Dynamic RFID Data Filtering and Application

Khaled Mohammed

doi:10.58963/qausrj.v1i25.104

Authors

Khaled Mohammed Queen Arwa University Author

DOI:

https://doi.org/10.58963/qausrj.v1i25.104

Keywords:

Filtering Technique , Duplicate Elimination , RFID system , De-noising RFID data , Dynamic RFID Data Filtering and Employee Attendance Management Syste

Abstract

Radio frequency Identification “RFID” Technology uses the radio frequency waves to transfer the RFID data between RFID readers and tags which are used to identify objects/employees without line of sight. The RFID data which is captured by the tag reader may contain false readings, noise, and duplicates which implies data filtering and cleaning. Therefore, it is necessary to develop efficient processing algorithms of RFID data. This pa-per presents a dynamic technique to filter the RFID data, elimi-nate duplicates and filter noise. Data filtering during employee identification in the work place enhances the performance of employee attendance management systems. The proposed sys-tem compared to the Denoising and duplication Elimination approach under different arrival rates at a rate 0.1 tag/sec and under the noise rate at rate between 0.085-0.01 tag/sec.

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Author Biography

Khaled Mohammed, Queen Arwa University

khaled.alburaihi@gmail.com

References

O. Mylyy, “RFID Data management, aggregation and filtering,” Hasso lattner Institute at the university of Potsdam, 2006.

C. Floerkemeier, D. Anarkat, T. Osinski, and M. Harrison, “PML core specification 1.0,” Auto-ID Center White Paper, 2003.

C. C. Aggarwal and J. Han, “A survey of RFID data processing,” in Managing and Mining Sensor Data, ed: Springer, 2013, pp. 349-382.

J. Brusey, C. Floerkemeier, M. Harrison, and M. Fletcher, “Reasoning about uncertainty in location identification with RFID,” in Workshop on Reasoning with Uncertainty in Robotics at IJCAI, 2003, pp. 23-30.

H. Vogt, “Efficient object identification with passive RFID tags,” in Pervasive computing, ed: Springer, 2002, pp. 98-113.

Y. Bai, F. Wang, and P. Liu, “Efficiently Filtering RFID Data Streams,” in CleanDB, 2006.

H. Mahdin and J. Abawajy, “An approach to filtering RFID data streams,” in Pervasive Systems, Algorithms, and Networks (ISPAN), 2009 10th International Symposium on, 2009, pp. 742-746.

S. Tyagi, A. Ansari, and M. A. Khan, RFID Data Management: INTECH Open Access Publisher, 2010.

S. Tyagi, A. Ansari, and M. A. Khan, “Dynamic threshold based slidingwindow filtering technique for RFID data,” in Advance Computing Conference (IACC), 2010 IEEE 2nd International, 2010, pp. 115-120.

A. K. Bashir, M.-S. Park, S.-I. Lee, J. Park, W. Lee, and S. C. Shah,“In-network RFID Data Filtering Scheme in RFID-WSN for RFID Applications,” in Intelligent Robotics and Applications, ed: Springer, 2013, pp. 454-465.

R. Derakhshan, M. E. Orlowska, and X. Li, “RFID data management:challenges and opportunities,” in IEEE International conference on RFID,2007, pp. 175-182.

H. Kamaludin, H. Mahdin, and J. H. Abawajy, “Filtering Redundant Data from RFID Data Streams,” Journal of Sensors, vol. 2016, 2015.

H. Yongsheng and G. Zhijun, “Redundancy removal approach for integrated rfid readers with counting bloom filter,” Journal of Computer Information Systems, vol. 9, pp. 1917-1924, 2013.

Z. Ji, Z. Luo, E. Wong, and C. T. X. Peng, “A P2P Collaborative RFID Data Cleaning Model,” in Hong Kong the 3rd International Conference on Grid and Pervasive Computing-Workshops2008 IEEE, 2008.