Engineering Cyber Physical Systems (original) (raw)
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Patterns for Self-Adaptation in Cyber-Physical Systems
Multi-Disciplinary Engineering for Cyber-Physical Production Systems, 2017
Engineering Cyber-Physical Systems (CPS) is challenging, as these systems have to handle uncertainty and change during operation. A typical approach to deal with uncertainty is enhancing the system with self-adaptation capabilities. However, realizing self-adaptation in CPS, and consequently also in Cyber-Physical Production Systems (CPPS) as a member of the CPS family, is particularly challenging due to the specific characteristics of these systems, including the seamless integration of computational and physical components, the inherent heterogeneity and large-scale of such systems, and their open-endedness. In this chapter we survey CPS studies that apply the promising design strategy of combining different self-adaptation mechanisms across the technology stack of the system. Based on the survey results, we derive recurring adaptation patterns that structure and consolidate design knowledge. The patterns offer problem-solution pairs to engineers for the design of future CPS and CPPS with self-adaptation capabilities. Finally, the chapter outlines the potential of Collective Intelligence Systems for CPPS and their engineering based on the survey results.
How to Deal with the Complexity of Future Cyber-Physical Systems?
Designs
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 awarene...
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AI & SOCIETY, 2020
This article conducts a literature review of current and future challenges in the use of artificial intelligence (AI) in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things (IoT) interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc.). We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-maki...
Science of design for societal-scale cyber-physical systems: challenges and opportunities
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Emerging industrial platforms such as the Internet of Things (IoT), Industrial Internet (II) in the US and Industrie 4.0 in Europe have tremendously accelerated the development of new generations of Cyber-Physical Systems (CPS) that integrate humans and human organizations (H-CPS) with physical and computation processes and extend to societal-scale systems such as traffic networks, electric grids, or networks of autonomous systems where control is dynamically shifted between humans and machines. Although such societalscale CPS can potentially affect many aspect of our lives, significant societal strains have emerged about the new technology trends and their impact on how we live. Emerging tensions extend to regulations, certification, insurance, and other societal constructs that are necessary for the widespread adoption of new technologies. If these systems evolve independently in different parts of the world, they will 'hardwire' the social context in which they are created, making interoperation hard or impossible, decreasing reusability, and narrowing markets for products and services. While impacts of new technology trends on social policies have received attention, the other side of the cointo make systems adaptable to social policiesis nearly absent from engineering and computer science design practice. This paper focuses on technologies that can be adapted to varying public policies and presents (1) hard problems and technical challenges and (2) some recent research approaches and opportunities. The central goal of this paper is to discuss the challenges and opportunities for constructing H-CPS that can be parameterized by social context. The focus in on three major application domains: connected vehicles, transactive energy systems, and unmanned aerial vehicles.
An Agent-Based Approach to Reconciling Data Heterogeneity in Cyber-Physical Systems
2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, 2011
Computing and communication devices in any cyber-physical system (CPS) of non-trivial scale exhibit significant heterogeneity. Critical infrastructure systems, which are prime examples of CPSs, are no exception. The extent of networking capability, decentralized control, and more generally, integration between the cyber and physical infrastructures can vary greatly within a large-scale CPS. Other manifestations of heterogeneity in CPSs are in the resolution, syntax, and semantics of data collected by sensors from the physical infrastructure. Similar challenges complicate the use of databases that maintain past sensor data, device settings, or information about the physical infrastructure. The work presented in this paper aims to address these challenges by using the summary schemas model (SSM), which enables heterogeneous data sources to be queried with an unrestricted view and/or terminology. This support for imprecise queries significantly broadens the scope of data that can be used for intelligent decision support and carries the promise of increased reliability and performance for the CPS. We seek to ensure that ambiguity and imprecision do not accompany this expanded scope. The ultimate goal of a CPS is to fortify and streamline the operation of its physical infrastructure. The success of this task is contingent upon correct and efficient interpretation of data describing the state of the physical components, and the constraints to which it is subject. To this end, we propose agentbased semantic interpretation services that extract meaningful and useful information from raw data from heterogeneous sources, aided by the SSM. The proposed approach is described in the context of intelligent water distribution networks, which are cyber-physical critical infrastructure systems responsible for reliable delivery of potable water. The methodology is general, and can be extended to a broad range of CPSs, including smart power grids and intelligent transportation systems.
