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Abstract-Recent advances in embedded devices capabilities and wireless networks paved the way for creating ubiquitous Cyber-Physical Systems (CPS) grafted with self-configuring and self-adaptive capabilities. As these systems need to strike a balance between dependability, open-endedness and adaptability, and operate in dynamic and opportunistic environments, their design and development is particularly challenging. We take an architecture-based approach to this problem and advocate the use of component-based abstractions and related machinery to engineer self-adaptive CPS. Our approach is structured around DEECo -a component framework that introduces the concept of component ensembles to deal with the dynamicity of CPS at the middleware level. DEECo provides the architecture abstractions of autonomous components and component ensembles on top of which different adaptation techniques can be deployed. This makes DEECo a vehicle for seamless experiments with selfadaptive systems where the physical distribution and mobility of nodes, and the limited data availability play an important role.

International audienceDesign of self-adaptive software-intensive Cyber-Physical Systems (siCPS) operating in dynamic environments is a significant challenge when a sufficient level of dependability is required. This stems partly from the fact that the concerns of self-adaptivity and dependability are to an extent contradictory. In this paper, we introduce IRM-SA (Invariant Refinement Method for Self-Adaptation) – a design method and associated formally grounded model targeting siCPS – that addresses self-adaptivity and supports dependability by providing traceability between system requirements, distinct situations in the environment, and predefined configurations of system architecture. Additionally, IRM-SA allows for architecture self-adaptation at runtime and integrates the mechanism of predictive monitoring that deals with operational uncertainty. As a proof of concept, it was implemented in DEECo, a component framework that is based on dynamic ensembles of components. Furthermore, its feasibility was evaluated in experimental settings assuming decentralized system operation

Abstract-Component ensembles are a promising way of building self-aware autonomic adaptive systems. This approach has been promoted by the EU project ASCENS, which develops the core idea of ensembles by providing rigorous semantics as well as models and methods for the whole development life cycle of an ensemble-based system. These methods specifically address adaptation, self-awareness, self-optimization, and continuous system evolution. In this paper, we demonstrate the key concepts and benefits of the ASCENS approach in the context of intelligent navigation of electric vehicles (e-Mobility), which itself is one of the three key case studies of the project.

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