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 [1], [11], [5]. 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 [9], [5]. 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.

• Type: Conference
• Year: 2009
• Where: INFOCOM
• Authors: Min Ding, Xiuzhen Cheng
• Keywords: Communications, INFOCOM 2009, Sensor Network, Sensor Readings, Sensors