Engineering Of Cyber-physical Systems Research Articles

Agents and Digital Twins for the engineering of Cyber-Physical Systems: opportunities, and challenges

Agents and Digital Twins for the engineering of Cyber-Physical Systems: opportunities, and challenges

Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives

The engineering of large-scale cyber-physical systems (CPS) increasingly relies on principles from self-organisation and collective computing, enabling these systems to cooperate and adapt in dynamic environments. CPS engineering also often leverages digital twins that provide synchronised logical counterparts of physical entities. In contrast, sensor networks rely on the different but related concept of virtual device that provides an abstraction of a group of sensors. In this work, we study how such concepts can contribute to the engineering of self-organising CPSs. To that end, we analyse the concepts and devise modelling constructs, distinguishing between identity correspondence and execution relationships. Based on this analysis, we then contribute to the novel concept of “collective digital twin” (CDT) that captures the logical counterpart of a collection of physical devices. A CDT can also be “augmented” with purely virtual devices, which may be exploited to steer the self-organisation process of the CDT and its physical counterpart. We underpin the novel concept with experiments in the context of the pulverisation framework of aggregate computing, showing how augmented CDTs provide a holistic, modular, and cyber-physically integrated system view that can foster the engineering of self-organising CPSs.

T-CHAT educational framework for teaching cyber-physical system engineering

ABSTRACT Cyber-physical systems (CPS) are increasingly used in manufacturing, transportation, health, and other industries. To develop these complex interdisciplinary systems, highly qualified CPS engineers are required who possess sound engineering knowledge and excellent transferable skills. Academic institutions offer a range of modules and curricula to teach CPS engineering. However, the literature reports a gap between expectations of industry and competencies of CPS graduates. To close this gap, this paper introduces and describes a holistic educational framework (T-CHAT) for teaching CPS engineering at the module level. To evaluate this framework, two use cases were analysed by conducting self-perception surveys and semi-structured interviews with students. Descriptive statistics and t-tests were calculated for the survey data. Interviews were coded and analysed using a General Inductive Approach. The analysis results were discussed by the comparison of the T-CHAT implementations in these two use cases.

Model-driven engineering city spaces via bidirectional model transformations

Engineering cyber-physical systems inhabiting contemporary urban spatial environments demands software engineering facilities to support design and operation. Tools and approaches in civil engineering and architectural informatics produce artifacts that are geometrical or geographical representations describing physical spaces. The models we consider conform to the CityGML standard; although relying on international standards and accessible in machine-readable formats, such physical space descriptions often lack semantic information that can be used to support analyses. In our context, analysis as commonly understood in software engineering refers to reasoning on properties of an abstracted model—in this case a city design. We support model-based development, firstly by providing a way to derive analyzable models from CityGML descriptions, and secondly, we ensure that changes performed are propagated correctly. Essentially, a digital twin of a city is kept synchronized, in both directions, with the information from the actual city. Specifically, our formal programming technique and accompanying technical framework assure that relevant information added, or changes applied to the domain (resp. analyzable) model are reflected back in the analyzable (resp. domain) model automatically and coherently. The technique developed is rooted in the theory of bidirectional transformations, which guarantees that synchronization between models is consistent and well behaved. Produced models can bootstrap graph-theoretic, spatial or dynamic analyses. We demonstrate that bidirectional transformations can be achieved in practice on real city models.

Compositional Cyber-Physical Systems Modeling

Assuring the correct behavior of cyber-physical systems requires significant modeling effort, particularly during early stages of the engineering and design process when a system is not yet available for testing or verification of proper behavior. A primary motivation for `getting things right' in these early design stages is that altering the design is significantly less costly and more effective than when hardware and software have already been developed. Engineering cyber-physical systems requires the construction of several different types of models, each representing a different view, which include stakeholder requirements, system behavior, and the system architecture. Furthermore, each of these models can be represented at different levels of abstraction. Formal reasoning has improved the precision and expanded the available types of analysis in assuring correctness of requirements, behaviors, and architectures. However, each is usually modeled in distinct formalisms and corresponding tools. Currently, this disparity means that a system designer must manually check that the different models are in agreement. Manually editing and checking models is error prone, time consuming, and sensitive to any changes in the design of the models themselves. Wiring diagrams and related theory provide a means for formally organizing these different but related modeling views, resulting in a compositional modeling language for cyber-physical systems. Such a categorical language can make concrete the relationship between different model views, thereby managing complexity, allowing hierarchical decomposition of system models, and formally proving consistency between models.

Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems

Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the safety of machine learning became a focus area for research in recent years. Despite very considerable advances in selected areas related to machine learning safety, shortcomings were identified on holistic approaches that take an end-to-end view on the risks associated to the engineering of ML-based control systems and their certification. Applying a classic technique of safety engineering, our paper provides a comprehensive and methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and could compromise the safe operation of ML-based CPS. Identified hazards are illustrated and explained using a real-world application scenario—an autonomous shop-floor transportation vehicle. The comprehensive analysis presented in this paper is intended as a basis for future holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the focus on research onto safety hazards that are not yet adequately addressed.

