A Kernel-Based Localization Approach in Wireless Sensor Networks

Our solution builds on a kernel-based method called the support vector machine (SVM) for determining the locations of the nodes. The basic SVM algorithm contains two steps: (1) one-region classification using the SVM; and (2) multi-region localization which is a repeated application of one-region classification for a number of different regions. In this paper, we first analyze the error effects of the choice of regions in the multi-region localization, which influences the accuracy of the localization results significantly. The realization of a choice of regions is posed as a beacon node coverage problem, i.e., the spatial distribution of the beacon nodes is determined from the coverage point of view. Second, we develop a method to arrange the regions, which we call expanded coverage region distribution, in order to avoid the problem of border effects in existing solutions. We show that expanded cover region distribution can reduce the localization errors. Our results show that, by optimally choosing and arranging the regions based on our analysis, we can significantly enhance the performance of SVM based localization. Furthermore, the optimal choice of regions to avoid the border effects can be similarly applied in other kernel-based learning methods for localization.