A Machine Learning Technique in a Multi-Agent Framework for Online Outliers Detection in Wireless Sensor Networks



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.

Related Project

iCIS - Intelligent Computing in the Internet of Services


41st Annual Conference of the IEEE Industrial Electronics Society, November 2015

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