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A rapid anti-collision algorithm with class parting and optimal frames length in RFID systems

Author

Listed:
  • Mohsen Chekin

    (Islamic Azad University)

  • Mehdi Hossienzadeh

    (Iran University of Medical Sciences
    University of Human Development)

  • Ahmad Khademzadeh

    (Iran Telecommunication Research Center (ITRC))

Abstract

Several dynamic frame-slotted ALOHA (DFSA) methods are suggested to resolve the collision problem in radio frequency identification systems. This paper proposes a rapid DFSA-based algorithm for tags identification. The above-stated algorithm is based on adaptive class parting technique to select the optimal frames length. The selection of the best frame length is a major research factor to be used for dynamic frame slotted ALOHA algorithm. To get the best frame length in DFSA protocol, we classified the tags into the some groups. Each group of tags is determined by same prefixes. The main objectives of the new algorithm are to improve the tags identification time and to increase the reader energy efficiency. The ideal frame size has to be fixed to 2 times of the total of tags bit length if the ratio among collision-slot and empty-slot is 5. Observing the results clarifies that the algorithm in this paper offers a reading rapidity of up to 400 tags/s and can achieve time-saving identification up to 15–20% in comparison to the traditional DFSA. The rapid DFSA anti-collision algorithm has a number of merits such as compatibility with ISO 18000-6, better system performance and ease of implementation.

Suggested Citation

  • Mohsen Chekin & Mehdi Hossienzadeh & Ahmad Khademzadeh, 2019. "A rapid anti-collision algorithm with class parting and optimal frames length in RFID systems," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 71(1), pages 141-154, May.
    • Handle: RePEc:spr:telsys:v:71:y:2019:i:1:d:10.1007_s11235-018-0492-7
    DOI: 10.1007/s11235-018-0492-7
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    References listed on IDEAS

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    1. Yuanyi Chen & Jingyu Zhou & Minyi Guo, 2016. "A context-aware search system for Internet of Things based on hierarchical context model," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 62(1), pages 77-91, May.
    2. Zeinab Shariat & Ali Movaghar & Mehdi Hoseinzadeh, 2017. "A learning automata and clustering-based routing protocol for named data networking," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 65(1), pages 9-29, May.
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