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A Frame Breaking Based Hybrid Algorithm for UHF RFID Anti-Collision

Xinyan Wang1,*, Minjun Zhang2, Zengwang Lu3

Intelligent Science and Information Engineering College, Xi’an Peihua University, Xi’an, 710125, China.
School of Information Science and Technology, Northwest University, Xi’an, 710127, China.
Huawei Technologies Dusseldorf GmbH, Düsseldorf 40549, Germany.

* Corresponding Author: Xinyan Wang. Email: email.

Computers, Materials & Continua 2019, 59(3), 873-883. https://doi.org/10.32604/cmc.2019.05230

Abstract

Multi-tag collision imposes a vital detrimental effect on reading performance of an RFID system. In order to ameliorate such collision problem and to improve the reading performance, this paper proposes an efficient tag identification algorithm termed as the Enhanced Adaptive Tree Slotted Aloha (EATSA). The key novelty of EATSA is to identify the tags using grouping strategy. Specifically, the whole tag set is divided into groups by a frame of size F. In cases multiple tags fall into a group, the tags of the group are recognized by the improved binary splitting (IBS) method whereas the rest tags are waiting in the pipeline. In addition, an early observation mechanism is introduced to update the frame size to an optimum value fitting the number of tags. Theoretical analysis and simulation results show that the system throughput of our proposed algorithm can reach as much as 0.46, outperforming the prior Aloha-based protocols.

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Cite This Article

APA Style
Wang, X., Zhang, M., Lu, Z. (2019). A frame breaking based hybrid algorithm for UHF RFID anti-collision. Computers, Materials & Continua, 59(3), 873-883. https://doi.org/10.32604/cmc.2019.05230
Vancouver Style
Wang X, Zhang M, Lu Z. A frame breaking based hybrid algorithm for UHF RFID anti-collision. Comput Mater Contin. 2019;59(3):873-883 https://doi.org/10.32604/cmc.2019.05230
IEEE Style
X. Wang, M. Zhang, and Z. Lu, “A Frame Breaking Based Hybrid Algorithm for UHF RFID Anti-Collision,” Comput. Mater. Contin., vol. 59, no. 3, pp. 873-883, 2019. https://doi.org/10.32604/cmc.2019.05230

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cc Copyright © 2019 The Author(s). Published by Tech Science Press.
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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