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Orthogonal Uncertainty Modeling in the Engineering of Cyber-Physical Systems

Software-intensive cyber-physical systems (CPS) perform essential tasks such as controlling automated production processes in industrial production plants. The required levels of autonomy, openness, and self-adaptation, as well as the dynamic nature of the context of such CPS, result in challenging tasks for their engineering. During operation, unexpected situations in which the system has insufficient knowledge about the current state of the system itself as well as its context may occur. Engineering CPS, e.g., for industrial production sites, must account for such uncertainties the system will have to cope with during its lifetime in a structured and systematic way. Since the development of CPS requires consideration of different system perspectives, current uncertainty modeling approaches cannot be applied right away, as they do not explicitly consider uncertainty aspects that affect different artifacts. To aid the engineering of CPS, this article presents a model-based approach to document uncertainty. We propose “Orthogonal Uncertainty Models,” which closely integrate with other engineering artifacts from different perspectives, as a means for capturing a dedicated uncertainty viewpoint. Our approach has been evaluated in the industry automation domain. The application shows that the idea of regarding uncertainty within a dedicated perspective is highly beneficial. Particularly, our approach helps to uncover and document uncertainties related to behavioral, functional, and structural properties of a system, as well as uncertainties related to business models that would otherwise possibly remain covert. Note to Practitioners-Identifying and documenting uncertainties, which may occur during operation of a system, is a common problem in engineering processes. Such uncertainties may lead to severe damage, and thus need to be mitigated appropriately. It is crucial to account for these uncertainties during engineering, especially in the early phases. Depending on the specific project characteristics, a multitude of different diagram types are used to model a system. Uncertainties thus reflect in many artifacts, which leads to: 1) redundancies in the specified uncertainty attached to diagram elements and 2) uncertainty information (e.g., about the cause or effect of uncertainty) that is spread across different diagrams. The latter makes it difficult to structure uncertainty information and trace it throughout the engineering process so that uncertainty can be systematically considered. Our approach provides a graphical modeling language that employs a dedicated perspective on uncertainty in separate diagrams that can be linked to any engineering artifact.

Test Generation and Test Prioritization for Simulink Models with Dynamic Behavior

All engineering disciplines are founded and rely on models, although they may differ on purposes and usages of modeling. Among the different disciplines, the engineering of Cyber Physical Systems (CPSs) particularly relies on models with dynamic behaviors (i.e., models that exhibit time-varying changes). The Simulink modeling platform greatly appeals to CPS engineers since it captures dynamic behavior models. It further provides seamless support for two indispensable engineering activities: (1) automated verification of abstract system models via model simulation, and (2) automated generation of system implementation via code generation. We identify three main challenges in the verification and testing of Simulink models with dynamic behavior, namely incompatibility, oracle and scalability challenges. We propose a Simulink testing approach that attempts to address these challenges. Specifically, we propose a black-box test generation approach, implemented based on meta-heuristic search, that aims to maximize diversity in test output signals generated by Simulink models. We argue that in the CPS domain test oracles are likely to be manual and therefore the main cost driver of testing. In order to lower the cost of manual test oracles, we propose a test prioritization algorithm to automatically rank test cases generated by our test generation algorithm according to their likelihood to reveal a fault. Engineers can then select, according to their test budget, a subset of the most highly ranked test cases. To demonstrate scalability, we evaluate our testing approach using industrial Simulink models. Our evaluation shows that our test generation and test prioritization approaches outperform baseline techniques that rely on random testing and structural coverage.

How to Deal with the Complexity of Future Cyber-Physical Systems?

Cyber-Physical Systems (CPS) integrate computation, networking and physical processes to produce products that are autonomous, intelligent, connected and collaborative. Resulting Cyber-Physical Systems of Systems (CPSoS) have unprecedented capabilities but also unprecedented corresponding technological complexity. This paper aims to improve understanding, awareness and methods to deal with the increasing complexity by calling for the establishment of new foundations, knowledge and methodologies. We describe causes and effects of complexity, both in general and specific to CPS, consider the evolution of complexity, and identify limitations of current methodologies and organizations for dealing with future CPS. The lack of a systematic treatment of uncertain complex environments and “composability”, i.e., to integrate components of a CPS without negative side effects, represent overarching limitations of existing methodologies. Dealing with future CPSoS requires: (i) increased awareness of complexity, its impact and best practices for how to deal with it, (ii) research to establish new knowledge, methods and tools for CPS engineering, and (iii) research into organizational approaches and processes to adopt new methodologies and permit efficient collaboration within and across large teams of humans supported by increasingly automated computer aided engineering systems.

