Wireless sensor networks fuse data from a multiplicity of sensors of different modalities and spatiotemporal scales to provide information for reconnaissance, surveillance, and situational awareness in many defense applications. For decisions to be based on information returned by sensor networks it is crucial that such information be of sustained high quality. While the Quality of Information (QoI) depends on many factors, perhaps the most crucial is the integrity of the sensor data sources themselves. Even ignoring malicious subversion, sensor data quality may be compromised by non-malicious causes such as noise, drifts, calibration, and faults. On-line detection and isolation of such misbehaviors is crucial not only for assuring QoI delivered to the end-user, but also for efficient operation and management by avoiding wasted energy and bandwidth in carrying poor quality data and enabling timely repair of sensors. We describe a two-tiered system for on-line detection of sensor faults. A local tier running at resource-constrained nodes uses an embedded model of the physical world together with a hypothesis-testing detector to identify potential faults in sensor measurements and notifies a global tier. In turn, the global tier uses these notifications on the one hand during fusion for more robust estimation of physical world events of interest to the user, and on the other hand for consistency checking among notifications from various sensors and generating feedback to update the embedded physical world model at the local nodes. Our system eliminates the undesirable attributes of purely centralized and purely distributed approaches that respectively suffer from high resource consumption from sending all data to a sink, and high false alarms due to lack of global knowledge. We demonstrate the performance of our system on diverse real-life sensor faults by using a modeling framework that permits injection of sensor faults to study their impact on the application QoI.
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