eng URI http://hdl.handle.net/10795/2977 μαθηματικά δίκτυο πληροφορικής 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. 4 pp. LOMv1.0 creator LOMv1.0 publisher LOMv1.0 Scientific Coordinator LOMv1.0 Project Executing Organisation 2011-05 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Environmental hazard Predictive modeling WSNs Kullback-Leibler divergence http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5946729&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5946729 401039 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/2977/2/2977_1.70_%ce%91%ce%9d_22_5_11.pdf URI http://hdl.handle.net/10795/2977 LOMv1.0creator 2016-04-27T11:53:11Z LOMv1.0validator 2016-04-27T11:53:11Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")