Selma Saidi

Real-time systems are intrinsic components of many pivotal applications, such as self-driving vehicles, aerospace and defense systems. The trend in these applications is to incorporate multiple tasks onto fewer, more powerful hardware platforms, e.g., multi-core systems, mainly for reducing cost and power consumption. Many real-time tasks, like control tasks, can tolerate occasional deadline misses due to robust algorithms. These tasks can be modeled using the weakly-hard model. Literature shows that leveraging the weakly-hard model can relax the over-provisioning associated with designed real-time systems. However, a wide-range of the research focuses on single-core platforms. Therefore, we strive to extend the state-of-the-art of scheduling weakly-hard real-time tasks to multi-core platforms. We present a global job-level fixed priority scheduling algorithm together with its schedulability analysis. The scheduling algorithm leverages the tolerable continuous deadline misses to assigning priorities to jobs. The proposed analysis extends the Response Time Analysis (RTA) for global scheduling to test the schedulability of tasks. Hence, our analysis scales with the number of tasks and number of cores because, unlike literature, it depends neither on Integer Linear Programming nor reachability trees. Schedulability analyses show that the schedulability ratio is improved by 40% comparing to the global Rate Monotonic (RM) scheduling and up to 60% more than the global EDF scheduling, which are the state-of-the-art schedulers on the RTEMS real-time operating system. Our evaluation on industrial embedded multi-core platform running RTEMS shows that the scheduling overhead of our proposal does not exceed 60 nanosecond.

With the development of wireless communication technology, complex and dynamic scenarios pose great challenges to the Quality of Service (QoS) of wireless communication, especially in indoor scenarios. The quality of beam management can be greatly improved if signal ray-tracing module is embedded in wireless devices to handle synthetic multipath transmissions in real time. In this article, a novel reflection path derivation algorithm for ray tracing of signal beams is proposed, which builds the core mechanism of the proposed FPGA accelerator for ray tracing: by decomposing the computation of the entire ray path into mutually independent subproblems associated with the respective planes involved in the reflection and implemented by independent processing element on FPGAs, the parallelization of the entire ray tracing is realized, which significantly improves the convergence speed of the ray tracing; meanwhile, a new high-level synthesis workflow corresponds to the proposed algorithm and hardware architecture is proposed, which opens the door on synthesizing embedded hardware dedicated for robust and real-time wireless communication. After validation, the method proposed in this article can generate FPGA accelerator for real-time ray-tracing effectively, which achieves ray-tracing simulation in milliseconds.

This is a summary paper of a use case of a Robotdog dedicated to guide visually impaired people in complex environment like a smart intersection. In such scenarios, the Robotdog has to autonomously decide whether it is safe to cross the intersection or not in order to further guide the human. We leverage data sharing and collaboration between the Robotdog and other autonomous systems operating in the same environment. We propose a system architecture for autonomous systems through a separation of a collaborative decision layer, to enable collective decision making processes, where data about the environment, relevant to the Robotdog decision, together with evidences for trustworthiness about other systems and the environment are shared.

Autonomous applications are typically developed over Robot Operating System 2.0 (ROS2) even in time-critical systems like automotive. Recent years have seen increased interest in developing model-based timing analysis and schedule optimization approaches for ROS2-based applications. To complement these approaches, we propose a tracing and measurement framework to obtain timing models of ROS2-based applications. It offers a tracer based on extended Berkeley Packet Filter (eBPF) that probes different functions in ROS2 middleware and reads their arguments or return values to reason about the data flow in applications. It combines event traces from ROS2 and the operating system to generate a directed acyclic graph showing ROS2 callbacks, precedence relations between them, and their timing attributes. While being compatible with existing analyses, we also show how to model (i)~message synchronization, e.g., in sensor fusion, and (ii)~service requests from multiple clients, e.g., in motion planning. Considering that, in real-world scenarios, the application code might be confidential and formal models are unavailable, our framework still enables the application of existing analysis and optimization techniques.

Collaboration in multi-agent autonomous systems is critical to increase performance while ensuring safety. However, due to heterogeneity of their features in, e.g., perception qualities, some autonomous systems have to be considered more trustworthy than others when contributing to collaboratively build a common environmental model, especially under uncertainty. In this paper, we introduce the idea of increasing the reliability of autonomous systems by relying on collective intelligence. We borrow concepts from social epistemology to exploit individual characteristics of autonomous systems, and define and formalize at design rules for collective reasoning to achieve collaboratively increased safety, trustworthiness and good decision making.

Fueled by the progress of artificial intelligence, autonomous systems become more and more integral parts of many Internet-of-Things (IoT) and cyber–physical systems (CPSs) applications, such as automated driving, robotics, avionics, and industrial automation. Autonomous systems are self-governed and self-adaptive systems that are designed to operate in an open and evolving environment that has not been completely defined at design time. However, this does not come for free. The advance from established systems technology to autonomous systems is not an issue of developing and introducing new functions in existing design processes. We firmly believe, and will elaborate, that it will have a deep impact on engineering, on the way we design, verify, test, and operate systems. We must find ways to keep high engineering standards, such as dependability and controllability, when allowing the system to change its behavior in the field. In other words, the future of autonomous systems is not only an issue of systems intelligence, but depends on our capability to design them responsibly.

Autonomous systems act independently and solve complex tasks without human intervention, and they are able to learn and adapt to an evolving environment. These capabilities of autonomous systems promise significant benefits and disruption potential in many domains such as mobility, production systems, healthcare, smart home and smart energy systems. As these domains are classical cyber-physical system (CPS) domains, autonomous systems inherit strict dependability requirements (e.g. safety, security, availability or real-time) from these systems. Assuring these requirements poses a major challenge on the way to broad market introduction as can be seen e.g. by roadmaps for autonomous driving systems which are currently stretched farther out.

The Internet of Vehicle (IoV) is an extension of Vehicle-to-Vehicle (V2V) communication that can improve vehicles’ fully autonomous driving capabilities. However, these communications are vulnerable to many attacks. Therefore, it is critical to provide run-time mechanisms to detect malware and stop the attackers before they manage to gain a foothold in the system. Anomaly-based detection techniques are convenient and capable of detecting off-nominal behavior by the component caused by zero-day attacks. One significant critical aspect when using anomaly-based techniques is ensuring the correct definition of the observed component’s normal behavior. In this paper, we propose using the task’s temporal specification as a baseline to define its normal behavior and identify temporal thresholds that give the system the ability to predict malicious tasks. By applying our solution on one use-case, we got temporal thresholds 20–40 % less than the one usually used to alarm the system about security violations. Using our boundaries ensures the early detection of off-nominal temporal behavior and provides the system with a sufficient amount of time to initiate recovery actions.