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Single-tag and multi-tag RFID data cleaning approach in edge computing

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Abstract

As the performance of Radio Frequency Identification (RFID) devices is susceptible to the influence of the surrounding environment, making the original data collected by RFID devices with uncertain, of which missed data and redundant data are the main source of uncertainty data, these uncertainty data will seriously affect the quality of RFID upper layer applications. Therefore, to solve the uncertainty in RFID data, the original data must be cleaned. For the shortcomings of tag dynamics detection in the traditional data cleaning algorithm named Statistical Smoothing for Unreliable RFID data (SMURF), an adaptive sliding window-based data cleaning algorithm for RFID single-tag is proposed. The method takes into account the influence of tag speed on tag integrity judgment, and divides sliding sub-windows to accurately detect tag changes and then reasonably adjusts the sliding window size. In addition, considering that the average reading rate of tags is affected by the collision between multiple tags, an RFID multi-tag cleaning method based on twice-tag number estimation is proposed. The method accurately estimates the number of tags by the twice-tag estimation method, controls the read period by the Markov chain, and reduces the occurrence of multiple tag collisions by using an unequal time slot optimization method. Experimental results show that the proposed method in this paper can form a complete set of RFID data stream cleaning algorithms, which effectively reduces the uncertain data and improves the data accuracy.

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Acknowledgements

The work was supported by Open Fund of Yunnan Key Laboratory of Blockchain Application Technology (No. YNB202204), Open Fund of Car Clean Energy Fujian University Application Technology Engineering Center (CQJNY22-02), Open Fund of Key Laboratory of AI and Information Processing (Hechi University), Open Fund of Education Department of Guangxi Zhuang Autonomous Region (No. 2022GXZDSY012), Open Research Fund of Key Laboratory of JiNan Digital Twins and Intelligent Water Conservancy, Grant (No. 37H2022KY040106), Open Fund of State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System (GZ2022KF014), Open Fund of Key Laboratory of Electromechanical Equipment Security in Western Complex Environment for State Market Regulation (No. CQTJ-XBJD-KFKT202201), Open Fund of Key Laboratory of Flight Techniques and Flight Safety, CAAC (No. FZ2022KF18).

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Authors and Affiliations

  1. Car Clean Energy Fujian University Application Technology Engineering Center, Quanzhou Vocational and Technical University, Quanzhou, China

    Chunlin Li

  2. Jinan Key Laboratory of Digital Twin and Intelligent Water Conservancy, Shandong Survey and Design Institute of Water Conservabcy Co., LTD, Jinan, China

    Youlong Luo

  3. Yunnan Key Laboratory of Blockchain Application Technology, Yunnan Innovation Institute of Beihang University, Kunming, China

    Chunlin Li

  4. State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System, Baotou, China

    Chunlin Li, Xinyong Li & Libin Zhang

  5. Key Laboratory of Electromechanical Equipment Security in Western Complex Environment for State Market Regulation, Chongqing Special Equipment Inspection and Research Institute, Chongqing, China

    Kun Jiang

  6. Key Laboratory of Flight Techniques and Flight Safety, CAAC, Civil Aviation Flight University of China, DeYang, Sichuan province, China

    Kun Jiang

  7. Key Laboratory of AI and Information Processing, Education Department of Guangxi Zhuang Autonomous Region, Hechi University, Hechi, China

    Youlong Luo

  8. Department of Computer Science, Wuhan University of Technology, Wuhan, China

    Chunlin Li, Kun Jiang & Youlong Luo

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  1. Chunlin Li
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Contributions

CL, KJ, YL designed the study, developed the methodology, performed the analysis, and wrote the manuscript. XL, LZ collected data.

Corresponding author

Correspondence to Youlong Luo.

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Li, C., Jiang, K., Li, X. et al. Single-tag and multi-tag RFID data cleaning approach in edge computing. Cluster Comput 27, 177–197 (2024). https://doi.org/10.1007/s10586-022-03857-z

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  • DOI: https://doi.org/10.1007/s10586-022-03857-z

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