Online Fault-Tolerant Dynamic Event Region Detection In Sensor Networks Via Trust Model And Its Application

As a means of accessing information from the real physical word,Wireless Sensor Networks have been paid attention by more and more people.Event region detection is one of the important applications of Wireless Sensor Networks.In reality,the event region is usually changing,which indicates a dynamic event region.So although the theory on traditional Wireless Sensor Networks models and algorithms is ripe,it shows many limitations in practical applications.Considering that Wireless Sensor Networks are equipped with limited energy and system is susceptible to physical environments,the dynamic event region detection algorithm is required to be of low complexity and fault tolerant to ensure the accuracy of event detection.This thesis 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 so on.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.