Data anomaly detection in wireless sensor networks with application to an oil refinery

P. Gil, H. Martins, F. Januário, Amâncio Santos, L. Palma, A. Cardoso, J. Henriques

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a study concerning the online detection of outliers in non-stationary time-series collected over Wireless Sensor Networks on scenario implemented at an oil refinery. Two different approaches are under assessment. One based on the Least Squares Support Vector Machine and sliding window learning and standard Gaussian kernel. The other consists in a modification to the standard Gaussian kernel in order to improve the detection performance for non-stationary data streams. The implementability and effectiveness of these methods are evaluate on an oil refinery test-bed over a Wireless Sensor Network.

Original languageEnglish
Title of host publication13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings
EditorsCesar Teixeira, Jorge Henriques, Paulo Gil, Alberto Cardoso
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages425-429
Number of pages5
ISBN (Electronic)9781538653463
DOIs
Publication statusPublished - 29 Oct 2018
Event13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Ponta Delgada, Sao Miguel Island, Azores, Portugal
Duration: 4 Jun 20186 Jun 2018

Conference

Conference13th APCA International Conference on Control and Soft Computing, CONTROLO 2018
CountryPortugal
CityPonta Delgada, Sao Miguel Island, Azores
Period4/06/186/06/18

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  • Cite this

    Gil, P., Martins, H., Januário, F., Santos, A., Palma, L., Cardoso, A., & Henriques, J. (2018). Data anomaly detection in wireless sensor networks with application to an oil refinery. In C. Teixeira, J. Henriques, P. Gil, & A. Cardoso (Eds.), 13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings (pp. 425-429). [8514551] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONTROLO.2018.8514551