Location estimation in indoor wireless networks by hierarchical support vector machines with fast learning algorithm

We consider the problem of estimating the physical locations of nodes in an indoor wireless network since knowing the physical locations of the nodes is important to many tasks of a wireless network such as network management, event detection, location-based service, and routing. A hierarchical support vector machines (H-SVM) scheme is proposed with the following advantages. First, H-SVM offers an efficient localization procedure in a distributed manner due to hierarchical structure. Second, H-SVM could determine nodes positions based only on simpler network information, e.g., the hop counts, without require particular ranging hardware. Third, the exact mean and the variance of the estimation error introduced by H-SVM are derived which are seldom addressed in previous works. Thanks for the quicker matrix diagonization technique, our algorithm can reduce the traditional SVM learning complexity from O(n³) to O(n²) where n is the training sample size. Finally, the simulation results verify the validity and effectiveness for the proposed HSVM with parallel learning algorithm.