This paper addresses the challenges in managing the continuous change and evolution of CPSs and their operation environment. It presents two frameworks, controlling concurrent change (CCC) and information processing factory (IPF), for building self-aware CPSs that have the capabilities of self-modeling, self-configuration, and monitoring.
The increasing complexity of automotive software systems and the desire for more frequent software and even feature updates require new approaches to the design, integration and testing of these systems. Ideally, those approaches enable an in-field updatability of automotive software systems that provides the same degree of safety guarantees as the traditionally labbased deployment. In this paper, we present a layered modelling approach that formalises the integration procedure of automotive software systems using graph-based models and formal analyses.
Automotive control systems typically have latency requirements for certain cause-effect chains. When implementing and integrating these systems, these latency requirements must be guaranteed e.g. by applying a worst-case analysis that takes all indeterminism and limited predictability of the timing behaviour into account. In this paper, we address the latency analysis for multi-rate distributed cause-effect chains considering staticpriority preemptive scheduling of offset-synchronised periodic tasks. We particularly focus on data age as one representative of the two most common latency semantics. Our main contribution is an Mixed Integer Linear Program-based optimisation to select design parameters (priorities, task-to-processor mapping, offsets) that minimise the data age. In our experimental evaluation, we apply our method to two real-world automotive use cases.
The IoT will host a large number of co-existing cyber-physical applications. Continuous change, application interference, environment dynamics and uncertainty lead to complex effects which must be controlled to give performance and application guarantees. Application and platform self-configuration and self-awareness are one paradigm to approach this challenge. They can leverage context knowledge to control platform and application functions and their interaction. They could play a dominant role in large scale cyber-physical systems and systems-of-systems, simply because no person can oversee the whole system functionality and dynamics. IoT adds a new dimension because Internet based services will increasingly be used in such system functions. Autonomous vehicles accessing cloud services for efficiency and comfort as well as to reach the required level of safety and security are an example. Such vehicle platforms will communicate with a service infrastructure that must be reliable and highly responsive. Automated continuous self-configuration of data storage might be a good basis for such services up to the point where the different self-x strategies might affect each other, in a positive or negative form. This paper contains three contributions from different domains representing the current status of self-aware systems as they will meet in the Internet-of-Things and closes with a short discussion of upcoming challenges.
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