Frequent Itemset Based Event Detection in Uncertain Sensor Networks

More and more sensor networks are deployed for the detection of events. Yet due to the resource-constraint nature of nodes, the readings are inherently inaccurate, imprecise and are distributed among the nodes, so it is a challenging task to detect events in such a kind of networks. In this paper, we study the problem of uncertain event detection in sensor networks, and propose an efficient detection algorithm Fibed. We use a possible world semantics to interpret the uncertain data, and events are defined based on computing the frequent item sets. A polynomial is constructed to calculate the probability of each frequent item, and the coefficient vector of the polynomial is merged and updated when it is routed towards the base station. Early decisions could be made for the events, and lots of items could be pruned to save unnecessary transmissions as their probability do not meet the probability threshold. Experimental studies show that Fibed is efficient in detecting the uncertain events and cutting down the incurred transmissions.