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This paper presents a general fault-tolerant event detection scheme that allows nodes to detect erroneous local decisions by leveraging the local decisions reported by their neighbors. This detection scheme can handle cases where nodes have different accuracy levels. The derived fault-tolerant estimator is proven to be optimal under the maximum a posteriori (MAP) criterion. An equivalent weighted voting scheme is also derived. Further, two new error models are derived to take into account the neighbor distance and the geographical distributions of the two decision quorums. These models are particularly suitable for detection applications where the event under consideration is highly localized. The fault-tolerant estimator is simulated using a network of 1,024 nodes deployed randomly in a square region and assigned random probabilities of failure. Several estimation schemes that allow nodes to learn their error rates continuously are developed. These error rates are used in the distributed estimation schemes to assign appropriate weights to the nodes in the voting scheme.