Mehdi Essoloh

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Over the past few years, wireless sensor networks received tremendous attention for monitoring physical phenomena, such as the temperature field in a given region. Applying conventional kernel regression methods for functional learning such as support vector machines is inappropriate for sensor networks, since the order of the resulting model and its(More)
In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking advantage of recent developments on kernel-based machine learning, we consider a new sparsification criterion for online learning. As opposed to previously derived criteria, it is based on the estimated error and is therefore is well suited for tracking the(More)
In this paper, we introduce a distributed strategy for localization in a connected wireless sensor network composed of limited range sensors. Our distributed algorithm is computed through the network and provides sensor position estimation from local connectivity measurements. This work takes advantage of a conditionally and locally convex criterion that is(More)
In this paper, we introduce a distributed strategy for localization in a wireless sensor network composed of limited range sensors. The proposed distributed algorithm provides sensor position estimation from local similarity measurements. Incremental Kernel Principal Component Analysis techniques are used to build the nonlinear manifold linking anchor(More)
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