Robust and secure resource management for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.Modern vehicles are examples of complex cyber-physical systems with tens to hundreds of interconnected Electronic Control Units (ECUs) that manage various vehicular subsystems. With the shift towards autonomous driving, emerging vehicles are being characterized by an increase in the number of hardware ECUs, greater complexity of applications (software), and more sophisticated in-vehicle networks. These advances have resulted in numerous challenges that impact the reliability, security, and real-time performance of these emerging automotive systems. Some of the challenges include coping with computation and communication uncertainties (e.g., jitter), developing robust control software, detecting cyber-attacks, ensuring data integrity, and enabling confidentiality during communication. However, solutions to overcome these challenges incur additional overhead, which can catastrophically delay the execution of real-time automotive tasks and message transfers. Hence, there is a need for a holistic approach to a system-level solution for resource management in automotive cyber-physical systems that enables robust and secure automotive system design while satisfying a diverse set of system-wide constraints. ECUs in vehicles today run a variety of automotive applications ranging from simple vehicle window control to highly complex Advanced Driver Assistance System (ADAS) applications. The aggressive attempts of automakers to make vehicles fully autonomous have increased the complexity and data rate requirements of applications and further led to the adoption of advanced artificial intelligence (AI) based techniques for improved perception and control. Additionally, modern vehicles are becoming increasingly connected with various external systems to realize more robust vehicle autonomy. These paradigm shifts have resulted in significant overheads in resource constrained ECUs and increased the complexity of the overall automotive system (including heterogeneous ECUs, network architectures, communication protocols, and applications), which has severe performance and safety implications on modern vehicles. The increased complexity of automotive systems introduces several computation and communication uncertainties in automotive subsystems that can cause delays in applications and messages, resulting in missed real-time deadlines. Missing deadlines for safety-critical automotive applications can be catastrophic, and this problem will be further aggravated in the case of future autonomous vehicles. Additionally, due to the harsh operating conditions (such as high temperatures, vibrations, and electromagnetic interference (EMI)) of automotive embedded systems, there is a significant risk to the integrity of the data that is exchanged between ECUs which can lead to faulty vehicle control. These challenges demand a more reliable design of automotive systems that is resilient to uncertainties and supports data integrity goals. Additionally, the increased connectivity of modern vehicles has made them highly vulnerable to various kinds of sophisticated security attacks. Hence, it is also vital to ensure the security of automotive systems, and it will become crucial as connected and autonomous vehicles become more ubiquitous. However, imposing security mechanisms on the resource constrained automotive systems can result in additional computation and communication overhead, potentially leading to further missed deadlines. Therefore, it is crucial to design techniques that incur very minimal overhead (lightweight) when trying to achieve the above-mentioned goals and ensure the real-time performance of the system. We address these issues by designing a holistic resource management framework called ROSETTA that enables robust and secure automotive cyber-physical system design while satisfying a diverse set of constraints related to reliability, security, real-time performance, and energy consumption. To achieve reliability goals, we have developed several techniques for reliability-aware scheduling and multi-level monitoring of signal integrity. To achieve security objectives, we have proposed a lightweight security framework that provides confidentiality and authenticity while meeting both security and real-time constraints. We have also introduced multiple deep learning based intrusion detection systems (IDS) to monitor and detect cyber-attacks in the in-vehicle network. Lastly, we have introduced novel techniques for jitter management and security management and deployed lightweight IDSs on resource constrained automotive ECUs while ensuring the real-time performance of the automotive systems

