Abstract
Radio Frequency Identification (RFID) classification statistics problem is defined as classifying the tags into distinct groups and counting the quantity of tags in each group. The issue of time efficiency is significant in classification statistics, especially when the number of tags is large. In such case, the dilemma of short time requirement and massive tags makes traditional one-by-one identification methods impractical. This paper studies the problem of fast classification statistics in RFID systems. 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.
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Acknowledgements
The work is partly supported by China NSF grants (61672349, 61672353, 61472252, 61373155) and China 973 project (2014CB340303).
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Wen, Z., Huang, J., Kong, L., Wu, MY., Chen, G. (2017). Classification Statistics in RFID Systems. In: Gao, X., Du, H., Han, M. (eds) Combinatorial Optimization and Applications. COCOA 2017. Lecture Notes in Computer Science(), vol 10628. Springer, Cham. https://doi.org/10.1007/978-3-319-71147-8_29
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DOI: https://doi.org/10.1007/978-3-319-71147-8_29
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