  In this paper we present an application of techniques from statistical signal
processing to the problem of event detection in wireless sensor networks used
for environmental monitoring. The proposed approach uses the well-established
Principal Component Analysis (PCA) technique to build a compact model of the
observed phenomena that is able to capture daily and seasonal trends in the
collected measurements. We then use the divergence between actual measurements
and model predictions to detect the existence of discrete events within the
collected data streams. Our preliminary results show that this event detection
mechanism is sensitive enough to detect the onset of rain events using the
temperature modality of a wireless sensor network.
