Dictionary Learning for data compression within a Digital Twin Framework

Digital Twin system plays a crucial role in several contexts, from smart agriculture to predictive maintenance, from healthcare to weather modelling. To be effective, it involves a continuous exchange of massive data between IoT sensors on real world and digital system hosted on HPC and vice versa. Nevertheless, the transmitted signals often exhibit high similarity, resulting in a redundant dataset very suitable for compression. This paper shows how Dictionary Learning can be used as a preprocessing technique for AI algorithms due to its ability to compress large data volumes up to 80% with a potential enhancement of the performances acting both as a denoising and compression technique. This algorithm operates efficiently on various types of datasets, from images to timeseries, and is well-suited for deployment on devices with limited computational resources, like IoT sensors.