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    申彦明

    • 教授     博士生导师   硕士生导师
    • 性别:男
    • 毕业院校:纽约理工大学
    • 学位:博士
    • 所在单位:计算机科学与技术学院
    • 办公地点:海山楼B0813
    • 联系方式:shen@dlut.edu.cn
    • 电子邮箱:shen@dlut.edu.cn

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    Sampling Bloom Filter-Based Detection of Unknown RFID Tags

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    论文类型:期刊论文

    发表时间:2015-04-01

    发表刊物:IEEE TRANSACTIONS ON COMMUNICATIONS

    收录刊物:SCIE、EI、Scopus

    卷号:63

    期号:4

    页面范围:1432-1442

    ISSN号:0090-6778

    关键字:RFID; unknown tags detection; energy-efficiency; time-efficiency

    摘要: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%.