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The Internet of Things (IoT) envisions billions of connected objects being able to sense, act and communicate with each other. This situation provides rich opportunities to dynamic context recognition mechanisms, which aim to make use of the connected sensors to recognize desired context information. However, there are still several challenges to solve for dynamic context recognition mechanisms to be implemented. One of those challenges is dynamic resource selection, the ability to dynamically select between different data sources to detect a specific type of information and to adapt this selection, when necessary. In this paper, we present an agent-based architecture to combine event-driven Service-Oriented Architecture (SOA) with a data streaming platform to enable dynamic context recognition. The architecture has been defined in the scope of a testing environment for autonomous driving. There, it is used to dynamically select data sources to provide information to autonomous vehicles and applications based on them.

Opportunistic sensing is an adaptive technique for context recognition that aims to make use of available sensors instead of requiring the deployment of specific sensors for a specific recognition goal. Methods implementing the opportunistic sensing approach need to adapt themselves to the availability of data sources and the context recognition goals of the system. Opportunity Framework, a reference implementation of opportunistic sensing, provides this adaptability with two black box components. In the scope of autonomous computing the explicit use of feedback loops to control self-adaptation has been proposed. Several authors have argued that this concept is central to self-adaptive systems in general. Therefore, applying an explicit feedback loop can be useful to conceptually compare and combine Opportunity with other self-adaptive systems. In this paper we first applied MAPE-K feedback loop for the adaptability of the Opportunity Framework on a conceptual level. Our findings are that this feedback loop could be applied to Opportunity but that multiple feedback loops could provide clearer separation of the adaptive components. Second, using the DYNAMICO model for this purpose, a conceptual model that proposes three interacting feedback loops, yields further insights and leads us to propose separation of components for the adaptability in Opportunity Framework.

A self-adaptive software system modifies its behavior at runtime in response to changes within the system or in its execution environment. The fulfillment of the system requirements needs to be guaranteed even in the presence of adverse conditions and adaptations. Thus, a key challenge for self-adaptive software systems is assurance. Traditionally, confidence in the correctness of a system is gained through a variety of activities and processes performed at development time, such as design analysis and testing. In the presence of selfadaptation, however, some of the assurance tasks may need to be performed at runtime. This need calls for the development of techniques that enable continuous assurance throughout the software life cycle. Fundamental to the development of runtime assurance techniques is research into the use of models at runtime (M@RT). This chapter explores the state of the art for usingM@RT to address the assurance of self-adaptive software systems. It defines what information can be captured by M@RT, specifically for the purpose of assurance, and puts this definition into the context of existing work. We then outline key research challenges for assurance at runtime and characterize assurance methods. The chapter concludes with an exploration of selected application areas where M@RT could provide significant benefits beyond existing assurance techniques for adaptive systems., Comment: 36 pages, 2 figures, In: Models@run.time, LNCS 8378, pp. 101-136, Springer Publisher, 2014

Traffic systems of major cities have reached a high level of sophistication, yet, the authors think their heterogeneity difficult. Ever-new mobility concepts occur, such as car or ride sharing. It is the authors opinion that a holistic consideration of all available concepts may provide significant benefits. In this paper the authors present such consideration in the form of an open accessible, agent-based mobility platform, namely the intermodal mobility assistance (IMA) system.