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We present a middleware-based protocol that reliably synchronizes large samples consisting of multiple frames efficiently and within application level QoS requirements over a lossy wireless channel. The protocol uses a custom retransmission scheme, exploiting the latency requirements on sample level for frame level scheduling. It can be integrated into the popular DDS middleware. We investigate some technical limits of such a protocol and compare it to existing error protocols in the software stack and in the wireless protocol and combinations thereof. The comparison is based on an Omnet++ simulation using an established wireless channel error model. For evaluation, we take a use case from automated valet parking where infrastructure data provided via a wireless link augments in-vehicle sensor data. The use case respects the related safety requirements. Results show that the application awareness of the presented protocol, significantly improves service availability by transmitting data efficiently in time even under higher frame error rates.

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When modelling software components for timing analysis, we typically encounter functional chains of tasks that lead to precedence relations. As these task chains represent a functionally-dependent sequence of operations, in real-time systems, there is usually a requirement for their end-to-end latency. When mapped to software components, functional chains often result in communicating threads. Since threads are scheduled rather than tasks, specific task chain properties arise that can be exploited for response-time analysis by extending the busy-window analysis for such task chains in static-priority preemptive systems. We implemented this analysis by means of an analysis extension for pyCPA, a research-grade implementation of compositional performance analysis (CPA).The major scope of this demo is to show how CPA can be reasonably performed for realistic component-based systems. It also demonstrates how research on and with CPA is conducted using the pyCPA analysis framework. In the course of this demo, we show two approaches for the extraction of an appropriate timing model: 1) the derivation from a contract-based specification of the software components and 2) a tracing-based approach suitable for black-box components. We also demonstrate how this timing model is fed into the analysis extension in order to obtain response-time results for the task chains of interest. Finally, we present how the developed analysis extension speeds up the CPA and therefore enables an automated design-space exploration and optimisation of the threads' priority assignments in order to satisfy the pre-defined latency requirements.