A Weighted Kernel-based Hierarchical Classification Method for Zoning of Sensors in Indoor Wireless Networks

This paper presents a solution for localization of sensors by zoning, in indoor wireless networks. The problem is tackled by a classification technique, where the objective is to classify the zone of the mobile sensor for any observation. The method is hierarchical and uses the belief functions theory to assign confidence levels for zones. For this purpose, kernel density estimation is used first to model the features observations. The algorithm then uses hierarchical clustering and similarity divergence, creating a two-level hierarchy, to reduce the number of zones to be classified at a time. At each level of the hierarchy, a feature selection technique is carried to optimize the misclassification rate and feature redundancy. Experiments are realized in a wireless sensor network to evaluate the performance of the proposed method.