Predictive modeling of the spatiotemporal evolution of an environmental hazard and its sensor network implementation

Predicting accurately the spatiotemporal evolution of a diffusive environmental hazard is of paramount importance for its effective containment. We approximate the front line of a hazard with a set of line segments (local front models). We model the progression characteristics of these front segments by appropriately modified 2D Gaussian functions. The modified Gaussian model parameters are adjusted based on the solution of a Kullback-Leibler (KL) divergence minimization problem. The whole scheme can be realized by a wireless sensor network by forming dynamically triplets of cooperating sensor nodes along the path of the hazard. It is shown that the algorithm can track effectively the front characteristics (in terms of direction and speed) even in the presence of faulty sensor nodes.