Abstract
The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Networked RFID (radio frequency identification), which uses the Internet to connect otherwise isolated RFID systems and software, is an emerging technology to support traceability applications. Despite its promising benefits, there remains many challenges to be overcome before these benefits can be realized. One significant challenge centers around dealing with uncertainty of raw RFID data. In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks. The framework consists of a global object tracking model and a local RFID data cleaning model. In particular, we propose a Markov-based model for tracking objects globally and a particle filter based approach for processing noisy, low-level RFID data locally. Our implementation validates the proposed approach and the experimental results show its effectiveness.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Nath, B., Reynolds, F., Want, R.: Rfid technology and applications. IEEE Pervasive Computing 5, 22–24 (2006)
Wu, Y., Ranasinghe, D.C., Sheng, Q.Z., Zeadally, S., Yu, J.: RFID Enabled Traceability Networks: A Survey. Distributed and Parallel Databases 29, 397–443 (2011)
Franklin, M., Jeffery, S., Krishnamurthy, S., Reiss, F., Rizvi, S., Wu, E., Cooper, O., Edakkunni, A., Hong, W.: Design considerations for high fan-in systems: The hifi approach. In: Proceedings of the 2nd Biennial Conference on Innovative Data Systems Research, CIDR 2005 (2005)
Gonzalez, H., Han, J., Li, X., Klabjan, D.: Warehousing and analyzing massive rfid data sets. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, pp. 83–83. IEEE (2006)
Ilic, A., Andersen, T., Michahelles, F.: Increasing supply-chain visibility with rule-based rfid data analysis. IEEE Internet Computing 13, 31–38 (2009)
Roussos, G., Duri, S., Thompson, C.: Rfid meets the internet. IEEE Internet Computing 13, 11–13 (2009)
Jeffery, S., Franklin, M., Garofalakis, M.: An adaptive rfid middleware for supporting metaphysical data independence. The VLDB Journal 17, 265–289 (2008)
Sheng, Q., Li, X., Zeadally, S.: Enabling next-generation rfid applications: Solutions and challenges. Computer 41, 21–28 (2008)
Fox, V., Hightower, J., Liao, L., Schulz, D., Borriello, G.: Bayesian filtering for location estimation. IEEE Pervasive Computing 2(3), 24–33 (2003)
Russell, S.: Artificial intelligence: A modern approach, December 30 (2002)
Ng, B., Peshkin, L., Pfeffer, A.: Factored particles for scalable monitoring. In: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pp. 370–377 (2002)
EPCGLOBAL, http://www.epcglobal.com
Wu, Y., Sheng, Q., Ranasinghe, D.: P2p object tracking in the internet of things. In: Proceedings of International Conference on Parallel Processing (ICPP 2011), pp. 502–511. IEEE (2011)
Cambridge University: Serial-level inventory tracking model. Bridge WP03, Cambridge University, BT Research (2007)
Nie, Y., Cocci, R., Cao, Z., Diao, Y., Shenoy, P.: Spire: Efficient data inference and compression over rfid streams. IEEE Transactions on Knowledge and Data Engineering, 141–155 (2012)
Diao, Y., Li, B., Liu, A., Peng, L., Sutton, C., Tran, T., Zink, M.: Capturing data uncertainty in high-volume stream processing. In: Proceedings of the 4th Biennial Conference on Innovative Data Systems Research, CIDR 2009 (2009)
Welbourne, E., Khoussainova, N., Letchner, J., Li, Y., Balazinska, M., Borriello, G., Suciu, D.: Cascadia: A system for specifying, detecting, and managing rfid events. In: Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services (MobiSys 2008), New York, USA, pp. 281–294 (2008)
Wang, F., Liu, P.: Temporal management of rfid data. In: Proceedings of International Conference on Very Large Databases (VLDB 2005), Norway, pp. 1128–1139 (2005)
Soliman, M., Ilyas, I., Chen-Chuan Chang, K.: Top-k query processing in uncertain databases. In: Proceedings of the 23rd International Conference on Data Engineering (ICDE 2007), pp. 896–905. IEEE (2007)
Zhang, Y., Lin, X., Zhu, G., Zhang, W., Lin, Q.: Efficient rank based knn query processing over uncertain data. In: Proceedings of the 26th International Conference on Data Engineering (ICDE 2010), pp. 28–39. IEEE (2010)
Cormode, G., Garofalakis, M.: Sketching probabilistic data streams. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 281–292. ACM (2007)
EPCglobal: EPCglobal Specifications, http://www.epcglobalinc.org/standards/specs
Mo, J., Sheng, Q., Li, X., Zeadally, S.: Rfid infrastructure design: a case study of two australian rfid projects. IEEE Internet Computing 13, 14–21 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, J., Sheng, Q.Z., Ranasinghe, D., Chuah, J.M., Wu, Y. (2012). A Framework for Distributed Managing Uncertain Data in RFID Traceability Networks. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds) Web Information Systems Engineering - WISE 2012. WISE 2012. Lecture Notes in Computer Science, vol 7651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35063-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-35063-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35062-7
Online ISBN: 978-3-642-35063-4
eBook Packages: Computer ScienceComputer Science (R0)