Efficient Pattern Detection in Extremely Resource-Constrained Devices

We present a novel approach for the on-line detection of complex events in wireless sensor networks. Complex events are sets of data points that correspond to unusual patterns that can not be detected using threshold-based techniques. Our method uses an efficient implementation of SAX, a mature data mining algorithm, that transforms a stream of readings into a symbolic representation. Complex event detection is then performed via four alternative modes: (a.) multiple pattern detection using a suffix array, (b.) distance-based comparison, (c.) unknown pattern detection, and (d.) probabilistic detection. The method allows users to specify complex events as patterns or to search for interesting changes without supplying any information. The appropriateness of the approach has been verified by applying it to four sensor data sets. In addition, we have developed an efficient implementation for the TinyOS operating system, and further validated our assertions by collecting and analyzing data in real-time.