  Environmental scientists have increasingly been deploying wireless sensor
networks to capture valuable data that measures and records precise information
about our environment. One of the major challenges associated with wireless
sensor networks is the quality of the data and more specifically the detection
of segment outliers and unusual events. Most previous research has focused on
detecting outliers that are errors that are caused by unreliable sensors and
sensor nodes. However, there is an urgent need for the development of new tools
capable of identifying, tagging and visualizing erroneous segment outliers and
unusual events from sensor data streams. In this paper, we present a SOUE
Detector (Segment Outlier and Unusual Event-Detector) system for wireless
sensor networks that combines statistical analyses using Dynamic Time Warping
(DTW) with domain expert knowledge (captured via an ontology and semantic
inferencing rules). The resulting Web portal enables scientist to efficiently
search across a collection of wireless sensor data streams and identify,
retrieve and display segment outliers (both erroneous and genuine) within the
data streams. In this paper, we firstly describe the detection algorithms, the
implementation details and the functionality of the SOUE Detector system.
Secondly we evaluate our approach using data that comprises sensor observations
collected from a sensor network deployed in the Springbrook National Park in
Queensland, Australia. The experimental results show that the SOUE-Detector can
efficiently detect segment outliers and unusual events with high levels of
precision and recall.
