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.

Downloads

Download data is not yet available.

Author Biography

Khaled Mohammed, Queen Arwa University

Assistant Professor, Head of Accreditation and Quality Assurance Division, Sana'a Community College, Sana'a, Yemen.
Head of the Management Information Systems Department, Faculty of Economics and Administrative Sciences, Queen Arwa University, Sana'a, Yemen.
Email: khaled.alburaihi@gmail.com

References

Mylyy, O. (2006). RFID data management, aggregation and filtering [Diploma thesis, Hasso Plattner Institute]. University of Potsdam.

Floerkemeier, C., Anarkat, D., Osinski, T., & Harrison, M. (2003). PML core specification 1.0 (Auto-ID Center White Paper). Auto-ID Center.

Aggarwal, C. C. (2013). A survey of RFID data processing. In C. C. Aggarwal (Ed.), Managing and mining sensor data (pp. 349–382). Springer. https://doi.org/10.1007/978-1-4614-6309-2_11

Brusey, J., Floerkemeier, C., Harrison, M., & Fletcher, M. (2003, August 9–15). Reasoning about uncertainty in location identification with RFID [Paper presentation]. Workshop on Reasoning with Uncertainty in Robotics at the International Joint Conference on Artificial Intelligence (IJCAI'03), Acapulco, Mexico.

Vogt, H. (2002). Efficient object identification with passive RFID tags. In M. Beigl & S. Intille (Eds.), Lecture Notes in Computer Science: Vol. 2414. Pervasive Computing (pp. 98–113). Springer. https://doi.org/10.1007/3-540-45809-3_9

Bai, Y., Wang, F., & Liu, P. (2006, September 11–15). Efficiently filtering RFID data streams [Paper presentation]. International Workshop on Data Cleaning and Data Integration (CleanDB), Seoul, Korea.

Mahdin, H., & Abawajy, J. (2009). An approach to filtering RFID data streams. In Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks (pp. 742–746). IEEE. https://doi.org/10.1109/I-SPAN.2009.117

Tyagi, S., Ansari, A., & Khan, M. A. (Eds.). (2010). RFID data management. IntechOpen. https://doi.org/10.5772/2493

Tyagi, S., Ansari, A., & Khan, M. A. (2010). Dynamic threshold based sliding-window filtering technique for RFID data. In Proceedings of the 2010 IEEE 2nd International Advance Computing Conference (IACC) (pp. 115–120). IEEE. https://doi.org/10.1109/IADCC.2010.5429497

Bashir, A. K., Park, M.-S., Lee, S.-I., Park, J., Lee, W., & Shah, S. C. (2013). In-network RFID data filtering scheme in RFID-WSN for RFID applications. In J. Lee, M. Lee, H. Liu, & J.-H. Kim (Eds.), Lecture Notes in Computer Science: Vol. 8103. Intelligent Robotics and Applications (pp. 454–465). Springer. https://doi.org/10.1007/978-3-642-40852-6_45

Derakhshan, R., Orlowska, M. E., & Li, X. (2007). RFID data management: Challenges and opportunities. In Proceedings of the 2007 IEEE International Conference on RFID (pp. 175–182). IEEE. https://doi.org/10.1109/RFID.2007.346163

Kamaludin, H., Mahdin, H., & Abawajy, J. H. (2016). Filtering redundant data from RFID data streams. Journal of Sensors, 2016, Article 8278234. https://doi.org/10.1155/2016/8278234

He, Y., & Guo, Z. (2013). Redundancy removal approach for integrated RFID readers with counting bloom filter. Journal of Computer Information Systems, 9(5), 1917–1924.

Ji, Z., Luo, Z., Wong, E., & Peng, C. T. X. (2008). A P2P collaborative RFID data cleaning model. In Proceedings of the 2008 Third International Conference on Grid and Pervasive Computing - Workshops (pp. 370–375). IEEE. https://doi.org/10.1109/GPC.2008.WORKSHOPS.63