Efficient Range Queries for Large-Scale Sensor-Augmented RFID Systems

Liu, Xiulong; Xie, Xin; Wang, Shangguang; Liu, Jia; Yao, Didi; Cao, Jiannong; Li, Keqiu

doi:10.1109/tnet.2019.2936977

Cited by 43 publications

(22 citation statements)

References 31 publications

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“…Such problem has been studied in the previous work [39][40] and is outside the scope of this paper. Moreover, the authors in the literature [41] recently verified that bitwise synchronization is feasible in the UHF RFID system by carrying out the practical experiments with USRP and WISP tags. Also, we make similar assumption that a communication channel between the reader and tags is error-free and with no capture-effect [13][14] [17][18][19] [23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Preliminaries: Bit Tracking and Id Querymentioning

confidence: 99%

From M-Ary Query to Bit Query: A New Strategy for Efficient Large-Scale RFID Identification

Chen

Sheng

et al. 2020

IEEE Trans. Commun.

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Section: Preliminaries: Bit Tracking and Id Querymentioning

confidence: 99%

From M-Ary Query to Bit Query: A New Strategy for Efficient Large-Scale RFID Identification

Chen

Sheng

et al. 2020

IEEE Trans. Commun.

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“…1 The protocols for the group writing problem require a time-consuming preprocess that writes an additional bit-vector into the user memory of each tag. 2 Therefore, they can not be applied to an RFID system that contains read-only tags (read-only tags do not have any user memory into which the userspecific data can be stored).…”

Section: A Prior Arts and Limitationsmentioning

confidence: 99%

“…(2) Randomly choose N different EPCs from U to form set S and then randomly choose n EPCs from S to form set P; (3) Do exhaustive enumeration to find all the 1-filter-strings for the sets: S and P which are determined in step (2), and record this number; (4) Repeat step (2) and (3) for 10 5 times to compute the average number of 1-filter-strings for the number n. Please note that the average number we get in the above procedure provides a simulated upper bound of the probability of existence of 1-filter-string solution for a given parameter n. Fig. 2 shows the average number of 1-filterstring solutions for the two settings.…”

Section: Select Commandmentioning

confidence: 99%

EPC-Based Efficient Tag Selection in RFID Systems

Zhu

Wang

Yang³

et al. 2020

IEEE Access

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Tag-selection problem, which selects a set of wanted tags from a tag population, is vital for boosting efficiencies of the real-time applications in RFID systems. However, prior arts for the problem can not be applied to RFID systems directly, given that they either require additional computing functions implemented in tag's chips or require a time-consuming pre-process with a large communication cost. This paper studies the tag-selection problem and propose an efficient Electronic Product Code (EPC)-based tag selection method with theoretical analysis. In particular, firstly, we prove a nontrivial lower bound of communication overhead for a protocol which is capable of solving the tag-selection problem. Secondly, we propose an efficient protocol, denoted by TagSP, which only uses the ''select'' command (a mandatory command that all RFID tags shall support) and EPC. The proposed TagSP can be applied directly into offthe-shelf RFID systems with a communication overhead close to the lower bound. Extensive simulations are conducted and the simulation results show TagSP's superiority compared with existing protocols. INDEX TERMS RFID systems, tag selection, lower bound of communication overhead, EPC.

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“…Passive RFID tags are small and sufficiently cheap to be widely used in applications such as logistics and warehousing [11,12]. Moreover, researchers have found that RFID tags can be used to detect the object usage by attaching them on objects used every day [13]. Fazzzinga et al even utilized RFID tags to trace the objects both online and offline [14,15,16].…”

Section: Design Of the Rf-arp Frameworkmentioning

confidence: 99%

A Novel Human Activity Recognition and Prediction in Smart Home Based on Interaction

Lim

Tan

2019

Sensors

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Smart Homes are generally considered the final solution for living problem, especially for the health care of the elderly and disabled, power saving, etc. Human activity recognition in smart homes is the key to achieving home automation, which enables the smart services to automatically run according to the human mind. Recent research has made a lot of progress in this field; however, most of them can only recognize default activities, which is probably not needed by smart homes services. In addition, low scalability makes such research infeasible to be used outside the laboratory. In this study, we unwrap this issue and propose a novel framework to not only recognize human activity but also predict it. The framework contains three stages: recognition after the activity, recognition in progress, and activity prediction in advance. Furthermore, using passive RFID tags, the hardware cost of our framework is sufficiently low to popularize the framework. In addition, the experimental result demonstrates that our framework can realize good performance in both activity recognition and prediction with high scalability.

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Efficient Range Queries for Large-Scale Sensor-Augmented RFID Systems

Cited by 43 publications

References 31 publications

From M-Ary Query to Bit Query: A New Strategy for Efficient Large-Scale RFID Identification

From M-Ary Query to Bit Query: A New Strategy for Efficient Large-Scale RFID Identification

EPC-Based Efficient Tag Selection in RFID Systems

A Novel Human Activity Recognition and Prediction in Smart Home Based on Interaction

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