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Fast, Accurate Event Classification on Resource-Lean Embedded Sensors

Due to the limited computational and energy resources available on existing wireless sensor platforms, achieving high-precision classification of high-level events in-network is a challenge. In this article, we present in-network implementations of a Bayesian classifier and a condensed kd-tree classifier for identifying events of interest on resource-lean embedded sensors. The first approach uses preprocessed sensor readings to derive a multidimensional Bayesian classifier used to classify sensor data in real time. The second introduces an innovative condensed kd-tree to represent preprocessed sensor data and uses a fast nearest-neighbor search to determine the likelihood of class membership for incoming samples. Both classifiers consume limited resources and provide high-precision classification. To evaluate each approach, two case studies are considered, in the contexts of human movement and vehicle navigation, respectively. The classification accuracy is above 85% for both classifiers across the two case studies.