Cleaning RFID data streams based on K-means clustering method

doi:10.19682/j.cnki.1005-8885.2020.1009

中国邮电高校学报(英文) ›› 2020, Vol. 27 ›› Issue (2): 72-81.doi: 10.19682/j.cnki.1005-8885.2020.1009

Cleaning RFID data streams based on K-means clustering method

Lin Qiaomin, Fa Anqi, Pan Min, Xie Qiang, Du Kun, Sheng Michael

1. College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2. College of Education Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3. Department of Computing, Macquarie University, Sydney 2109, Australia
4. College of Overseas Education, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

收稿日期:2019-12-12 修回日期:2020-05-11 出版日期:2020-04-30 发布日期:2020-07-07
通讯作者: Lin Qiaomin, E-mail: lqm@njupt.edu.cn E-mail:lqm@njupt.edu.cn
作者简介:Lin Qiaomin, E-mail: lqm@njupt.edu.cn
基金资助:
This work was supported by National Natural Science Foundation of China (61907025, 61807020), Scientific Research Foundation of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (WSNLBZY201512), Jiangsu Government Scholarship for Studying Abroad.

Cleaning RFID data streams based on K-means clustering method

Lin Qiaomin, Fa Anqi, Pan Min, Xie Qiang, Du Kun, Sheng Michael

1. College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2. College of Education Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3. Department of Computing, Macquarie University, Sydney 2109, Australia
4. College of Overseas Education, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Received:2019-12-12 Revised:2020-05-11 Online:2020-04-30 Published:2020-07-07
Contact: Lin Qiaomin, E-mail: lqm@njupt.edu.cn E-mail:lqm@njupt.edu.cn
About author:Lin Qiaomin, E-mail: lqm@njupt.edu.cn
Supported by:
This work was supported by National Natural Science Foundation of China (61907025, 61807020), Scientific Research Foundation of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (WSNLBZY201512), Jiangsu Government Scholarship for Studying Abroad.

摘要/Abstract

摘要： Currentlyradio frequency identification (RFID) technology has been widely used in many kinds of applications. Store retailers use RFID readers with multiple antennas to monitor all tagged items. However, because of the interference from environment and limitations of the radio frequency technology, RFID tags are identified by more than one RFID antenna, leading to the false positive readings. To address this issue, we propose a RFID data stream cleaning method based on K-means to remove those false positive readings within sampling time. First, we formulate a new data stream model which adapts to our cleaning algorithm. Then we present the preprocessing method of the data stream model, including sliding window setting, feature extraction of data stream and normalization. Next, we introduce a novel way using K-means clustering algorithm to clean false positive readings. Last, the effectiveness and efficiency of the proposed method are verified by experiments. It achieves a good balance between performance and price.

关键词: false positive reading, data stream, K-means, RFID

Abstract: Currentlyradio frequency identification (RFID) technology has been widely used in many kinds of applications. Store retailers use RFID readers with multiple antennas to monitor all tagged items. However, because of the interference from environment and limitations of the radio frequency technology, RFID tags are identified by more than one RFID antenna, leading to the false positive readings. To address this issue, we propose a RFID data stream cleaning method based on K-means to remove those false positive readings within sampling time. First, we formulate a new data stream model which adapts to our cleaning algorithm. Then we present the preprocessing method of the data stream model, including sliding window setting, feature extraction of data stream and normalization. Next, we introduce a novel way using K-means clustering algorithm to clean false positive readings. Last, the effectiveness and efficiency of the proposed method are verified by experiments. It achieves a good balance between performance and price.

Key words: false positive reading, data stream, K-means, RFID

Lin Qiaomin, Fa Anqi, Pan Min, Xie Qiang, Du Kun, Sheng Michael. Cleaning RFID data streams based on K-means clustering method[J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(2): 72-81.

参考文献

References
1. Yu L, Qiu H, Li J H, et al. A RFID data cleaning method based on improved M-kernel density estimation. Journal of Information and Computational Science, 2011, 8(13): 2719 -2734
2. Yun H W. Path based K-means clustering for RFID data sets. Journal of Information and Communication Convergence Engineering, 2008, 6(4): 434 -438
3. Han C, Yuan Y S, Mei T, et al. Data stream outlier detection algorithm based on K-means. Computer Engineering and Applications, 2017, 53(3): 58 -63
4. Lin Q, Xiao Y, Ye N. A method of cleaning RFID data streams based on naive bayes classifier. International Journal of Ad Hoc and Ubiquitous Computing, 2016, 21(4): 237 -244
5. Souto G, Muralter F, Arjona L. Protocol for streaming data from an RFID sensor network. Sensors, 2019, 19(14): 3148p
6. Liu X, Yin R, Liu Y. Vital signs monitoring with RFID: opportunities and challenges. IEEE Network, 2019, 33(4): 126 -132
7. Sarkka S, Viikari V, Huusko M. Phase-based UHF RFID tracking with nonlinear kalman filtering and smoothing. IEEE Sensors Journal, 2012, 12(5): 904 -910
8. Shawn R J, Gustavo A, Michael J F. Declarative support for sensor data cleaning. Pervasive Computing, Springer Berlin Heidelberg, 2006: 83 -100
9. Shawn R J, Gustavo A, Michael J F. A pipelined framework for online cleaning of sensor data streams. International Conference on Data Engineering (ICDE 06), 2006: 140p
10. Hector G, Han J W, Shen X H. Cost-conscious cleaning of massive RFID data sets. IEEE 23rd International Conference on Data Engineering (ICDE 07), 2007: 1268 -1272
11. Xie J, Yang J, Chen Y, et al. A sampling-based approach to information recovery. IEEE 24th International Conference on Data Engineering (ICDE 2008), 2008: 476 -485
12. Chen H, Ku W S, Wang H, et al. Leveraging spatio-temporal redundancy for RFID data cleansing. Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, ACM, 2010: 51 -62
13. Dobkin D M. The RF in RFID: UHF RFID in practice. Burlington, MA: Newnes, 2012: 85 -88
14. Ferreira C, Leonardo W, Erik B, et al. Finding misplaced items in retail by clustering RFID data. Proceedings of the 13th International Conference on Extending Database Technology, ACM, 2010: 501 -512
15. Mike L. Speedway installation and operations guide. https://support.impinj.com
16. Muhammad S, Liu A X. Fast and reliable detection and identification of missing RFID tags in the wild. IEEE/ACM Transactions on Networking (TON), 2016, 24(6): 3770 -3784
17. Ganjar A. False positive RFID detection using classification models. Appl Sci 2019, 9(6): 1154p
18. Ma H S. Automatic detection of false positive RFID readings using machine learning algorithms. Expert Systems with Applications, 2018, 91: 442 -451

Cleaning RFID data streams based on K-means clustering method

Cleaning RFID data streams based on K-means clustering method

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