A new kernelized approach to wireless sensor network localization

In this paper, a new approach to range-free localization in Wireless Sensor Networks (WSNs) is proposed using nonlinear mapping, and the kernel function is introduced. The localization problem in the WSN is formulated as a kernelized regression problem, which is solved by support vector regression (SVR) and multi-dimensional support vector regression (MSVR). The proposed methods are simple and efficient in that no additional hardware is required for the measurements, and only proximity information and position information of the anchor nodes are used for the localization. The proposed methods are composed of three steps: the measurement step, kernelized regression step, and localization step. In the measurement step, the proximity information of the given network is measured. In the regression step, the relationships among the geographical distances and the proximity among sensor nodes is built using kernelized regression. In the localization step, each sensor node finds its own position in a distributed manner using a kernelized regressor. The simulation result demonstrates that the proposed methods exhibit excellent and robust location estimation performance.