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Schaffer and Land 14 described a method whereby a machine intelligence (MI) process can “know what it doesn’t know.” In this paper, the concept is illustrated by three examples: the GRNN oracle ensemble method that combines multiple SVM classifiers for detecting Alzheimer’s type dementia using features automatically extracted from a speech sample, an Evolutionary Programming and Adaptive Boosting hybrid and a Generalized Regression Neural Network hybrid for classifying breast cancer. The authors assert it is (1) applicable quite directly to a great many other learning classifier systems, and (2) provides an intuitive approach to comparing the performance of different classifiers on a given task using the size of the “area of uncertainty” as a measure of performance metric. This paper provides support for these assertions by describing the steps needed to apply it to a previously published study of breast cancer benign / malignancy prediction, and then illustrates how this “area of u...
A Preliminary Study on Architecting Cyber-Physical Systems
Proceedings of the 2015 European Conference on Software Architecture Workshops, 2015
Cyber-physical systems (CPSs) are deemed as the key enablers of next generation applications. Needless to say, the design, verification and validation of cyber-physical systems reaches unprecedented levels of complexity, specially due to their sensibility to safety issues. Under this perspective, leveraging architectural descriptions to reason on a CPS seems to be the obvious way to manage its inherent complexity. A body of knowledge on architecting CPSs has been proposed in the past years. Still, the trends of research on architecting CPS is unclear. In order to shade some light on the state-of-the art in architecting CPS, this paper presents a preliminary study on the challenges, goals, and solutions reported so far in architecting CPSs.
Phoenix : Complex Adaptive System of Systems (CASoS) engineering version 1.0
2011
Complex Adaptive Systems of Systems, or CASoS, are vastly complex ecological, sociological, economic and/or technical systems which we must understand to design a secure future for the nation and the world. Perturbations/disruptions in CASoS have the potential for far-reaching effects due to pervasive interdependencies and attendant vulnerabilities to cascades in associated systems. Phoenix was initiated to address this high-impact problem space as engineers. Our overarching goals are maximizing security, maximizing health, and minimizing risk. We design interventions, or problem solutions, that influence CASoS to achieve specific aspirations. Through application to real-world problems, Phoenix is evolving the principles and discipline of CASoS Engineering while growing a community of practice and the CASoS engineers to populate it. Both grounded in reality and working to extend our understanding and control of that reality, Phoenix is at the same time a solution within a CASoS and a CASoS itself. ACKNOWLEDGEMENTS Following the favorable reception of the Sandia National Laboratories A Roadmap for the
Empowering cyberphysical systems of systems with intelligence
2021
Cyber Physical Systems have been going into a transition phase from individual systems to a collecttives of systems that collaborate in order to achieve a highly complex cause, realizing a system of systems approach. The automotive domain has been making a transition to the system of system approach aiming to provide a series of emergent functionality like traffic management, collaborative car fleet management or large-scale automotive adaptation to physical environment thus providing significant environmental benefits (e.g air pollution reduction) and achieving significant societal impact. Similarly, large infrastructure domains, are evolving into global, highly integrated cyber-physical systems of systems covering all parts of the value chain. In practice, there are significant challenges in CPSoS applicability and usability to be addressed, i.e. even a small CPSoS such as a car consists several subsystems Decentralization of CPSoS appoints tasks to individual CPSs within the Syst...
Outlining Nine Major Design Challenges of Open, Decentralized, Adaptive Cyber-Physical Systems
Volume 2B: 33rd Computers and Information in Engineering Conference, 2013
Open, decentralized, adaptive cyber-physical systems (ODA-CPSs) have countless novel structural attributes and functional affordances. Consequently, they pose many design and engineering challenges. This paper identifies and analyzes nine of them. They are: (i) handling aggregative complexity, (ii) establishing static and dynamic compositional synergy, (iii) managing dynamic and evolutionary operation in time, (iv) multi-abstraction-based modeling, (v) system integrity verification and behavior validation, (vi) achieving dynamic scalability towards meta-systems, (vii) transformation of big data, (viii) employing testable surrogate prototyping, and (ix) attaining robust social compliance. These challenges should be addressed already in the course of conceptualization and design of these systems. It is shown that a kind of duality is hiding practically in each of these challenges, which are caused by the concurrence of short term dynamic behavior and long term evolution of ODA-CPSs. Though interrelated, these two aspects still need to be handled separate. In order to response effectively to the above challenges, foundational research and operative research need to produce new transdisciplinary insights and new practical principles, respectively. First, previous efforts in these dimensions are critically evaluated. Then it is circumscribed what new knowledge is needed in order to cope with the considered major challenges. Putting everything together, the paper concludes that the grand challenge is in the lack of a dedicated transdisciplinary design theory that could explain how ODA-CPSs should be ideated and synthesized, and that would allow the development of a comprehensive design methodology and computational support tools. Future research will attempt to propose concrete solutions for the discussed challenges and most probably identify other emerging ones.