SSA@EDAL – Software Sensor Augmentation at Environmental Data Analysis Laboratory

The project analyses the use of machine learning, with a particular focus on deep learning, for the abstraction of sensor data that measure the state of complex distributed processes and networks. The discovered knowledge is used for short-term and long-term forecasting of network behaviour and for completion of measurement data when individual sensors fail. Case studies include measurements of Adriatic Sea currents and waves and traffic flow measurements from the city of Skopje, while the consortium includes the Faculty of Science of the University of Split as applicant, the Faculty of Transport and Traffic Sciences of the University of Zagreb, and the Faculty of Technical Sciences at St. Kliment Ohridski University in North Macedonia.