Event Prediction and Modeling of Variable Rate Sampled Data Using Dynamic Bayesian Networks

Event detection is an important issue in sensor networks for a variety of real-world applications. Many events in real world are often correlated on a complex spatio-temporal level whereby they are manifested via observations over time and space proximities. In order to predict events in these spatiotemporal observations, the prediction model should be capable of modeling codependencies between data observed at various locations. In this paper, we propose a Dynamic Bayesian Network (DBN) with such spatio-temporal event prediction capability in sensor networks deployed for sensing environmental data. More specifically, we develop a DBN model with mixture distribution and a novel learning algorithm, for water level data prediction for different canals, using rainfall data at multiple locations. Experiments on real data demonstrates that our model and training method can provide accurate event prediction in real time for spatio-temporal sensor networks.