• DocumentCode
    1885186
  • Title

    Anti-collision algorithm based on counting mechanism and multi-state binary

  • Author

    Xue Jianbin ; Wang Wenhua ; Li Songbai ; Zhang Ting

  • Author_Institution
    Sch. of Comput. & Commun., Lanzhou Univ. of Technol., Lanzhou, China
  • fYear
    2013
  • fDate
    16-17 Jan. 2013
  • Firstpage
    276
  • Lastpage
    282
  • Abstract
    RFID has become a key technology with the development of the Internet of Thing, its development and application is limited by the tag anti-collision technology which has become an urgent problem needs to be solved. In this paper, the binary search algorithm and multi-state binary search algorithm are compared and analyzed in detail, and an anti-collision algorithm based on counting mechanism and multi-state binary(CMS) is proposed. By utilizing the obtained collision information collected from preprocessing, the supreme conflict counting mechanism is introduced into the multi-state binary search algorithm. Theoretical analysis and simulation results show that the proposed algorithm can greatly reduce the query search times, the length of reader´s send command and tag´s response command as well as the identification time.
  • Keywords
    Internet of Things; radiofrequency identification; search problems; CMS; Internet of Thing; RFID; anticollision algorithm; counting mechanism and multistate binary search algorithm; supreme conflict counting mechanism; tag response command; Algorithm design and analysis; Educational institutions; Encoding; Heuristic algorithms; Radiation detectors; Radiofrequency identification; Satellite broadcasting; Anti-collision algorithm; Binary search algorithm; Manchester; Radio Frequency Identification (RFID);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation (ICMTMA), 2013 Fifth International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4673-5652-7
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2013.72
  • Filename
    6493721