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Distributed target tracking and boundary estimation in wireless sensor networks

We study the problem of target tracking and boundary detection of a substance diffusing from a mobile source using a wireless sensor network. We present a Prediction-based Mobility Adaptive Tracking (P-MAT algorithm to study the tradeoff among energy, accuracy of tracking, coverage and boundary estimation. P-MAT minimises overall energy consumption by incorporating adaptivity in two forms: (1) the size of the active region and (2) modulation of the sampling rate. It uses adaptive Kalman filtering to predict the target's future location and velocity. The predicted target location determines a set of sensors surrounding that location to be activated known as the active region. Sensors in the active region are responsible for target tracking and boundary detection. In this article, we include dynamic boundary estimation. Boundary estimation in many situations can be performed efficiently using a subset of nodes within the vicinity of the phenomenon. This subset of nodes in our algorithm is the set of nodes in the active region. As the substance spreads, sensors in the active region determine if additional sensors outside of the active region are needed to enclose the boundary. Results from simulation experiments show that P-MAT can perform both tracking and boundary.