Fault Tolerant Event Boundary Detection and Target Tracking in Sensor Networks Open Access
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Detecting event frontline or boundary sensors and tracking dynamically moving targets in a complex sensor network environment are critical problems for sensor network applications. In this paper, we propose novel algorithms of event frontline sensor detection based on statistical mixture methods with model selection. 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. Two classes of Boundary and Non-Boundary sensors can be effectively classified using the model selection techniques for finite mixture models. We further propose its temporally adaptive version for dynamic target tracking in changing environments, under a unified statistical mixture modeling framework. The proposed algorithms can be implemented within each purely localized sensor neighborhood and scale well to large-range sensor networks. The computational complexity is moderate and comparable to our previous Median based approaches. Our extensive experimental results demonstrate that our algorithms effectively detect the event boundary with a high accuracy under moderate noise levels. Desirable quantitative target tracking results are also achieved under challenging background conditions.