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Mote-based Online Anomaly Detection using Echo State Networks

Summary: Mote-based Online Anomaly Detection using
Echo State Networks
Marcus Chang1
, Andreas Terzis2
, and Philippe Bonnet1
Dept. of Computer Science, University of Copenhagen, Copenhagen, Denmark
Dept. of Computer Science, Johns Hopkins University, Baltimore MD, USA
Abstract. Sensor networks deployed for scientific data acquisition must
inspect measurements for faults and events of interest. Doing so is cru-
cial to ensure the relevance and correctness of the collected data. In this
work we unify fault and event detection under a general anomaly de-
tection framework. We use machine learning techniques to classify mea-
surements that resemble a training set as normal and measurements that
significantly deviate from that set as anomalies. Furthermore, we aim at
an anomaly detection framework that can be implemented on motes,
thereby allowing them to continue collecting scientifically-relevant data
even in the absence of network connectivity. The general consensus thus
far has been that learning-based techniques are too resource intensive to


Source: Amir, Yair - Department of Computer Science, Johns Hopkins University


Collections: Computer Technologies and Information Sciences