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A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System 被引量:3

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摘要 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 conflicting 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 efficiency.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.
出处 《Computers, Materials & Continua》 SCIE EI 2021年第1期931-945,共15页 计算机、材料和连续体(英文)
基金 the National Natural Science Foundation of China under Grant 61502411 Natural Science Foundation of Jiangsu Province under Grant BK20150432 and BK20151299 Natural Science Research Project for Universities of Jiangsu Province under Grant 15KJB520034 China Postdoctoral Science Foundation under Grant 2015M581843 Jiangsu Provincial Qinglan Project Teachers Overseas Study Program of Yancheng Institute of Technology Jiangsu Provincial Government Scholarship for Overseas Studies Talents Project of Yancheng Institute of Technology under Grant KJC2014038 “2311”Talent Project of Yancheng Institute of Technology Open Fund of Modern Agricultural Resources Intelligent Management and Application Laboratory of Huzhou Normal University.
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