A machine learning technique in a multi-agent framework for online outliers detection in Wireless Sensor Networks

H. Martins, F. Januário, L. Palma, A. Cardoso, Paulo José Carrilho de Sousa Gil

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

10 Citations (Scopus)

Abstract

Wireless Sensor Networks enable flexibility, low operational and maintenance costs, as well as scalability in a variety of scenarios. However, in the context of industrial monitoring scenarios the use of Wireless Sensor Networks can compromise the system's performance due to several factors, being one of them the presence of outliers in raw data. In order to improve the overall system's resilience, this paper proposes a distributed hierarchical multi-agent architecture where each agent is responsible for a specific task. This paper deals with online detection and accommodation of outliers in non-stationary time-series by appealing to a machine learning technique. The methodology is based on a Least Squares Support Vector Machine along with a sliding window-based learning algorithm. A modification to this method is considered to improve its performance in transient raw data collected from transmitters over a Wireless Sensor Networks (WSNs). An empirical study based on laboratory test-bed show the feasibility and relevance of incorporating the proposed methodology in the context of monitoring systems over Wireless Sensor Networks.
Original languageEnglish
Title of host publicationIndustrial Electronics Society, IECON 2015 - 41st Annual Conference of the IEEE
Pages688-693
Number of pages6
DOIs
Publication statusPublished - 1 Nov 2015
Event41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015 - Yokohama, Japan
Duration: 9 Nov 201512 Nov 2015

Conference

Conference41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015
CountryJapan
CityYokohama
Period9/11/1512/11/15

Keywords

  • Context, data acquisition, distributed hierarchical multiagent architecture, industrial monitoring, Kernel, learning (artificial intelligence), least squares approximations, least square support vector machine, machine learning technique, Memory, monitoring, multiagent framework, Multi-agent systems, nonstationary time-series, online outlier detection, sliding window-based learning algorithm, support vector machines, Symmetric matrices, time series, transmitter, transmitters, wireless sensor network, wireless sensor networks, WSN
  • Automation & Control Systems
  • Engineering, Electrical & Electronic

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  • Cite this

    Martins, H., Januário, F., Palma, L., Cardoso, A., & Gil, P. J. C. D. S. (2015). A machine learning technique in a multi-agent framework for online outliers detection in Wireless Sensor Networks. In Industrial Electronics Society, IECON 2015 - 41st Annual Conference of the IEEE (pp. 688-693) https://doi.org/10.1109/IECON.2015.7392180