Fast and Accurate Missing Tag Detection for Multi-category RFID Systems

Abstract

This paper studies the practically important problem of missing tag detection for multi-category RFID systems, where tags are normally categorized into different categories according to types or brands of the associated items. Existing missing tag detection protocols make a common unrealistic assumption that all tags belong to a single category. Although performing an existing missing tag detection protocol separately on each category of tags seems workable, it will take a quite long execution time when tens of tag categories are involved. This paper proposes a Simultaneous Missing Tag Detection (SMTD) protocol, which can decode out multiple frame occupation vectors from one actual time frame. Each frame occupation vector can be used for detection of missing tags in the corresponding category. To guarantee the required detection accuracy on each category and also minimize the total detection time, we propose sufficient theoretical analysis to investigate the parameter settings. Different tag categories may have different tag population sizes or different requirements on detection accuracy, which results in that a pair of parameter settings may not fit all categories at the same time. Indiscriminately performing SMTD on all categories simultaneously may cause a long detection time instead. To address this issue, we propose a supplementary protocol called Category Clustering (CC) to cluster the tag categories that require similar parameter settings into one batch. Then, we perform SMTD protocol on each batch of categories sequentially. Extensive simulation results reveal that the proposed SMTD+CC protocol achieves 2.5~3.6x speedup than the state-of-the-art missing tag detection protocol in a large-scale RFID system.