Distributed deviation detection in sensor networks
ACM SIGMOD Record
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Frequently appeared sensor faults greatly reduce the usability and reliability of sensor networks. Distributed automatous faulty sensor detection is critical for self-managed and sustainable sensor networks. A number of detection methods have been proposed for specific fault types. In this paper, we propose a general approach for detecting arbitrary types of faults. The approach includes a new general measurement mutual divergence for evaluating detecting results in the absence of the ground truth, and a distributed collective detection method that produces probabilistic decision results. For comparison purposes, we also introduce two detection methods in both distributed and centralized manners. We show that mutual divergence correctly measures the average fault amount and the distributed collective method consistently outperforms the other methods with up to 50% higher detection accuracy.