Splitting method for spatio-temporal sensors deployment in underwater systems

Mathieu Chouchane (1) , Sébastien Paris (1) , François Le Gland (2) , Mustapha Ouladsine (1)

1 LSIS - Laboratoire des Sciences de l'Information et des Systèmes
2 ASPI - Applications of interacting particle systems to statistics

In this paper, we present a novel stochastic optimization algorithm based on the rare events simulation framework for sensors deployment in underwater systems. More precisely, we focus on finding the best spatio-temporal deployment of a set of sensors in order to maximize the detection probability of an intelligent and randomly moving target in an area under surveillance. Based on generalized splitting technique with a dedicated Gibbs sampler, our approach does not require any state-space discretization and rely on the evolutionary framework.

Mots clés: Gibbs sampler; Genetic algorithm; Generalized splitting; Evolutionary algorithm; Stochastic optimization

Domaines: Probabilités [math.PR]

Mathieu Chouchane, Sébastien Paris, François Le Gland, Mustapha Ouladsine. Splitting method for spatio-temporal sensors deployment in underwater systems. Jin-Kao Hao; Martin Middendorf. Proceedings of the 12th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2012), Málaga 2012, 7245, Springer, pp.243-254, 2012, Lecture Notes in Computer Science, 978-3-642-29123-4. ⟨10.1007/978-3-642-29124-1_21⟩. ⟨hal-00744742⟩