Abstract
In the applications of sensor networks, outlier detection has attracted more and more attention. The identification of outliers can be used to filter false data, find faulty nodes and discover interesting events. A few papers have been published for this issue. However some of them consume too much communication, some of them need user to pre-set correct thresholds, some of them generate approximate results rather than exact ones. In this paper, a new unsupervised approach is proposed to detect global top n outliers in the network. This approach can be used to answer both snapshot queries and continuous queries. Two novel concepts, modifier set and candidate set for the global outliers, are defined in the paper. Also a commit-disseminate-verify mechanism for outlier detection in aggregation tree is provided. Using this mechanism and the these two concepts, the global top n outliers can be detected through exchanging short messages in the whole tree. Theoretically, we prove that the results generated by our approach are exact. The experimental results show that our approach is the most communication-efficient one compared with other existing methods. Moreover, our approach does not need any pre-specified threshold. It can be easily extended to multi-dimensional data, and is suitable for detecting outliers of various definitions.
Supported by the Key National Natural Science Foundation of China under Grant No. 60533110; National Grand Fundamental Research 973 Program of China under Grant No. 2006CB303000; the Key National Natural Science Foundation of Heilongjiang Province; the National Natural Science Foundation of China under Grant No. 60473075; Program for New Century Excellent Talents in University ”NCET” under Grant No. NCET-05-0333.
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
Shnayder, V., Hempstead, M., Chen, B.R., Allen, G.W., Welsh, M.: Simulating the Power Consumption of Large-scale Sensor Network Applications. In: SenSys (2004)
Gupta, P., Kumar, P.R.: The Capacity of Wireless Networks. IEEE Trans. Information Theory 46(2), 388–404 (2000)
Warneke, B., Last, M., Liebowitz, B., Pister, K.: Smart Dust: Communicating with A Cubic-millimeter Computer. IEEE Computer Magazine, pp. 44–51 (January 2001)
Gunopulos, D., Kollios, G., Tsotras, J., Domeniconi, C.: Approximating Multi-Dimensional Aggregate Range Queries over Real Attributes. In: SIGMOD (2000)
Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: The Design of An Acquisitional Query Processor for Sensor Networks. In: SIGMOD (2003)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient Algorithms for Mining Outliers from Large Datasets. In: SIGMOD (2000)
Knorr, E., Ng, R.: Algorithms for Mining Distance-Based Outliers in Large Datasets. In: VLDB, 24–27 (1998)
Branch, J., Szymanski, B., Giannella, C., Wolff, R.: In-Network Outlier Detection in Wireless Sensor Networks. In: ICDCS (2006)
Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Online Outlier Detection in Sensor Data Using Non-Parametric Models. In: VLDB (2006)
Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Distributed Deviation Detection in Sensor Networks. ACM SIGMOD 32(4), 77–82 (2003)
Zhuang, Y., Chen, L.: In-network Outlier Cleaning for Data Collection in Sensor Networks. In: CleanDB (2006)
Intel Berkeley Research Lab. http://db.lcs.mit.edu/labdata/labdata.html
Crossbow Technology Inc. http://www.xbow.com/
Ash, J.N., Moses, R.L.: Outlier Compensation in Sensor Network Self-localization via the EM Algorithm. In: ICASSP (2005)
Jun, M.C., Jeong, H., Kuo, C.J.: Distributed Spatio-temporal Outlier Detection in Sensor Networks. In: SPIE (2005)
Janakiram, D., Reddy, A.M., Kumar, A.P.: Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks. In: Comsware (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Zhang, K., Shi, S., Gao, H., Li, J. (2007). Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-73871-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73870-1
Online ISBN: 978-3-540-73871-8
eBook Packages: Computer ScienceComputer Science (R0)