Mobility using first and second derivatives for kernel-based regression in wireless sensor networks

This paper deals with the problem of tracking and monitoring physical phenomena using wireless sensor networks. It proposes an original mobility scheme that aims at improving the tracking process. To this end, a model is defined using kernel-based methods and a learning process. The sensors are given the ability to move in a manner that minimizes the approximation error, and thus improves the efficiency of the model. First and second derivatives of the approximation error are used to define the new positions of the nodes. The performance of the proposed method is illustrated in the context of monitoring gas diffusion with wireless sensor networks.