Existing results for cyber-physical systems have been proposed to merge the requirements associated to the stability of the physical components and the schedulability of the cyber components. Nevertheless, none of the existing results has studied these requirements for multiple real-time cascade control tasks where their periods choice are dependent and affect stability. In this paper, we propose a methodology to evaluate the periods of the real-time cascade control tasks that ensures stability of the physical components, then we present a co-design problem for the period choice that guarantees good performance of the physical components and schedulability of the cyber components under fixed-priority scheduling. We then evaluate this methodology on a real use-case of a drone system. Results show the importance of studying these requirements together as their relation has an impact on stable periods range.

In this paper, we provide first results introducing the impact of both software and hardware events on the estimation of worst-case energy consumption of programs on embedded processors. We build a framework to better understand the representativeness of measurements with respect to both software and hardware events. We test this framework on execution times and energy consumption data for 5 existing benchmarks as a step towards a statistical worst-case energy consumption model.

The real-time systems community is facing the lack of benchmarks adapted to measurement-based worst-case execution time (WCET) estimators. We provide in this paper first steps towards such benchmarks by proposing them for single core microcontrollers, while we leave as future work the migration to multicore microcontrollers. The considered benchmarks are the programs of an open source drone autopilot. We conclude the paper by underlining the main difficulties of such migration.

This paper presents the design and analysis of a data science competition on a problem of time series regression from aeronautics data. For the purpose of performing predictive maintenance, aviation companies seek to create aircraft “numerical twins”, which are programs capable of accurately predicting strains at strategic positions in various body parts of the aircraft. Given a number of input parameters (sensor data) recorded in sequence during the flight, the competition participants had to predict output values (gauges), also recorded sequentially during test flights, but not recorded during regular flights. The competition data included hundreds of complete flights. It was a code submission competition with complete blind testing of algorithms. The results indicate that such a problem can be effectively solved with gradient boosted trees, after preprocessing and feature engineering. Deep learning methods did not prove as efficient.

The design of cyber-physical systems (CPSs) is facing the explosion of new functionalities requiring increased computation capacities and, thus, the introduction of multi-core processors. Moreover, some functionalities may impose precedence constraints between the programs implementing these new functionalities. While important effort has been dedicated to the scheduling of precedence constraints tasks on multi-core processors, existing work considers either partitioned scheduling for a single precedence graph defining precedence constraints between tasks, or global scheduling policies.In this paper, we consider partitioned scheduling for multiple precedence graphs defining precedence constraints between tasks. The variability of execution times and of communication times is described by probability distributions. We propose a new response time analysis over-performing existing ILP-based results. Thanks to its scalability, our solution is extendable to a probabilistic version and we validate it on a PX4 drone autopilot. Beside this autopilot for our experiments, we implemented a probabilistic extension of a multi-core processor simulator, SimSo. A priority assignment heuristic allowing parallel executions is also proposed. Thanks to its adaptation to partitioned scheduling, our heuristic has better performances than existing solutions and its performances are, also, compared against a genetic-based heuristic.

The design of embedded systems is facing the explosion of new functionalities requiring increased computation capacities and, thus, the introduction of multi-core processors. Moreover, some functionalities may impose precedence constraints between the programs implementing them. In this paper, we consider partitioned scheduling of tasks with precedence constraints defined by multiple Directed Acyclic Graphs (DAGs). The variability of execution and communication times is taken into account by describing them with probability distributions. Our probabilistic response time analysis is validated on random generated task sets and on a PX4 drone autopilot.

The continuous integration of new functionality increases the complexity of embedded systems, while each functionality might impose precedence constraints between the programs fulfilling it. In addition, the prevalence of several processors may create the illusion of higher computation capacity easing the associated scheduling problem. However, this capacity is not exploitable in critical real time systems because of the increased variability of the execution times due to processor features designed to provide excellent average time behaviour and not necessarily ensuring small worst case bounds. This difficulty is added to the existence of scheduling anomalies when the systems are built on top of several processors. In this paper, we study the feasibility of independent tasks scheduled according to a given preemptive fixed-priority partitioned policy on identical processors. Each task is composed of several dependent subtasks related between them according to a directed acyclic graph (DAG). We provide a worst case response time analysis for DAG tasks when each sub-tasks have an individual priority level. This assumption allows to decrease the number of possible execution scenarios, making our analysis easier and less pessimistic.