Abstract-Cyber-physical systems (CPS) are systems of cooperating autonomous components which closely interact with and control the physical environment. Being distributed and typically based on periodic activities, CPS have to cope with the problem that data capturing a distributed state of the system and its environment are inherently inaccurate (they represent belief on the state). In particular, this poses a problem when dependability is being pursued. In this paper we address this issue by modeling belief at the architecture level. In particular, we enhance the architecture by models describing belief inaccuracy over time. We exploit these models to quantify at runtime the impact of belief staleness on its inaccuracy. We then use this quantification to drive architectural adaptation with the aim to increase dependability of the running CPS system.

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Distributed systems have continued to evolve and we note two important trends: the dramatically increasing level of dynamism in contemporary distributed systems and the convergence of mobile computing with cloud computing. The end result is that it is very difficult to achieve the required level of scalability and dependability in a systematic way when considering pervasive systems that are software-and compute-intensive and whose functionality is typically augmented by static cloud infrastructure resources. This work discusses relevant challenges and requirements for integrating cloud computing with pervasive systems operating in dynamic environments. We present a set of requirements using a holistic case study and describe a reference architecture to address these requirements.

In this work we tackle the problem of designing and developing software-intensive cyber-physical systems (CPS), which are large distributed systems of collaborating elements that closely interact with the physical world, such as intelligent transportation systems and crowdsourcing applications. Due to their specific constraints, such as extreme dynamism and continuous evolution of the physical substratum, and requirements, such us openendedness and adaptability, CPS introduce many new challenges for software engineering. In response, we present a tailored ecosystem of software engineering models, methods, and tools. This ecosystem is centered on the DEECo component model, which we have proposed specifically for architecting softwareintensive CPS.

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