eng URI http://hdl.handle.net/10795/2981 μαθηματικά δίκτυο πληροφορικής ηλεκτρονικός υπολογιστής We present a novel decentralized Wireless Sensor Network (WSN) algorithm which can estimate both the speed and direction of an evolving diffusive hazardous phenomenon (e.g. a wildfire, oil spill, etc.). In the proposed scheme we approximate a progressing hazard’s front as a set of line segments. The spatiotemporal evolution of each line segment is modeled by a modified 2D Gaussian function. As the phenomenon evolves, the parameters of this model are updated based on the analytical solution of a Kullback – Leibler (KL) divergence minimization problem. This leads to an efficient WSN distributed parameters estimation algorithm that can be implemented by dynamically formed clusters (triplets) of collaborating sensor nodes. Computer simulations show that our approach is able to track the evolving phenomenon with reasonable accuracy even if a percentage of sensors fails due to the hazard and/or the phenomenon has a time varying speed. 7 pp. LOMv1.0 creator LOMv1.0 publisher LOMv1.0 Scientific Coordinator LOMv1.0 Project Executing Organisation 2011-10 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Predictive modeling Environmental hazard WSN Spatio-temporal evolution Kullback-Leibler divergence http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6084201&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6084201 1004559 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/2981/2/2981_1.70_%ce%91%ce%9d_9_10_11.pdf URI http://hdl.handle.net/10795/2981 LOMv1.0creator 2016-04-27T12:24:48Z LOMv1.0validator 2016-04-27T12:24:48Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")