Computer Science

Efficient anonymous temporal-spatial joint estimation at category level over multiple tag sets with unreliable channels

Document Type

Article

Abstract

Radio-frequency identification (RFID) technologies have been widely used in inventory control, object tracking and supply chain management. One of the fundamental system functions is called cardinality estimation, which is to estimate the number of tags in a covered area. In this paper, we extend the research of this function in two directions. First, we perform joint cardinality estimation among tags that appear at different geographical locations and at different times. Moreover, we target at category-level information, which is more significant in practical scenarios where we need to monitor the tagged objects of many different categories. Second, we enforce anonymity in the process of information gathering in order to preserve the privacy of the tagged objects. These capabilities will enable new applications such as tracking how products of different categories are transferred in a large, distributed supply chain. We propose and implement a novel protocol to meet the requirements of anonymous category-level joint estimation over multiple tag sets. We formally analyze the performance of our estimator and determine the optimal system parameters. Moreover, we extend our protocol to unreliable channels and consider two channel error models. Extensive simulations show that the proposed protocol can efficiently and accurately estimate joint information over multiple tag sets at category level, while preserving tags' anonymity.

Publication Title

IEEE/ACM Transactions on Networking

Publication Date

2020

Volume

28

Issue

5

First Page

2174

Last Page

2187

ISSN

1063-6692

DOI

10.1109/TNET.2020.3011347

Keywords

radio-frequency identification (RFID) tags, ultra high frequency (UHF) communication, wireless application protocol

APA Citation

Zhang, Y., Chen, S., Zhou, Y., Odegbile, O. O., & Fang, Y. (2020). Efficient anonymous temporal-spatial joint estimation at category level over multiple tag sets with unreliable channels. IEEE/ACM Transactions on networking, 28(5), 2174-2187.

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