Open Access

ARTICLE

A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System

Cuixiang Wang¹, Xing Shao^1,2,3,*, Yifan Meng⁴, Jun Gao¹

1 School of Information Engineering, Yancheng Institute of Technology, Yancheng, 224000, China
2 School of Computer, Nanjing University of Posts and Telecommunication, Nanjing, 210003, China
3 Modern Agricultural Resources Intelligent Management and Application Laboratory, Huzhou Normal University, Huzhou, 313000, China
4 School of Science and Technology, Troy University, Troy, 36082, USA

* Corresponding Author: Xing Shao. Email:

Computers, Materials & Continua 2021, 66(1), 931-945. https://doi.org/10.32604/cmc.2020.012267

Received 23 June 2020; Accepted 12 August 2020; Issue published 30 October 2020

Abstract

In RFID (Radio Frequency IDentification) system, when multiple tags are in the operating range of one reader and send their information to the reader simultaneously, the signals of these tags are superimposed in the air, which results in a collision and leads to the degrading of tags identifying efficiency. To improve the multiple tags’ identifying efficiency due to collision, a physical layer network coding based binary search tree algorithm (PNBA) is proposed in this paper. PNBA pushes the conflicting signal information of multiple tags into a stack, which is discarded by the traditional anti-collision algorithm. In addition, physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the con- flicting information in the stack. Therefore, PNBA reduces the number of interactions between reader and tags, and improves the tags identification efficiency. Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings, and improve RFID identification effi- ciency. Especially, when the number of tags to be identified is 100, the average needed reading number of PNBA is 83% lower than the basic binary search tree algorithm, 43% lower than reverse binary search tree algorithm, and its reading efficiency reaches 0.93.

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Keywords

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