IoT systems deployed for predictive maintenance, asset management or virtual power plants collect and process large amounts of data locally. This data is used by “prescriptive” models implemented in micro data centers at the edge of the network. These models are adapted to changing local conditions, maintained with the least possible interference from operators and optimized for clear objectives agreed geographically or per sector.
We research the methods and tools for developing AI-based solutins on edge-based software platforms. The resulting applications must be deployed to collect and process large amounts of data locally. The key research problem is to devise solutions that scale to the entire fleets of equipment with accurate AI predictions.