Robust Event Boundary Detection in Sensor Networks - A Mixture Model Based Approach

Detecting event frontline or boundary sensors in a complex sensor network environment is one of the critical problems for sensor network applications. In this paper, we propose a novel algorithm for event frontline sensor detection based on statistical mixture methods with model selection (Akaike, 1973). A boundary sensor is considered as being associated with a multimodal local neighborhood of (univariate or multivariate) sensing readings, and each non-boundary sensor is treated as being with a unimodal sensor reading neighborhood. Furthermore, the set of sensor readings within each sensor´s spatial neighborhood is formulated using Gaussian mixture model (McLachlan and Peel, 2000). Two classes of boundary and non-boundary sensors can be effectively classified using the model selection techniques for finite mixture models. Our extensive experimental results demonstrate that our algorithm effectively detects the event boundary with a high accuracy under moderate noise levels.