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Anomaly Detection in Environmental Monitoring Networks [Application Notes]

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6 Author(s)
Bezdek, J.C. ; Univ. of Melbourne, Melbourne, VIC, Australia ; Rajasegarar, S. ; Moshtaghi, M. ; Leckie, C.
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We apply a recently developed model for anomaly detection to sensor data collected from a single node in the Heron Island wireless sensor network, which in turn is part of the Great Barrier Reef Ocean Observation System. The collection period spanned six hours each day from February 21 to March 22, 2009. Cyclone Hamish occurred on March 9, 2009, roughly in the middle of the collection period. Our system converts sensor measurements to elliptical summaries. Then a dissimilarity image of the data is built from a measure of focal distance between pairs of ellipses. Dark blocks along the diagonal of the image suggest clusters of ellipses. Finally, the single linkage algorithm extracts clusters from the dissimilarity data. We illustrate the model with three two-dimensional subsets of the three dimensional measurements of (air) pressure, temperature and humidity. Our examples show that iVAT images of focal distance are a reliable basis for estimating cluster structures in sets of ellipses, and that single linkage can successfully extract the indicated clusters. In particular, we are able to clearly isolate the cyclone Hamish event with this method, which demonstrates the ability of our model to detect anomalies in environmental monitoring networks.

Published in:

Computational Intelligence Magazine, IEEE  (Volume:6 ,  Issue: 2 )

Date of Publication:

May 2011

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