Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)

Abstract

Recently, wireless sensor networks providing fine-grained spatio-temporal 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 re- liable 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 resource consumption low. 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.
Original languageUndefined
Title of host publicationThird International Conference on Geosensor Networks
Place of PublicationBerlin
PublisherSpringer
Pages31-41
Number of pages11
DOIs
Publication statusPublished - 13 Jul 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
Volume5659
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • METIS-263930
  • EWI-15723
  • IR-67815

Cite this

Zhang, Y., Meratnia, N., & Havinga, P. J. M. (2009). Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks. In Third International Conference on Geosensor Networks (pp. 31-41). [10.1007/978-3-642-02903-5_4] (Lecture Notes in Computer Science; Vol. 5659). Berlin: Springer. https://doi.org/10.1007/978-3-642-02903-5_4
Zhang, Y. ; Meratnia, Nirvana ; Havinga, Paul J.M. / Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks. Third International Conference on Geosensor Networks. Berlin : Springer, 2009. pp. 31-41 (Lecture Notes in Computer Science).
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abstract = "Recently, wireless sensor networks providing fine-grained spatio-temporal 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 re- liable 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 resource consumption low. 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.",
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Zhang, Y, Meratnia, N & Havinga, PJM 2009, Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks. in Third International Conference on Geosensor Networks., 10.1007/978-3-642-02903-5_4, Lecture Notes in Computer Science, vol. 5659, Springer, Berlin, pp. 31-41. https://doi.org/10.1007/978-3-642-02903-5_4

Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks. / Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

Third International Conference on Geosensor Networks. Berlin : Springer, 2009. p. 31-41 10.1007/978-3-642-02903-5_4 (Lecture Notes in Computer Science; Vol. 5659).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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T1 - Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks

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AU - Havinga, Paul J.M.

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N2 - Recently, wireless sensor networks providing fine-grained spatio-temporal 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 re- liable 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 resource consumption low. 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.

AB - Recently, wireless sensor networks providing fine-grained spatio-temporal 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 re- liable 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 resource consumption low. 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.

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Zhang Y, Meratnia N, Havinga PJM. Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks. In Third International Conference on Geosensor Networks. Berlin: Springer. 2009. p. 31-41. 10.1007/978-3-642-02903-5_4. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-02903-5_4