Abstract
With the rapid development of technologies related to Ubiquitous Sensor Network (USN), sensors are being utilized in various application areas. In general, a sensor has a low computing capacity and power and keeps sending data to the central server. In this environment, uncertain data can be stored in the central server due to delayed transmission or other reasons and make query processing produce wrong results. Thus, this paper examines how to process uncertain data in ubiquitous sensor networks and suggests an efficient index, called UR-tree, for uncertain data. The index reduces the cost of update by delaying update in uncertainty areas. In addition, it solves the problem of low accuracy in search resulting from update delay by delaying update only for specific update areas. Lastly, we analyze the performance of UR-tree and prove the superiority of its performance by comparing its performance with that of R-Tree and PTI using various datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cheng, R., Prabhakar, S.: Managing Uncertainty in Sensor Databases. SIGMOD Record 32(4), 41–46 (2003)
Cheng, R., et al.: Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data. In: Proceedings of the 30th Intl. Conf. on Very Large Databases(VLDB), pp. 876–887 (2004)
Civilis, A., Jensen, C.S., Pakalnis, S.: Techniques for Efficient Road-Network-Based Tracking of Moving Objects. IEEE Transactions on Knowledge and Data Engineering 17(5), 698–712 (2005)
Dai, X., et al.: Probabilistic Spatial Queries on Existentially Uncertain Data. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 400–417. Springer, Heidelberg (2005)
Guttman, A.: R-trees: A Dynamic Index Structure for Spatial Searching. In: Proceedings of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 47–57 (1984)
Hosbond, J.H., Saltenis, S., Ørtoft, R.: Indexing Uncertainty of Continuously Moving Objects. In: Proceedings of DEXA Workshops, pp. 911–915 (2003)
Kwon, D.S., Lee, S.J., Lee, S.H.: Indexing the Current Positions of Moving Objects Using the Lazy Update R-tree. In: Proceedings of the Intl. Conf. on Mobile Data Management, pp. 113–120 (2002)
Olston, C., Loo, B.T., Widom, J.: Adaptive Precision Setting for Cached Approximate Values. In: Proceedings of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 355–366 (2001)
Tao, Y., et al.: Indexing Multi-Dimensional Uncertain Data with Arbitrary Probability Density Functions. In: Proceedings of the 30th Intl. Conf. on Very Large Databases(VLDB), pp. 922–933 (2005)
Trajcevski, G., et al.: Managing Uncertainty in Moving Objects Databases. ACM Transactions on Database Systems (TODS) 29(3), 463–507 (2004)
Yuni, X., et al.: Indexing Continuously Changing Data with Mean-Variance Tree. In: Proceedings of the ACM Symposium on Applied Computing, pp. 1125–1132 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Kim, DO., Hong, DS., Kang, HK., Han, KJ. (2007). UR-Tree: An Efficient Index for Uncertain Data in Ubiquitous Sensor Networks. In: Cérin, C., Li, KC. (eds) Advances in Grid and Pervasive Computing. GPC 2007. Lecture Notes in Computer Science, vol 4459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72360-8_51
Download citation
DOI: https://doi.org/10.1007/978-3-540-72360-8_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72359-2
Online ISBN: 978-3-540-72360-8
eBook Packages: Computer ScienceComputer Science (R0)