Self-localization algorithm for sensor networks using SVM classification region
基于SVM分类区域的传感器网络节点自定位算法

Focused on the requirements of low cost and low power in Wireless Sensor Network (WSN), this paper proposed a range-free localization algorithm based on Support Vector Machine (SVM) classification regions. First, SVM constructed a binary decision tree classifier via learning of the training data. Then the classifier determined the certain classification region where the unknown nodes were located. Finally, the study used the region's center point as the estimated position of the unknown node. The proposed algorithm required mere connectivity information (i.e., hop counts only), so as to reduce the network cost and communication load. The simulation results show that this algorithm alleviates the coverage holes and border problem significantly while certain location accuracy is assured.