TY - JOUR
T1 - Fast and Reliable Dynamic Tag Estimation in Large-Scale RFID Systems
AU - Xi, Zhong
AU - Liu, Xuan
AU - Luo, Juan
AU - Zhang, Shigeng
AU - Guo, Song
N1 - Funding Information:
Manuscript received May 14, 2020; revised July 1, 2020; accepted July 29, 2020. Date of publication August 12, 2020; date of current version January 22, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61602167, Grant 61772559, Grant 61872310, and Grant 61972140; in part by the Hunan Provincial Natural Science Foundation of China under Grant 2020JJ3016; and in part by the National Defense Basic Research Plan under Grant JCKY2018110C145. The work of Xuan Liu was supported in part by the National Defense Science and Technology Innovation Special Zone Project of China. The work of Song Guo was supported in part by the funding from Hong Kong RGC Research Impact Fund (RIF) under Project R5034-18, and in part by the General Research Fund of the Research Grants Council of Hong Kong under Grant PolyU 152221/19E. (Corresponding authors: Xuan Liu; Shigeng Zhang.) Zhong Xi and Juan Luo are with the College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2014 IEEE.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - Radio-frequency identification (RFID) has been utilized in many applications, such as supply chain and stock management in supermarkets. RFID systems in such practical applications are inherently dynamic because tags may move in and out frequently. One important but challenging problem in such systems is how to estimate the number of dynamic tags fast and reliably. This article proposes effective solutions to this problem, which guarantee the accuracy of estimation and time efficiency. Especially, we want to simultaneously estimate the number of tags that moved out of the system (missing tags) and the number of tags that entered the system (unknown tags) in a specified time interval. We design a novel method called time slot reuse (TSR) that generates two logical frames corresponding to the two types of tags from only one physical frame. Based on TSR, we propose a protocol called SSR that can accurately estimate the number of dynamic tags by using the generated logic frames. However, the performance of SSR degrades significantly when the disparity between the number of the two types of tags is remarkable. We further propose an enhanced version of SSR (ESSR), which overcomes this drawback by partitioning the frame into ranges and mapping different types of tags into different ranges. Rigorous theoretical analysis is performed to tune parameters in SSR and ESSR to minimize the execution time. The simulation results demonstrate up to 80% improvement in time efficiency when compared with state-of-the-art solutions to the same problem.
AB - Radio-frequency identification (RFID) has been utilized in many applications, such as supply chain and stock management in supermarkets. RFID systems in such practical applications are inherently dynamic because tags may move in and out frequently. One important but challenging problem in such systems is how to estimate the number of dynamic tags fast and reliably. This article proposes effective solutions to this problem, which guarantee the accuracy of estimation and time efficiency. Especially, we want to simultaneously estimate the number of tags that moved out of the system (missing tags) and the number of tags that entered the system (unknown tags) in a specified time interval. We design a novel method called time slot reuse (TSR) that generates two logical frames corresponding to the two types of tags from only one physical frame. Based on TSR, we propose a protocol called SSR that can accurately estimate the number of dynamic tags by using the generated logic frames. However, the performance of SSR degrades significantly when the disparity between the number of the two types of tags is remarkable. We further propose an enhanced version of SSR (ESSR), which overcomes this drawback by partitioning the frame into ranges and mapping different types of tags into different ranges. Rigorous theoretical analysis is performed to tune parameters in SSR and ESSR to minimize the execution time. The simulation results demonstrate up to 80% improvement in time efficiency when compared with state-of-the-art solutions to the same problem.
KW - Cardinality estimation
KW - radio-frequency identification (RFID) system
KW - tags
KW - time efficiency
UR - http://www.scopus.com/inward/record.url?scp=85100259901&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2020.3015943
DO - 10.1109/JIOT.2020.3015943
M3 - Journal article
AN - SCOPUS:85100259901
SN - 2327-4662
VL - 8
SP - 1651
EP - 1661
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 3
M1 - 9165802
ER -