A machine learning approach for user localization exploiting connectivity data

The growing popularity of Location-Based Services (LBSs) has boosted research on cheaper and more pervasive localization systems, typically relying on such monitoring equipment as Wireless Sensor Networks (WSNs), which allow to re-use the same instrumentation both for monitoring and for localization without requiring lengthy off-line training. This work addresses the localization problem, exploiting knowledge acquired in sample environments, and extensible to areas not considered in advance. Localization is turned into a learning problem, solved by a statistical algorithm. Additionally, parameter tuning is fully automated thanks to its formulation as an optimization problem based only on connectivity information. Performance of our approach has been thoroughly assessed based on data collected in simulation as well as in actual deployment.