Selected Topics in Management and Modeling of Complex Systems: Editorial Introduction to Issue 15 of CSIMQ

The 15th issue of CSIMQ presents articles discussing management and modeling aspects of informatics research. The provided models and their application help to understand the domains under discussion. The models range from socio-technical models over business process models to models of usable graphical interfaces. Readers might find a lot of interesting aspects in this broad spectrum of management and modeling research. The published articles support management and modeling of complex systems in different ways on different levels of abstraction. The research results reflected in the articles can help to fill knowledge gaps and facilitate decision making in project management, business process management, very complex domain analysis, and data-intensive cyber-physical systems engineering.

Engineering industrial cyber-physical systems: An application map based method

The extensive digitization in industry and the advancing introduction of cyber-physical systems (CPS) in manufacturing offer a wide-ranging potential for industrial value creation. To ensure effective engineering processes of these systems, technological and organizational challenges need to be addressed systematically. Thereto, we develop a method for the systematic engineering of industrial CPS in this paper. The operational framework of the suggested method provides a guided process for (1) the selection of application fields for industrial CPS under the consideration of economic and technological necessities as well as (2) guidelines for the subsequent system configuration with components and functionalities.

Fundamental Limits of Cyber-Physical Systems Modeling

This article examines the role of modeling in the engineering of cyber-physical systems. It argues that the role that models play in engineering is different from the role they play in science, and that this difference should direct us to use a different class of models, where simplicity and clarity of semantics dominate over accuracy and detail. I argue that determinism in models used for engineering is a valuable property and should be preserved whenever possible, regardless of whether the system being modeled is deterministic. I then identify three classes of fundamental limits on modeling, specifically chaotic behavior, the inability of computers to numerically handle a continuum, and the incompleteness of determinism. The last of these has profound consequences.

Engineering Methods and Tools for Cyber–Physical Automation Systems

Much has been published about potential benefits of the adoption of cyber–physical systems (CPSs) in manufacturing industry. However, less has been said about how such automation systems might be effectively configured and supported through their lifecycles and how application modeling, visualization, and reuse of such systems might be best achieved. It is vitally important to be able to incorporate support for engineering best practice while at the same time exploiting the potential that CPS has to offer in an automation systems setting. This paper considers the industrial context for the engineering of CPS. It reviews engineering approaches that have been proposed or adopted to date including Industry 4.0 and provides examples of engineering methods and tools that are currently available. The paper then focuses on the CPS engineering toolset being developed by the Automation Systems Group (ASG) in the Warwick Manufacturing Group (WMG), University of Warwick, Coventry, U.K. and explains via an industrial case study how such a component-based engineering toolset can support an integrated approach to the virtual and physical engineering of automation systems through their lifecycle via a method that enables multiple vendors' equipment to be effectively integrated and provides support for the specification, validation, and use of such systems across the supply chain, e.g., between end users and system integrators.

Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring.

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

The past, present and future of cyber-physical systems: a focus on models.

This paper is about better engineering of cyber-physical systems (CPSs) through better models. Deterministic models have historically proven extremely useful and arguably form the kingpin of the industrial revolution and the digital and information technology revolutions. Key deterministic models that have proven successful include differential equations, synchronous digital logic and single-threaded imperative programs. Cyber-physical systems, however, combine these models in such a way that determinism is not preserved. Two projects show that deterministic CPS models with faithful physical realizations are possible and practical. The first project is PRET, which shows that the timing precision of synchronous digital logic can be practically made available at the software level of abstraction. The second project is Ptides (programming temporally-integrated distributed embedded systems), which shows that deterministic models for distributed cyber-physical systems have practical faithful realizations. These projects are existence proofs that deterministic CPS models are possible and practical.

Engineering Cyber Physical Systems: Machine Learning, Data Analytics and Smart Systems Architecting Preface

Engineering Cyber Physical Systems: Machine Learning, Data Analytics and Smart Systems Architecting Preface

Challenges in Engineering Cyber-Physical Systems

To successfully make the transition from traditional physical products to next-generation cyber-physical systems, many manufacturers will have to reinvent their innovation and development processes and take a user-centric engineering approach.

High-confidence cyber-physical co-design

Cyber-physical systems (CPS) [4] are characterized by combining computations, networks, and physical processes. Engineering cyber-physical systems is not new; high-end automobiles have for decades included complex embedded systems that interact with the physical environment. As an intellectual discipline, however, CPS design poses both new opportunities and challenges. The rapid development of a CPS with high-confidence of its functional correctness is a co-design problem ---the design of the cyber part (embedded control systems and networks) and the physical part influence each other. For instance, when designing an industrial robot, the thickness of the robot arms changes the physical behavior of the system; thinner arms have less inertia and can move faster, but introduce more flexibility and spring behavior, making the control algorithm harder to design. As a consequence, to meet increasingly challenging systemlevel objectives, the cyber and physical parts need to be concurrently designed.