Cyber-Physical Systems (CPS) are complex systems composed of different interacting computing and physical entities that contribute concurrently to determine the behaviour of the system as a whole. Computing layer and physical environment are tightly bound; therefore, such systems need to adapt, prospectively and autonomously, to rapid changes in the environment and in the system itself. By their nature, CPS and System of Systems (CPSoS) usually operate in uncertain environments and should satisfy multiple concurring and, usually, competing requirements regarding affordability, performance, safety, security, sustainability, etc. As a result, the design of CPS and CPSoS becomes inherently difficult, challenging, and time consuming. Promising ways of addressing this can be to follow new development methodologies, such as NIST framework for CPS, and to use model-based design platforms both commercially available or academic. However, despite their big promise (considering the claimed enhancement of and the declared speed-up), the existing model-based frameworks are not as popular as it could be expected. Modelling, maintenance, and interoperability overhead, especially with heterogeneous models over several levels of abstraction, are not addressed in a satisfactory way. CERBERO intends to develop design methodology and address these challenges for a specific aspect of CPS – adaptivity. While deeply studied, there is no standard solution yet for adaptation and reconfiguration. In particular, self-reconfiguration and adaptation have been acknowledged as key features for CPS operators dealing with faults management, but existing design frameworks rarely address them. In order to focus CERBERO effort even more and evaluate the proposed framework and developed tools, CERBERO defined three use cases, targeting development of CPS in very different levels of abstraction and covering a wide spectrum of system features: (i) self-healing robotic arm for Planetary Exploration utilizing adaptability of heterogeneous embedded computing platform to improve computing robustness; (ii) video-sensing unmanned vehicles for Ocean Monitoring utilizing system level adaptivity for cost effective immersive environmental monitoring capabilities on-sea and subsea; and (iii) driver assistant for Smart Travelling of electric vehicles utilizing system of systems (SoS) level adaptivity including interaction with humans in an immersive simulation environment.
