  This paper proposes a Bayesian modeling approach to address the problem of
online fault-tolerant dynamic event region detection in wireless sensor
networks. In our model every network node is associated with a virtual
community and a trust index, which quantitatively measures the trustworthiness
of this node in its community. If a sensor node's trust value is smaller than a
threshold, it suggests that this node encounters a fault and thus its sensor
reading can not be trusted at this moment. This concept of sensor node trust
discriminates our model with the other alternatives, e.g.,the Markov random
fields. The practical issues, including spatiotemporal correlations of neighbor
nodes' sensor readings, the presence of sensor faults and the requirement of
online processing are linked together by the concept trust and are all taken
into account in the modeling stage. Based on the proposed model, the trust
value of each node is updated online by a particle filter algorithm upon the
arrival of new observations. The decision on whether a node is located in the
event region is made based upon the current estimate of this node's trust
value. Experimental results demonstrate that the proposed solution can provide
striking better performance than existent methods in terms of error rate in
detecting the event region.