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

Online scheduling for real-time multitasking on reconfigurable hardware devices

Nowadays the ever increasing algorithmic complexity of embedded applications requires the designers to turn towards heterogeneous and highly integrated systems denoted as SoC (System-on-a-Chip). These architectures may embed CPU-based processors, dedicated datapaths as well as recon gurable units. However, embedded SoCs are submitted to stringent requirements in terms of speed, size, cost, power consumption, throughput, etc. Therefore, new computing paradigms are required to ful l the constraints of the applications and the requirements of the architecture. Recon gurable Computing is a promising paradigm that provides probably the best trade-o between these requirements and constraints. Dynamically recon gurable architectures are their key enabling technology. They enable the hardware to adapt to the application at runtime. However, these architectures raise new challenges in SoC design. For example, on one hand, designing a system that takes advantage of dynamic recon guration is still very time consuming because of the lack of design methodologies and tools. On the other hand, scheduling hardware tasks di ers from classical software tasks scheduling on microprocessor or multiprocessors systems, as it bears a further complicated placement problem. This thesis deals with the problem of scheduling online real-time hardware tasks on Dynamically Recon gurable Hardware Devices (DRHWs). The problem is addressed from two angles : (i) Investigating novel algorithms for online real-time scheduling/placement on DRHWs. (ii) Scheduling/Placement algorithms library for RTOS-driven Design Space Exploration (DSE). Regarding the first point, the thesis proposes two main runtime-aware scheduling and placement techniques and assesses their suitability for online real-time scenarios. The first technique discusses the impact of synthesizing, at design time, several shapes and/or sizes per hardware task (denoted as multi-shape task), in order to ease the online scheduling process. The second technique combines a looking-ahead scheduling approach with a slots-based recon gurable areas management that relies on a 1D placement. The results show that in both techniques, the scheduling and placement quality is improved without signi cantly increasing the algorithm time complexity. Regarding the second point, in the process of designing SoCs embedding recon gurable parts, new design paradigms tend to explore and validate as early as possible, at system level, the architectural design space. Therefore, the RTOS (Real-Time Operating System) services that manage the recon gurable parts of the SoC can be re fined. In such a context, gathering numerous hardware tasks scheduling and placement algorithms of various complexity vs performance trade-o s in a kind of library is required. In this thesis, proposed algorithms in addition to some existing ones are purposely implemented in C++ language, in order to insure the compatibility with any C++/SystemC based SoC design methodology.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Scratchpad Memory Management For Multicore Real-Time Embedded Systems

Multicore systems will continue to spread in the domain of real-time embedded systems due to the increasing need for high-performance applications. This research discusses some of the challenges associated with employing multicore systems for safety-critical real-time applications. Mainly, this work is concerned with providing: 1) efficient inter-core timing isolation for independent tasks, and 2) predictable task communication for communicating tasks. Principally, we introduce a new task execution model, based on the 3-phase execution model, that exploits the Direct Memory Access (DMA) controllers available in modern embedded platforms along with ScratchPad Memories (SPMs) to enforce strong timing isolation between tasks. The DMA and the SPMs are explicitly managed to pre-load tasks from main memory into the local (private) scratchpad memories. Tasks are then executed from the local SPMs without accessing main memory. This model allows CPU execution to be overlapped with DMA loading/unloading operations from and to main memory. We show that by co-scheduling task execution on CPUs and using DMA to access memory and I/O, we can efficiently hide access latency to physical resources. In turn, this leads to significant improvements in system schedulability, compared to both the case of unregulated contention for access to physical resources and to previous cache and SPM management techniques for real-time systems. The presented SPM-centric scheduling algorithms and analyses cover single-core, partitioned, and global real-time systems. The proposed scheme is also extended to support large tasks that do not fit entirely into the local SPM. Moreover, the schedulability analysis considers the case of recovering from transient soft errors (bit flips caused by a single event upset) in several levels of memories, that cannot be automatically corrected in hardware by the ECC unit. The proposed SPM-centric scheduling is integrated at the OS level; thus it is transparent to applications. The proposed scheme is implemented and evaluated on an FPGA platform and a Commercial-Off-The-Shelf (COTS) platform. In regards to real-time task communication, two types of communication are considered. 1) Asynchronous inter-task communication, between either sequential tasks (single-threaded) or parallel tasks (multi-threaded). 2) Intra-task communication, where parallel threads of the same application exchange data. A new task scheduling model for parallel tasks (Bundled Scheduling) is proposed to facilitate intra-task communication and reduce synchronization overheads. We show that the proposed bundled scheduling model can be applied to several parallel programming models, such as fork-join and DAG-based applications, leading to improved system schedulability. Finally, intra-task communication is governed by a predictable inter-core communication platform. Specifically, we propose HopliteRT, a lean and predictable Network-on-Chip that connects the private SPMs

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected