Accelerate the classification statistics in RFID systems

Abstract Radio Frequency Identification (RFID) technology has been widely used in many applications such as logistics, warehouse management and animal identification. However, the dilemma of short time requirement and massive tags makes traditional one-by-one identification methods impractical. Meanwhile, existing off-the-shelf methods cannot count and classify RFID tags at the same time. In this paper, RFID classification statistics problem is defined as classifying the tags into distinct groups and counting the quantity of tags in each group by the reader. The issue of time efficiency is significant in classification statistics, especially when the number of tags is large. To address this problem, we propose a novel Twins Accelerating Gears (TAG) approach. One gear shortens the classification process in frequency domain through subcarrier allocation, when another gear accelerates the statistics process in time domain through geometric distribution based quantity estimation. TAG can handle classification and quantity estimation during one process while existing methods need to handle it separately. We give elaborate proof of the running time and quantity estimation value of the process in theory. Typically, the total time of TAG is O ( log ⁡ N ) and TAG outperforms existing identification solutions about 99.8% time reduction on 1000 tags classified statistics.