Future cyber-physical systems will host a large number of coexisting distributed applications on hardware platforms with thousands to millions of networked components communicating over open networks. These applications and networks are subject to continuous change. The current separation of design process and operation in the field will be superseded by a life-long design process of adaptation, infield integration, and update. Continuous change and evolution, application interference, environment dynamics and uncertainty lead to complex effects which must be controlled to serve a growing set of platform and application needs. Self-adaptation based on self-awareness and self-configuration has been proposed as a basis for such a continuous in-field process. Research is needed to develop automated in-field design methods and tools with the required safety, availability, and security guarantees. The paper shows two complementary use cases of self-awareness in architectures, methods, and tools for cyber-physical systems. The first use case focuses on safety and availability guarantees in self-aware vehicle platforms. It combines contracting mechanisms, tool based self-analysis and self-configuration. A software architecture and a runtime environment executing these tools and mechanisms autonomously are presented including aspects of self-protection against failures and security threats. The second use case addresses variability and long term evolution in networked MPSoC integrating hardware and software mechanisms of surveillance, monitoring, and continuous adaptation. The approach resembles the logistics and operation principles of manufacturing plants which gave rise to the metaphoric term of an Information Processing Factory that relies on incremental changes and feedback control. Both use cases are investigated by larger research groups. Despite their different approaches, both use cases face similar design and design automation challenges which will be summarized in the end. We will argue that seemingly unrelated research challenges, such as in machine learning and security, could also profit from the methods and superior modeling capabilities of self-aware systems.

In the future, vehicles are expected to act more and more autonomously. The transition towards highly automated and autonomous driving will push the safety requirements for in-vehicle networks. Such networks must support isolation between mixed-critical traffic (e.g. critical control and non-critical infotainment) and must be fail-operational. This paper will present new concepts and mechanisms to achieve these goals in Ethernet-based networks. It will cover advanced topics such as software defined networking (SDN) to implement isolation, fault recovery, and controlled degradation, e.g. to maintain (degraded) operation until the driver takes over or to reach a safe stop.

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.

In this paper, we present a middleware protocol, that transmits larger-than-a-frame data samples within application real-time and reliability requirements over a lossy wireless channel. The protocol efficiently deploys an intelligent retransmission control that exploits the extended deadline requirements of a sample for frame-level scheduling. The transmission of such samples is placed in the context of the necessary integration of such external sensor data into the decision-making process of an autonomous vehicle. Therefore, we provide parameterization and access rules for communication links in resource-managed scenarios and lay out how to integrate the protocol into the popular DDS middleware. The performance of the parameterized links to satisfy their reliability requirements is studied and compared to error protection protocols in the current software stack. We base the evaluation on an OMNeT++ simulation, whereby an established wireless error model is used. The use case is placed in the context of collaborative sensing in a valet parking environment, where external infrastructure sensor data augments the autonomous vehicle's data processing chain via a lossy wireless link. Thereby, the use case adheres to the underlying safety requirements. Results show that if the protocol's awareness of the application requirements is used for parameterization, guarantees for reliability can be provided that outperform existing solutions.

Multi-core CPUs are quickly gaining importance in automotive ECUs. While using multi-core architectures for application integration is meanwhile reasonably well understood, parallelization of existing task sets and partitioning of future computation intensive tasks still shows performance limitations and challenges portability and flexibility. The logical execution time (LET) paradigm has been proposed to control core-to-core communication timing which is one of the bottlenecks for automotive system parallelization. We show how to improve the predictability and performance of core-to-core communication by applying only minor modifications to the currently used partitioned static scheduling strategy. The basic idea is to guarantee better response times for low-priority tasks by boosting its priority, thereby using higher priority system slack. It can be selectively applied to individual tasks, and can implement the LET paradigm on all or on a subset of the tasks of a system. It is applicable to lower granularities down to individual runnables which is highly important in automotive applications with large container tasks. We show correctness of the approach and evaluate its performance in a microkernel implementation where it exhibits high performance.

Complex embedded systems such as automobiles and IoT-systems feature a wide range of applications with varying degrees of safety relevance. As many applications on these devices interlace more and more, new ways to guarantee sufficient isolation between safety levels become necessary. Previous work only regarded monitoring of timing properties for individual tasks on single resources and neglected functional cause-effect chains. This makes their application costly because nearly every task would require monitoring due to unknown system-level timing dependencies. We present cross-layer dependency analysis of timing properties and system-level synthesis strategies for monitors utilizing a reachability formulation of the problem. Our approach regards distributed cause-effect chains on networked platforms for which the accuracy of timing parameters can vary and heterogeneous safety requirements can be specified.