liu, Alex X.
Shen, Yanming
Qu, Wenyu
Xue, Weilian
Journal name IEEE transactions on communications
Volume number 63
Issue number 4
Start page 1432
End page 1442
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-04
ISSN 0090-6778
Keyword(s) Science & Technology
Engineering, Electrical & Electronic
Unknown tags detection
Summary Unknown RFID tags appear when the unread tagged objects are moved in or tagged objects are misplaced. This paper studies the practically important problem of unknown tag detection while taking both time-efficiency and energy-efficiency of battery-powered active tags into consideration. We first propose a Sampling Bloom Filter which generalizes the standard Bloom Filter. Using the new filtering technique, we propose the Sampling Bloom Filter-based Unknown tag Detection Protocol (SBF-UDP), whose detection accuracy is tunable by the end users. We present the theoretical analysis to minimize the time and energy costs. SBF-UDP can be tuned to either the time-saving mode or the energy-saving mode, according to the specific requirements. Extensive simulations are conducted to evaluate the performance of the proposed protocol. The experimental results show that SBF-UDP considerably outperforms the previous related protocols in terms of both time-efficiency and energy-efficiency. For example, when 3 or more unknown tags appear in the RFID system with 30 000 known tags, the proposed SBF-UDP is able to successfully report the existence of unknown tags with a confidence more than 99%. While our protocol runs 9 times faster than the fastest existing scheme and reducing the energy consumption by more than 80%.
Language eng
DOI 10.1109/TCOMM.2015.2402660
Field of Research 080109 Pattern Recognition and Data Mining
0906 Electrical And Electronic Engineering
1005 Communications Technologies
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
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