We propose a novel anomaly detection method for a heterogeneous sensor environment in a living space where it is hard to analyze the entire mechanism of the environment and we are unlikely to predict irregular events. By using Bayesian networks and nonparametric regression, our method learns the ordinary behaviors of sensor values and examines the degree of anomaly for each observation according to the estimated variance from the results of learning.

We applied our method to data collected in an office room equipped with brightness and motion sensors and obtained the plausible sensor relation networks with no or little prior knowledge. We detected some symptoms of anomalous events and determined the causal sensors by using the network structure.