University of Sussex
Browse
IEEE_ToN_RFID_2022.pdf (3.46 MB)

Identifying RFID Tags in Collisions

Download (3.46 MB)
journal contribution
posted on 2023-06-10, 05:17 authored by Jian Su, Zhengguo ShengZhengguo Sheng, Chenxi Huang, Gang Li, Alex X Liu, Zhangjie Fu
How to obtain the information from massive tags is a key focus of RFID applications. The occurrence of collisions leads to problems such as reduced identification efficiency in RFID networks. To tackle such challenges, most tag collision arbitration protocols focus on scheduling tag identification with collision avoidance. However, how to effectively identify tags in collisions to improve identification efficiency has not been well explored. In this paper, we propose a group query allocation method to divide the string space into mutually disjoint subsets which contains several strings. Each string can be viewed as a full ID or partial ID of a tag. When multiple string from a subset are sent simultaneously, the reader can identify all of them in a time slot. Based on the group query allocation method, a segment detection based characteristic group query tree (SD-CGQT) protocol is presented for fast tag identification by significantly reducing the collision slots and transmitted bits. Numerous experimental results verify the superiority of the proposed SD-CGQT, compared to prior arts in system efficiency, total identification time, communication complexity and energy consumption.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

IEEE ACM Transactions on Networking

ISSN

1063-6692

Publisher

IEEE

Page range

1-14

Department affiliated with

  • Engineering and Design Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2022-11-01

First Open Access (FOA) Date

2022-11-01

First Compliant Deposit (FCD) Date

2022-10-31

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC