Artificial Intelligence Review

, Volume 52, Issue 4, pp 2411–2436 | Cite as

Outliers detection methods in wireless sensor networks

  • Paulo GilEmail author
  • Hugo Martins
  • Fábio Januário


Detection and accommodation of outliers are crucial in a number of contexts, in which collected data from a given environment is subsequently used for assessing its running conditions or for data-based decision-making. Although a significant number of studies on this subject can be found in literature, a comprehensive empirical assessment in the context of local online detection in wireless sensor networks is still missing. The present work aims at filling this gap by offering an empirical evaluation of two state-of-the-art online detection methods. The first methodology is based on a Least Squares-Support Vector Machine technique, along with a sliding window-based learning algorithm, while the second approach relies on Principal Component Analysis and on the robust orthonormal projection approximation subspace tracking with rank-1 modification. The performance and implementability of these methods are evaluated using a generated non-stationary time-series and a test-bed consisting of a benchmark three-tank system and a wireless sensor network, where deployed algorithms are implemented under a multi-agent framework.


Outliers detection Online implementation Least-Squares Support Vector Machine Gaussian kernel PCA and subspace tracking 


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Faculty of Science and TechnologyUniversidade NOVA de LisboaCaparicaPortugal
  2. 2.CTS-UNINOVA, Universidade NOVA de LisboaCaparicaPortugal
  3. 3.CISUC, University of CoimbraCoimbraPortugal

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