Efficient and Reliable Missing Tag Identification for Large-Scale RFID Systems with Unknown Tags

Honglong Chen, Guoliang Xue, Zhibo Wang

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Radio frequency identification (RFID), which promotes the rapid development of Internet of Things (IoT), has been an emerging technology and widely deployed in various applications such as warehouse management, supply chain management, and social networks. In such applications, objects can be efficiently managed by attaching them with low-cost RFID tags and carefully monitoring them. The missing objects, therefore, can be identified by the readers in the RFID system. Most of prior missing tag identification protocols consider the ideal scenario that all the tags' IDs are known to the reader, which ignore that some tags with unknown IDs, called unknown tags, may be present in the system. In this paper, we investigate the problem of efficiently identifying the missing tags with a predefined reliability for large-scale RFID systems with unknown tags. We first propose a basic efficient and reliable missing tag identification protocol called B-ERMI. Then we propose an enhanced protocol called E-ERMI to further improve the efficiency. The parameters of our proposed ERMI protocols are optimized to minimize the execution time. We also conduct extensive simulations to evaluate the proposed ERMI protocols and the simulation results illustrate that the ERMI protocols outperform other existing ones.

Original languageEnglish (US)
Article number7843647
Pages (from-to)736-748
Number of pages13
JournalIEEE Internet of Things Journal
Volume4
Issue number3
DOIs
StatePublished - Jun 1 2017

Keywords

  • Efficient and reliable protocols
  • missing tag identification
  • radio frequency identification (RFID) systems
  • unknown tags

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management

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