Energy-efficient active tag searching in large scale RFID systems

Shigeng Zhang, Xuan Liu, Jianxin Wang, Jiannong Cao, Geyong Min

Research output: Journal article publicationJournal articleAcademic researchpeer-review

26 Citations (Scopus)


Radio Frequency Identification (RFID) has attracted much research attention in recent years. RFID can support automatic information tracing and management during the management process in many fields. A typical field that uses RFID is modern warehouse management, where products are attached with tags and the inventory of products is managed by retrieving tag IDs. Many practical applications require searching a group of tags to determine whether they are in the system or not. The existing studies on tag searching mainly focused on improving the time efficiency but paid little attention to energy efficiency which is extremely important for active tags powered by built-in batteries. To fill in this gap, this paper investigates the tag searching problem from the energy efficiency perspective. We first propose an Energy-efficient tag Searching protocol in Multiple reader RFID systems, namely ESiM, which pushes per tag energy consumption to the limit as each tag needs to exchange only one bit data with the reader. We then develop a time efficiency enhanced version of ESiM, namely TESiM, which can dramatically reduce the execution time while only slightly increasing the transmission overhead. Extensive simulation experiments reveal that, compared to state-of-the-art solution in the current literature, TESiM reduces per tag energy consumption by more than one order of magnitude subject to comparable execution time. In most considered scenarios, TESiM even reduces the execution time by more than 50%.
Original languageEnglish
Pages (from-to)143-156
Number of pages14
JournalInformation Sciences
Publication statusPublished - 1 Jan 2015


  • Active tags
  • Energy efficiency
  • IoT
  • RFID tag searching

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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