In-network detection of anomaly regions in sensor networks with obstacles
In the past couple of years, sensor networks have evolved to a powerful infrastructure component for monitoring and tracking events and phenomena in many application domains. An important task in processing streams of sensor data is the detection of anomalies, e.g., outliers or bursts, and in particular the computation of the location and spatial extent of such anomalies in a sensor network. In this paper, we present an approach that facilitates the efficient computation of such anomaly regions from sensor readings. We propose an algorithm to derive spatial regions from individual anomalous sensor readings, with a particular focus on obstacles present in the sensor network. We improve this approach by proposing a distributed in-network processing technique where the region detection is performed at the sensor nodes. We demonstrate the advantages of this strategy over a centralized processing strategy by utilizing a cost model for real sensors and sensor networks.
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