Interconnected Resource-aware Intelligent Systems

We at the Interconnected Resource-aware Intelligent Systems cluster address (distributed embedded) systems performance challenges in terms of timing behavior, dependability, programmability, reliability, robustness, scalability, accuracy, energy and data computation efficiency, and trustworthiness.

Science and technology have changed the way we live, communicate, work, and even spend our free time. We are surrounded by systems of varying sizes and forms that sense, communicate, and try to make sense of what happens, with, within, and around us or where they operate (e.g. in a car or at a factory). The goal of all these systems is to create a safer, healthier, more pleasant, and more productive environment and set of services.

Rapid advancement of science and technology has created a situation, in which ‘living in isolation’ is unimaginable. The same also applies to systems. It is rare, if possible at all, to find any system that is not, one way or the other, connected and work and collaborate together to achieve a single goal. There are also many other situations that are becoming unimaginable or unacceptable to say the least. Systems that fail, are unreliable, inaccurate, or not secure. But as the scale, complexity, heterogeneity, and extent of usage of these systems increase, so do the performance challenges of these systems.

We at the Interconnected Resource-aware Intelligent Systems cluster address (distributed embedded) systems performance challenges in terms of timing behavior, dependability, programmability, reliability, robustness, scalability, accuracy, energy and data computation efficiency, and trustworthiness.

The industrial applications and collaborations inspire us in our research and ensure that our research outcome remains as close as possible to real-world problems and has economic and societal impacts.

Our research is being performed in the context of various application domains including automotive, smart industry, predictive maintenance, logistics, smart cities, health and wellbeing, and intelligent lighting.

The following three pillars, and their interaction, form the basis of IRIS research: 

• Pervasive networking: Edge networking, (IoT) communication and interoperability, (IoT) Network resource management 

• Pervasive computing: Edge computing and federated learning, Edge-driven data analytics and machine learning, Embedded AI, Explainable AI, Data flow computing

• Predictable performance: Time-sensitive networks, Realtime distributed systems, Resource management and scheduling, Formal modeling, analysis & verification, Model-based engineering of CPS