Distributed Learning for Localization in ...

The problem of distributed or decentralized detection and estimation in application such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made aout a statistical description of nature. So the distributed learning method is borrowed to solve the localization problem. It assumes that a network with a number of beacon nodes that have perfect knowledge of its own coordinates and utilizes their knowledge as training data to perform the above classification. In this thesis, three approaches for distributed learning based on the different features that is used to determine the class of each node have been proposed, namely, the hop-count (HC) method, the density-aware hop-count length (DHL) method, and the distance vector (DV) method. These methods are compared under different system parameters and also compared with the triangulation method that is often employed in the literature. The simulation results show that the localization methods based on the distributed learning is more accurate and effectual.