Distributed Localization Algorithm in Wireless Sensor Network

Distributed learning-based localization algorithm for wireless sensor networks is studied in the paper. We partition the network into a number of partitions based on the sensor´s locations and determine which class each sensor falls into. We consider a network with a number of beacon nodes that have perfect knowledge of its own coordinates and utilize their knowledge as training data to perform the above classification. In this work, we propose the hop-count method for distributed learning based on the different features that are used to determine the class of each node. This method is compared under different system parameters.