This paper introduces an agent-based approach that semantically integrates the Building Information Model (BIM), Geographical Information System (GIS), and the Environmental Data Agent (EDA)-based optimization interface between Information and Operational Technology (IT/OT) for more trusted and resilient data centers. Using the cybersecurity-aware BIM-GIS-IoT data model facilitates the exchange of requirements and forecasts to optimize energy use, environmental impact, availability, and costs in data centers. At the core of this solution, the EDA securely mediates data exchange between IT and OT, translating IT resource consumption into energy metrics for effective optimization.

The OPtimization of Electric Vehicle Autonomy (OPEVA) project enhances data aggregation for Electric Vehicles (EVs) by collecting critical real-time data (i.e., vehicle performance, battery health, charging behaviours) through heterogeneous data acquisition devices built on robust HW and integrated with Internet of Things (IoT) protocols. By combining internal sensor data and driver-specific behaviours with external information (e.g., road conditions, charging station availability), OPEVA maximizes vehicles performance, establishing secure and seamless data communication between EVs and the infrastructure, and using IoT and cloud computing tools alongside Vehicle-to-Everything (V2X) devices and networks. This paper focuses on the extensible data model ensuring semantic data integrity considering in- and out-vehicle factors, presenting data acquisition solutions dealing with OPEVA's semantic data model and their use in various Artificial Intelligence (AI)-powered use cases (e.g., range prediction, route optimization, battery management).

Manufacturing is undergoing an immense change triggered with widespread sensorisation, volumes of data being generated, and advanced machine learning technologies. Problems once solvable via simpler approaches considering more monolithic paradigms have evolved to become larger systems (Cyber Physical Systems; CPS) and Systems of Systems. The scaling, manageability, security, data handling requirements of such systems, as well as the industry’s common goal to reusability have led to several outcomes at the broader European level, Arrowhead and Mimosa being two of those so far. In this study, we consider an Industry 4.0 "Predictive Maintenance" problem. Instead of a rushing with straight data analysis approach as defined under CRISP-DM, we first delve into creating a more widely consumable and reusable set of building blocks by implementing an Arrowhead and Mimosa framework, which together form the route to the machine learning steps that finally lead to the solution.