No abstract

No abstract

Modern software systems, such as cyber-physical systems (CPSs), operate in complex and dynamic environments. With the continuous and unanticipated change in the operational environment, these systems are subjected to a variety of uncertainties. Self-adaptive CPSs (SACPSs) can adjust their behavior or structure at run-time as a response to the changes in their perceived environment. Namely, self-adaptation is commonly realized through a MAPE-K feedback loop incorporating newly derived knowledge obtained by the sensed data from the run-time monitoring, during the operation of decentralized SACPSs. However, to build the knowledge, the need for run-time observations' aggregation and reasoning emerges, since the observations made by the decentralized systems might be conflicting. In this paper, we propose an approach for observations aggregation and knowledge modeling in SACPSs that is domain-independent and can deal with inaccurate, partial, and conflicting observations, based on the formalisms of Subjective Logic.

Collaborative Embedded Systems (CES) typically operate in highly dynamic contexts that cannot be completely predicted during design time. These systems are subject to a wide range of uncertainties occurring at runtime, which can be distinguished in aleatory or epistemic. While aleatory uncertainty refers to stochasticity that is present in natural or physical processes and systems, epistemic uncertainty refers to the knowledge that is available to the system, for example, in the form of an ontology, being insufficient for the functionalities that require certain knowledge. Even though both of these two kinds of uncertainties are relevant for CES, epistemic uncertainties are especially important, since forming Collaborative System Groups (CSGs) requires a structured exchange of information. In the au

Traditionally, integration and quality assurance of embedded systems are done entirely at development time. Moreover, since such systems often perform safety-critical tasks and work in human environments, safety analyses are performed and safety argumentations devised to convince certification authorities of their safety and to certify the systems if necessary. Collaborative embedded systems, however, are designed to integrate and collaborate with other systems dynamically at runtime. A complete prediction and analysis of all relevant properties during the design phase is usually not possible, as many influencing factors are not yet known. This makes the application of traditional safety analysis and certification techniques impractical, as they usually require a complete specification of the system and its context in advance. In the following chapter, we introduce new techniques to meet this challenge and outline a safety certification concept specifically tailored to collaborative embedded systems.