Efficiently Identifying Unknown COTS RFID Tags for Intelligent Transportation Systems

Over the last decade, the Internet of Things (IoT) technology has advanced significantly in a variety of fields. As a pivotal application of IoT, intelligent transportation systems (ITS) have harvested great attention from the research community. Radio frequency identification (RFID) which is an essential technology in IoT plays a key role in ITS to identify tagged vehicles. Unknown tag identification which aims at identifying the existing unknown tags is crucial to monitor the newly entering vehicles in the RFID-assisted intelligent transportation systems. However, the COTS (commercial-off-the-shelf) RFID tags that harvest energy from the reader can not support the hash function in reality, which hinders the widespread deployment of hash-enabled unknown tag identification protocols. To conquer this tough issue, the authors propose two approaches to efficiently identify unknown COTS RFID tags. They first propose a Single-Point Selective unknown tag identification approach called SPS, where an analog hash pattern using the EPC (Electronic Product Code) segments is deployed to exclusively identify unknown tags. An unknown tag will be identified when it selects a singleton slot to reply. To improve the time efficiency of SPS, they further propose a Multi-Point Selective unknown tag identification approach called MPS. In MPS, two techniques of batch identification and batch division are developed to reduce the number of empty slots and avoid tag collisions, respectively. Then the parameters are theoretically analyzed to maximize the identification efficiency. The effectiveness of the proposed approaches is validated via both the simulations and COTS RFID device based experiments.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2023.3289072
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Lin, Kai
  • Chen, Honglong
  • Li, Zhe
  • Yan, Na
  • Xue, Huansheng
  • Xia, Feng
• Publication Date: 2024-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 987-997
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 1
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Automatic vehicle identification; Intelligent transportation systems; Internet of things; Radio frequency identification
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01906286
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 29 2024 9:19AM