Sherlock Is Around: Detecting Network Failures With Local Evidence Fusion
Traditional approaches for wireless sensor network diagnosis are mainly sink-based. They actively collect global evidences from sensor nodes to the sink so as to conduct centralized analysis at the powerful back-end. On the one hand, long distance proactive information retrieval incurs huge transmission overhead; On the other hand, due to the coupling effect between diagnosis component and the application itself, sink often fails to obtain complete and precise evidences from the network, especially for the problematic or critical parts. To avoid large overhead in evidence collection process, self-diagnosis injects fault inference modules into sensor nodes and let them make local decisions.