RFID unreliable data filtering by integrating adaptive sliding window and Euclidean distance

Advances in Manufacturing ›› 2014, Vol. 2 ›› Issue (2): 121-129.

RFID unreliable data filtering by integrating adaptive sliding window and Euclidean distance

Li-Lan Liu • Zi-Long Yuan • Xue-Wei Liu •Cheng Chen • Ke-Sheng Wang

1. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, People’s Republic of China
2. Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, Norway

收稿日期:2014-01-28 修回日期:2014-05-14 出版日期:2014-06-28 发布日期:2014-06-05
通讯作者: e-mail: lancy@shu.edu.cn

RFID unreliable data filtering by integrating adaptive sliding window and Euclidean distance

Li-Lan Liu¹ • Zi-Long Yuan¹ • Xue-Wei Liu ¹•Cheng Chen ¹• Ke-Sheng Wang²

1. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, People’s Republic of China
2. Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, Norway

Received:2014-01-28 Revised:2014-05-14 Online:2014-06-28 Published:2014-06-05
Contact: e-mail: lancy@shu.edu.cn

摘要/Abstract

摘要： Through improving the redundant data filtering of unreliable data filter for radio frequency identification (RFID) with sliding-window, a data filter which integrates self-adaptive sliding-window and Euclidean distance is proposed. The input data required being filtered have been shunt by considering a large number of redundant data existing in the unreliable data for RFID and the redundant data in RFID are the main filtering object with utilizing the filter based on Euclidean distance. The comparison between the results from the method proposed in this paper and previous research shows that it can improve the
accuracy of the RFID for unreliable data filtering and largely reduce the redundant reading rate.

关键词: Radio frequency identification (RFID) , Adaptive sliding window, Euclidean distance , Redundant data

Abstract: Through improving the redundant data filtering of unreliable data filter for radio frequency identification (RFID) with sliding-window, a data filter which integrates self-adaptive sliding-window and Euclidean distance is proposed. The input data required being filtered have been shunt by considering a large number of redundant data existing in the unreliable data for RFID and the redundant data in RFID are the main filtering object with utilizing the filter based on Euclidean distance. The comparison between the results from the method proposed in this paper and previous research shows that it can improve the
accuracy of the RFID for unreliable data filtering and largely reduce the redundant reading rate.

Key words: Radio frequency identification (RFID) , Adaptive sliding window, Euclidean distance , Redundant data

Li-Lan Liu ? Zi-Long Yuan ? Xue-Wei Liu ?Cheng Chen ? Ke-Sheng Wang. RFID unreliable data filtering by integrating adaptive sliding window and Euclidean distance[J]. Advances in Manufacturing, 2014, 2(2): 121-129.

参考文献

1. Mi ZQ (2011) Radio frequency identification (RFID) technology and application. Publishing House of Electronics Industry, Beijing

2. Xia XF, Xuan LJ (2011) RFID unreliable data cleaning strategy under shunting mechanism. Comp Sci 38(10A):22–25

3. Monge AE. An adaptive and efficient algorithm for detecting approximately duplicate database records. http://citeseer.ni.nec.com/monge00adaptive.html. Accessed 30 Oct 2012

4. Jeffery SR, Franklin MJ, Garofalakis MN (2008) An adaptive RFID middleware for supporting Me tap hysical data independence.Proc of Very Large Datebase, Auckland, New Zealand,pp 256–289

5. Chen HQ, Ku WS, Wang HX (2010) Sun minte leveraging spatio-temporal redundancy for RFID data cleansing. In:Proceedings of Special Interest Group on Management of Data,

Indiana, USA, pp 51–62

6. Carbunar B, Ramanathan M, Koyuturk M et al (2005) Redundant reader elimination in RFID systems. In: The 2nd annual IEEE communications society conference on sensor and Ad Hoc communications and networks, 26–29 Sept 2005, pp 176–184

7. Jeffery SR, Alonso G, Franklin MJ et al (2006) A pipelined framework for online cleaning of sensor data streams. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE), Atlanta, Georgia, USA, p 140

8. Gu Y, Yu G, Zhang TC (2007) RFID complex event processing techniques. Front Comp Sci Technol 1(3):255–267

9. Jeffery SR, Garofalakis M, Franklin M (2006) Adaptive cleaning for RFID data streams. In: Proceedings of the 32nd international conference on very large data bases (VLDB), Seoul, Korea,pp 163–174

10. Ma Y, Zhang YY (2009) Based on sliding window of RFID data stream tabbed cleaning algorithm. Sci Technol Eng 9(5):1165–1171

11. Jiu LY, Shan GQ (2013) An effective multi-RFID readers redundant data cleaning algorithm. J Northwest Polytech Univ 29(3):6470–6473

12. Chen XH, Wang X (2013) An improved algorithm in redundant data cleaning based on RFID middleware. Microelectron Comp 30(7): 154–158

13. Chen H, Ku WS, Wang HX et al (2010) Leveraging spatiotemporal redundancy for RFID data cleansing. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data SIGMOD, New York, USA, pp 51–62

14. Li X (2013) Research on RFID middleware data cleaning technology.Dissertation, Beijing Jiaotong University

15. Han JW, Kamber M (2011) Data mining concepts and techniques.Morgan Kaufmann Press, Boston

16. Meng LY, Yu FQ (2010) RFID data cleaning based on adaptive window. In: Proceedings of the 2nd International Conference on Future Computer and Communication. Wuhan, China,pp 746–749

17. Eom KH, Lee SJ (2011) Improved Kalman filter method for measurement noise reduction in multi sensor RFID systems.Sensors, pp 10266–10282

18. Ning HS (2012) RFID major projects and national IOT. China Machinery Industry Press, Beijing

19. Chen L (2013) RFID middleware research data cleaning methods.Dissertation, Wuhan University of Technology

20. Shilei LV, Yu ZS (2012) A kind of RFID system based on middleware reader to redundancy algorithm. Electron J 40(5):965–970

编辑推荐 0

Metrics

阅读次数

全文

466

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	0	0	0	466

来源	本网站	其他网站

次数	128	338
比例	27%	73%

摘要

304

最新录用	在线预览	正式出版

0	0	304

来源	本网站	其他网站

次数	201	103
比例	66%	34%

[1]	Ke-Sheng Wang. Intelligent and integrated RFID (II-RFID) system for improving traceability in manufacturing[J]. Advances in Manufacturing, 2014, 2(2): 106-120.
[2]	Emrah Arica ? Daryl J. Powell. A framework for ICT-enabled real-time production planning and control[J]. Advances in Manufacturing, 2014, 2(2): 158-164.

RFID unreliable data filtering by integrating adaptive sliding window and Euclidean distance

RFID unreliable data filtering by integrating adaptive sliding window and Euclidean distance

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐 0

Metrics

本文评价