Abstract
In a radio frequency identification (RFID) system, the throughput is limited by tags collision, especially when the tags are distributed intensively. In this paper, we firstly analyze several representative Dynamic Frame Slotted ALOHA (DFSA) algorithms as a review. Then, we propose a novel DFSA algorithm based on grouping and parallel to identify large, unknown numbers of tags in RFID system with the data-intensive distribution. We would like to introduce our algorithm in estimated method for unread tags’ population and grouping strategy for in-frame or out-frame identification processes. Additionally, parallel strategy is imported during the whole communication process to enhance the identification speed. In details, our estimated method is composed in some appropriate sub-methods to cover every possible scenario, and grouping strategy is based on twice-divided beginning with a roughly grouping firstly. As a result, the simulation running by MATLEB exhibits a satisfactory throughput in system efficiency and identification speed when comparing with the similar conventional algorithms.
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Litian, D., Zizhong, W.J., Fu, D. (2015). An Identification Algorithm in Grouping and Paralleling for Data-Intensive RFID Systems. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_27
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DOI: https://doi.org/10.1007/978-3-319-22047-5_27
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