Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine

Research output: Contribution to journalArticleAcademicpeer-review

56 Citations (Scopus)

Abstract

Low quality sensor data limits WSN capabilities for providing reliable real-time situation awareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of communication, computational and memory complexity. In this paper, we take into account the correlation between sensor data attributes and propose two distributed and online outlier detection techniques based on a hyperellipsoidal one-class support vector machine (SVM). We also take advantage of the theory of spatio-temporal correlation to identify outliers and update the ellipsoidal SVM-based model representing the changed normal behavior of sensor data for further outlier identification. Simulation results show that our adaptive ellipsoidal SVM-based outlier detection technique achieves better detection accuracy and lower false alarm as compared to existing SVM-based techniques designed for WSNs.
Original languageUndefined
Pages (from-to)1062-1074
Number of pages13
JournalAd hoc networks
Volume11
Issue number3
DOIs
Publication statusPublished - May 2013

Keywords

  • EWI-23088
  • METIS-296308
  • Ellipsoidal support vector machine
  • Wireless Sensor Networks
  • IR-84331
  • Spatial correlation
  • Temporal correlation
  • METIS-296769
  • Outlier Detection

Cite this

@article{06869da1a1c24fa7bcedf49efdf04d3f,
title = "Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine",
abstract = "Low quality sensor data limits WSN capabilities for providing reliable real-time situation awareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of communication, computational and memory complexity. In this paper, we take into account the correlation between sensor data attributes and propose two distributed and online outlier detection techniques based on a hyperellipsoidal one-class support vector machine (SVM). We also take advantage of the theory of spatio-temporal correlation to identify outliers and update the ellipsoidal SVM-based model representing the changed normal behavior of sensor data for further outlier identification. Simulation results show that our adaptive ellipsoidal SVM-based outlier detection technique achieves better detection accuracy and lower false alarm as compared to existing SVM-based techniques designed for WSNs.",
keywords = "EWI-23088, METIS-296308, Ellipsoidal support vector machine, Wireless Sensor Networks, IR-84331, Spatial correlation, Temporal correlation, METIS-296769, Outlier Detection",
author = "Y. Zhang and Nirvana Meratnia and Havinga, {Paul J.M.}",
year = "2013",
month = "5",
doi = "10.1016/j.adhoc.2012.11.001",
language = "Undefined",
volume = "11",
pages = "1062--1074",
journal = "Ad hoc networks",
issn = "1570-8705",
publisher = "Elsevier",
number = "3",

}

Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine. / Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

In: Ad hoc networks, Vol. 11, No. 3, 05.2013, p. 1062-1074.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine

AU - Zhang, Y.

AU - Meratnia, Nirvana

AU - Havinga, Paul J.M.

PY - 2013/5

Y1 - 2013/5

N2 - Low quality sensor data limits WSN capabilities for providing reliable real-time situation awareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of communication, computational and memory complexity. In this paper, we take into account the correlation between sensor data attributes and propose two distributed and online outlier detection techniques based on a hyperellipsoidal one-class support vector machine (SVM). We also take advantage of the theory of spatio-temporal correlation to identify outliers and update the ellipsoidal SVM-based model representing the changed normal behavior of sensor data for further outlier identification. Simulation results show that our adaptive ellipsoidal SVM-based outlier detection technique achieves better detection accuracy and lower false alarm as compared to existing SVM-based techniques designed for WSNs.

AB - Low quality sensor data limits WSN capabilities for providing reliable real-time situation awareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of communication, computational and memory complexity. In this paper, we take into account the correlation between sensor data attributes and propose two distributed and online outlier detection techniques based on a hyperellipsoidal one-class support vector machine (SVM). We also take advantage of the theory of spatio-temporal correlation to identify outliers and update the ellipsoidal SVM-based model representing the changed normal behavior of sensor data for further outlier identification. Simulation results show that our adaptive ellipsoidal SVM-based outlier detection technique achieves better detection accuracy and lower false alarm as compared to existing SVM-based techniques designed for WSNs.

KW - EWI-23088

KW - METIS-296308

KW - Ellipsoidal support vector machine

KW - Wireless Sensor Networks

KW - IR-84331

KW - Spatial correlation

KW - Temporal correlation

KW - METIS-296769

KW - Outlier Detection

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