Abstract
Recently, wireless sensor networks providing fine-grained spatiotemporal observations have become one of the major monitoring platforms for geo-applications. Along side data acquisition, outlier detection is essential in geosensor networks to ensure data quality, secure monitoring and reliable detection of interesting and critical events. A key challenge for outlier detection in these geosensor networks is accurate identification of outliers in a distributed and online manner while maintaining low resource consumption. In this paper, we propose an online outlier detection technique based on one-class hyperellipsoidal SVM and take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. Experiments with both synthetic and real data show that our online outlier detection technique achieves better detection accuracy compared to the existing SVM-based outlier detection techniques designed for sensor networks. We also show that understanding data distribution and correlations among sensor data is essential to select the most suitable outlier detection technique.
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
Nittel, S., Labrinidis, A., Stefanidis, A.: GeoSensor Networks. Springer, Heidelberg (2006)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly Detection: A Survey. Technical report, University of Minnesota (2007)
SensorScope, http://sensorscope.epfl.ch/index.php/Main_Page
Rajasegarar, S., Leckie, C., Palaniswami, M.: CESVM: Centered Hyperellipsoidal Support Vector Machine Based Anomaly Detection. In: IEEE International Conference on Communications, pp. 1610–1614. IEEE Press, Beijing (2008)
Zhang, Y., Meratnia, N., Havinga, P.J.M.: An Online Outlier Detection Technique for Wireless Sensor Networks using Unsupervised Quarter-Sphere Support Vector Machine. In: 4th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 151–156. IEEE Press, Sydney (2008)
Zhang, Y., Meratnia, N., Havinga, P.J.M.: Outlier Detection Techniques for Wireless Sensor Network: A Survey. Technical report, University of Twente (2008)
Scholkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the Support of a High-Dimensional Distribution. Journal of Neural Computation 13(7), 1443–1471 (2001)
Tax, D.M.J., Duin, R.P.W.: Support Vector Data Description. Journal of Machine Learning 54(1), 45–56 (2004)
Wang, D., Yeung, D.S., Tsang, E.C.C.: Structured One-Class Classification. IEEE Transactions on System, Man and Cybernetics 36(6), 1283–1295 (2006)
Laskov, P., Schafer, C., Kotenko, I.: Intrusion Detection in Unlabeled Data with Quarter Sphere Support Vector Machines. In: Detection of Intrusions and Malware & Vulnerability Assessment, pp. 71–82. Dortmund (2004)
Rajasegarar, S., Leckie, C., Palaniswami, M., Bezdek, J.C.: Quarter Sphere based Distributed Anomaly Detection in Wireless Sensor Networks. In: IEEE International Conference on Communications, pp. 3864–3869. IEEE Press, Glasgow (2007)
Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)
Golub, G.H., Loan, C.F.V.: Matrix Computations. John Hopkins (1996)
Nash, S.G., Sofer, A.: Linear and Nonlinear Programming. McGraw-Hill, New York (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, Y., Meratnia, N., Havinga, P. (2009). Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks. In: Trigoni, N., Markham, A., Nawaz, S. (eds) GeoSensor Networks. GSN 2009. Lecture Notes in Computer Science, vol 5659. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02903-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-02903-5_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02902-8
Online ISBN: 978-3-642-02903-5
eBook Packages: Computer ScienceComputer Science (R0)