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Component-Based Software Engineering (CBSE) does not yet fully address non-functional requirements of embedded systems. To reach this goal, we show how to extend a component model like FRACTAL with relevant abstractions such as threads, protection rings, or security domains. The FRACTAL Architecture Description Language (ADL) is extended by means of properties that tag components, bindings, and interfaces of the system architectural definition with execution schemes, dynamic reconfiguration strategies, protection and isolation patterns, or QoS features. Each extension captures a property-specific "system view" offering a sound basis to address some non-functional requirement. These extensions were experimented in the THINK framework, a C-based implementation of FRACTAL. Results show that THINK provides a generic and efficient approach to fully support these extensions thanks to a customizable toolchain.

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Thanks to their miniature size and high autonomy, sensors have become an integral part of our life. They are being successfully used in many fields (military, house automation, medical, urban environments, etc.). However, a lot of work still needs to be done on querying and system management of large sets of sensors. Scalable solutions supporting continuous data stream management are required. This paper presents a scalable architecture for management of data streams coming from a large set of heterogeneous sensors. Our proposal is at the boundaries of two traditional approaches. The first approach uses centralized or slightly distributed platforms to manage continuous data streams, while the second one allows data processing in a strongly distributed way and is based on networks of "intelligent" sensors. Our hybrid approach proposed in this article offers scalable solutions combining the advantages of these two approaches.