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In the project, we develop a reference model specifically geared towards AI design and application in order to structure interrelationships and dependencies and make them transparent. We develop and test criteria and metrics to optimize AI systems along their life cycle with respect to their resource and energy requirements. The project also aims to quantify AI processes in terms of resource consumption, at least roughly, and to develop measurement methods for this. To this end, we analyze and compare existing methods and procedures. In addition, we want to systematically include components such as sensors, actuators, energy harvesting components, etc. in the AI reference model. Based on an intralogistics case study and other openly available case studies, we evaluate and disseminate the model.

The main goal of the research training group is to extend classical cyber-physical systems in such a way that they enable an adaptive integration of complex processes. Due to the large heterogeneity of IoT devices, the diversity of processes and the environmental factors acting on them, as well as the dynamic usage and interaction contexts, high complexity and dynamics arise. The systems must therefore be designed to be self-learning and adaptive, so that the data generated during their use can be continuously used to improve themselves (processes, topologies, resource requirements, etc.). This is enabled using AI and machine learning methods, but also considers the interaction between actors ("human-in-the-loop"), systems and processes.

The project "EASY" focuses on energy-efficient analysis and control processes in the dynamic edge cloud continuum for industrial manufacturing. For organizing the manufacturing processes according to the guiding principles of "Industry 4.0", edge computing enables the data-sovereign, near-real-time processing of data directly at the point of origin. The significant reduction in latency times associated with edge computing will drive the use of industrial analytics, control, and AI applications close to production. This promises to increase productivity and resource efficiency for the entire manufacturing process. The goal of the EASY project is to bring about these added values by creating an easily usable edge cloud continuum that provides a runtime environment and services for the execution of AI-based analytics and control processes. Within this continuum, dynamic distributed execution of services optimized in terms of energy consumption, data usage and transmission is enabled in an automated manner across the spectrum from centralized cloud to decentralized edge instances.

The research training group Intelligent Technologies for Sustainable Development is part of the "House of Professors: Spirit of Innovation, Joy & Diversity" at Trier University of Applied Sciences. The goal of House of Professors is to implement innovative recruitment and qualification paths for young researchers in order to shape the generation change at the university. To this end, new job models such as tandem-, focus-, and family-professorship will be tested in practice. The qualification of the next generation of academics is at the forefront of the establishment of the research training group. Young- and international researcher sabbaticals provide new access to Trier University of Applied Sciences.