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 adapt to changing local conditions, are maintained with as little interference from operators as possible, and are optimized for clear objectives agreed upon per region or per sector.
We research methods and tools for developing AI-based solutions 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 entire fleets of equipment while providing accurate AI predictions.