• 更多栏目

    鲁大伟

    • 教授     博士生导师   硕士生导师
    • 任职 : 统计与金融研究所所长
    • 性别:男
    • 毕业院校:大连理工大学
    • 学位:博士
    • 所在单位:数学科学学院
    • 学科:概率论与数理统计. 金融数学与保险精算
    • 办公地点:数学科学学院,π空间,512室

    访问量:

    开通时间:..

    最后更新时间:..

    Fast Tracking the Population of Key Tags in Large-Scale Anonymous RFID Systems

    点击次数:

    论文类型:期刊论文

    发表时间:2017-02-01

    发表刊物:IEEE-ACM TRANSACTIONS ON NETWORKING

    收录刊物:SCIE、EI

    卷号:25

    期号:1

    页面范围:278-291

    ISSN号:1063-6692

    关键字:Key RFID tags; cardinality estimation; population tracking; time-efficiency

    摘要:In large-scale radio frequency identification (RFID)enabled applications, we sometimes only pay attention to a small set of key tags, instead of all. This paper studies the problem of key tag population tracking, which aims at estimating how many key tags in a given set exist in the current RFID system and how many of them are absent. Previous work is slow to solve this problem due to the serious interference replies from a large number of ordinary (i.e., non-key) tags. However, time-efficiency is a crucial metric to the studied key tag tracking problem. In this paper, we propose a singleton slot-based estimator, which is time-efficient, because the RFID reader only needs to observe the status change of expected singleton slots corresponding to key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small, because key members are usually rare. As a result, even when the whole time frame is long, the number of expected singleton slots is limited and the running of our protocol is very fast. To obtain good scalability in large-scale RFID systems, we exploit the sampling idea in the estimation process. A rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. Extensive simulation results demonstrate that our scheme outperforms the prior protocols by significantly reducing the time cost.