Support Vector Classification Strategies for Localization in Sensor Networks

We consider the problem of estimating the geographic locations of nodes in a wireless sensor network where most sensors are without an effective self-positioning functionality. A solution to this localization problem is proposed, which uses support vector machines (SVM) and mere connectivity information only. We investigate two versions of this solution, each employing a different multiclass SVM strategy. They are shown to perform well in various aspects such as localization error, processing efficiency, and effectiveness in addressing the border issue.