Anonymous Temporal-Spatial Joint Estimation At Category Level Over Multiple Tag Sets

Authors:
Youlin Zhang University of Florida, USA
Shigang Chen University of Florida, USA
You Zhou University of Florida, USA
Yuguang Fang University of Florida, USA

Abstract:

Radio-frequency identification (RFID) technologies have been widely used in inventory management, 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. 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 collect category-level information, which is more significant in practical scenarios where we need to monitor the tagged objects of many different types. Second, we require 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 are moved in a large, distributed supply network. We propose a novel protocol design 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. Extensive simulations show that the proposed protocol can efficiently obtain accurate category-level estimation, while preserving tags' anonymity.

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