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A Novel Time-aware Frame Adjustment Strategy for RFID Anti-collision

Haipeng Chen1, Kexiong Liu2, Chunyang Ma3, Yu Han4, Jian Su5,*

Department of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China.
School of Technology, Beijing Forestry University, Beijing, 100083, China.
Department of Electro-Optics and Photonics, University of Dayton, Dayton, OH 45469, USA.
School of Information and Communication Engineering, University of Electronic Science and Technology, Chengdu, 611731, China.
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.

* Corresponding Author: Jian Su. Email: email.

Computers, Materials & Continua 2018, 57(2), 195-204. https://doi.org/10.32604/cmc.2018.03592

Abstract

Recently, object identification with radio frequency identification (RFID) technology is becoming increasingly popular. Identification time is a key performance metric to evaluate the RFID system. The present paper analyzes the deficiencies of the state-of-the-arts algorithms and proposes a novel sub-frame-based algorithm with adaptive frame breaking policy to lower the tag identification time for EPC global C1 Gen2 UHF RFID standard. Through the observation of slot statistics in a sub-frame, the reader estimates the tag quantity and efficiently calculates an optimal frame size to fit the unread tags. Only when the expected average identification time in the calculated frame size is less than that in the previous frame size, the reader starts the new frame. Moreover, the estimation of the proposed algorithm is implemented by the look-up tables, which allows dramatically reduction in the computational complexity. Simulation results show noticeable throughput and time efficiency improvements of the proposed solution over the existing approaches.

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Cite This Article

H. Chen, K. Liu, C. Ma, Y. Han and J. Su, "A novel time-aware frame adjustment strategy for rfid anti-collision," Computers, Materials & Continua, vol. 57, no.2, pp. 195–204, 2018. https://doi.org/10.32604/cmc.2018.03592

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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