A role-based architecture for self-adaptive cyber-physical systems

Today's computing world features a growing number of cyber-physical systems that require the cooperation of many physical devices. Examples include autonomous cars and co-working robots, which are expected to appropriately adapt to any possible context they find themselves in (e.g. the presence of a nearby human).

However, the controlling software continues to be developed using established object-oriented modelling techniques like UML, which do not natively possess a notion of context and thus may introduce accidental complexity. With increasing complexity, the probability of the introduction of software errors rises, which can have fatal consequences in cyber-physical systems. To address this, we envision a model-driven architecture for self-adaptive cyber-physical systems that explicitly models structured context. Entities are modelled as message-passing parallel processes and can play roles in specific contexts, which dynamically alter their behaviour and relationships with other parts of the system. Since the planning of complex adaptations can be cumbersome in real-world scenarios, we envision an intuitive formulation of adaptations as graph rewriting rules on the context model.

This paper discusses the current state of research and identifies open research challenges. Based on this, the envisioned architecture as well as an evaluation strategy are presented.