Feedback Control of Cyber-Physical Systems with Multi Resource Dependencies and Model Uncertainties

The problem of modeling and controlling re- sources in a system with interaction between hardware and software is considered. A model encompassing both hardware and software dynamics is developed together with an on- line estimation scheme in order reduce dependence on a- priori information. A control structure is presented in order to control performance under constrained resource situations and to reduce effects of estimation errors and disturbances. The approach is applied to a conversational video case and evaluated through simulations

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

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

Adaptive Resource Management for Uncertain Execution Platforms

Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible. This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high

Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities

Reliability assessment refers to the process of evaluating reliability of components or systems during their lifespan or prior to their implementation. In the manufacturing industry, the reliability of systems is directly linked to production efficiency, product quality, energy consumption, and other crucial performance indicators. Therefore, reliability plays a critical role in every aspect of manufacturing. In this review, we provide a comprehensive overview of the most significant advancements and trends in the assessment of manufacturing system reliability. For this, we also consider the three main facets of reliability analysis of cyber–physical systems, i.e., hardware, software, and human-related reliability. Beyond the overview of literature, we derive challenges and opportunities for reliability assessment of manufacturing systems based on the reviewed literature. Identified challenges encompass aspects like failure data availability and quality, fast-paced technological advancements, and the increasing complexity of manufacturing systems. In turn, the opportunities include the potential for integrating various assessment methods, and leveraging data to automate the assessment process and to increase accuracy of derived reliability models

Critical Infrastructure Protection Approaches: Analytical Outlook on Capacity Responsiveness to Dynamic Trends

Overview: Critical infrastructures (CIs) – any asset with a functionality that is critical to normal societal functions, safety, security, economic or social wellbeing of people, and disruption or destruction of which would have a very significant negative societal impact. CIs are clearly central to the normal functioning of a nation’s economy and require to be protected from both intentional and unintentional sabotages. It is important to correctly discern and aptly manage security risks within CI domains. The protection (security) of CIs and their networks can provide clear benefits to owner organizations and nations including: enabling the attainment of a properly functioning social environment and economic market, improving service security, enabling integration to external markets, and enabling service recipients (consumers, clients, and users) to benefit from new and emerging technological developments. To effectively secure CI system, firstly, it is crucial to understand three things - what can happen, how likely it is to happen, and the consequences of such happenings. One way to achieve this is through modelling and simulations of CI attributes, functionalities, operations, and behaviours to support security analysis perspectives, and especially considering the dynamics in trends and technological adoptions. Despite the availability of several security-related CI modelling approaches (tools and techniques), trends such as inter-networking, internet and IoT integrations raise new issues. Part of the issues relate to how to effectively (more precisely and realistically) model the complex behavior of interconnected CIs and their protection as system of systems (SoS). This report attempts to address the broad goal around this issue by reviewing a sample of critical infrastructure protection approaches; comprising tools, techniques, and frameworks (methodologies). The analysis covers contexts relating to the types of critical infrastructures, applicable modelling techniques, risk management scope covered, considerations for resilience, interdependency, and policy and regulations factors. Key Findings: This research presents the following key findings: 1. There is not a single specific Critical Infrastructure Protection (CIP) approach – tool, technique, methodology or framework – that exists or emerges as a ‘fit-for-all’; to allow the modelling and simulation of cyber security risks, resilience, dependency, and impact attributes in all critical infrastructure set-ups. 2. Typically, two or more modelling techniques can be (need to be) merged to cover a broader scope and context of modelling and simulation applications (areas) to achieve desirable highlevel protection and security for critical infrastructures. 3. Empirical-based, network-based, agent-based, and system dynamics-based modelling techniques are more widely used, and all offer gains for their use. 4. The deciding factors for choosing modelling techniques often rest on; complexity of use, popularity of approach, types and objectives of user Organisation and sector. 5. The scope of modelling functions and operations also help to strike the balance between ‘specificity’ and ‘generality’ of modelling technique and approach for the gains of in-depth analysis and wider coverage respectively. 6. Interdependency and resilience modelling and simulations in critical infrastructure operations, as well as associated security and safety risks; are crucial characteristics that need to be considered and explored in revising existing or developing new CIP modelling approaches. Recommendations: Key recommendations from this research include: 1. Other critical infrastructure sectors such as emergency services, food & agriculture, and dams; need to draw lessons from the energy and transportation sectors for the successive benefits of: i. Amplifying the drive and efforts towards evaluating and understanding security risks to their infrastructure and operations. ii. Support better understanding of any associated dependencies and cascading impacts. iii. Learning how to establish effective security and resilience. iv. Support the decision-making process linked with measuring the effectiveness of preparedness activities and investments. v. Improve the behavioural security-related responses of CI to disturbances or disruptions. 2. Security-related critical infrastructure modelling approaches should be developed or revised to include wider scopes of security risk management – from identification to effectiveness evaluations, to support: i. Appropriate alignment and responsiveness to the dynamic trends introduced by new technologies such as IoT and IIoT. ii. Dynamic security risk management – especially the assessment section needs to be more dynamic than static, to address the recurrent and impactful risks that emerge in critical infrastructures