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Distributed localization in wireless sensor networks as a pre-image problem in a Reproducing Kernel Hilbert Space

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 nodes. Non-anchor nodes positions are estimated by the pre-image of their nonlinear projection onto this manifold. This non-linear strategy provides a great accuracy when data of interest are highly corrupted by noise and when sensors are not able to estimate their Euclidean inter-distances.