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Cyber-physical systems (CPS) have gained an increasing attention recently for their immense potential towards the next generation smart systems that integrate cyber technology into the physical processes. However, CPS did not initiate either smart factories or smart manufacturing, and vice versa. Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0. Also emerging are other related models such as cloud manufacturing, social manufacturing and proactive manufacturing with the introduction of cloud computing (broadly, the Internet of Services, IoS), social networking (broadly, the Internet of People, IoP) and big data (broadly, the Internet of Content and Knowledge, IoCK), respectively. At present, there is a lack of a systemic and comprehensive study on the linkages and relations between these terms. Therefore, this study first presents a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS. Then, the paper addresses CPS-based smart manufacturing as an eight tuple of CPS,IoT, IoS and IoCK as elements. Further, the paper extends the eight-tuple CPS-based manufacturing to social-CPS (SCPS)-based manufacturing, termed wisdom manufacturing, which forms a nine tuple with the addition of one more element, the IoP and which is based on the SCPS instead of CPS. Both architectures and characteristics for smart and wisdom manufacturing are addressed. As such, these terms’ linkages are established and relations are clarified with a special discussion. This study thus contributes as a theoretical basis and as a comprehensive framework for emerging manufacturing integration.

With increasing diverse product demands, the manufacturing paradigm has been transformed into a mass-individualized one, among which one bottleneck is to achieve the interoperability between physical world and the digital world of manufacturing system for the intelligent organizing of resources. This paper presents a digital twin-driven manufacturing cyber-physical system (MCPS) for parallel controlling of smart workshop under mass individualization paradigm. By establishing cyber-physical connection via decentralized digital twin models, various manufacturing resources can be formed as dynamic autonomous system to co-create personalized products. Clarification on the MCPS concept, characteristics, architecture, configuration, operating mechanism and key enabling technologies are elaborated, respectively. A demonstrative implementation of the digital twin-driven parallel controlling of board-type product smart manufacturing workshop is also presented. It addresses a bi-level online intelligence in proactive decision making for the organization and operation of manufacturing resources.

With the development of synchronous measuring technology and communication technology, the units of measurement, calculation, execution and communication are deeply integrated into energy manage system, which can achieve panoramic state awareness through the fast and accurate state estimation algorithm. Meanwhile, the cyber-attack has become an important issue posing severe threats to the secure operation of power systems. A well-designed false data injection attack (FDIA) against state estimation can effectively bypass the traditional bad data detection methods and interfere with the decision of the control centre, thus causing the power system incidents. This study comprehensively discusses the characteristics of FDIA including not only the goals, construction methods and consequences of FDIA from the perspective of attackers but also the protection and detection countermeasures from the perspective of defenders. Moreover, a game-theory-based FDIA against the substation information network is simulated to reveal the interactions between attackers and defenders.

The recent pandemic has demanded a strong and smart healthcare system which can monitor the patients efficiently and handle the situation that arises from the outbreak of the disease. Smart healthcare cyber physical systems are the future systems as they integrate the physical and cyber world for efficient functioning of medical processes and treatment through external monitoring and control of patients, medical devices and equipment for continuous communication and information exchange of physiological data. Technologies like Internet of Things, Machine learning and Artificial Intelligence have given birth to smart cyber physical systems like Smart Healthcare Systems, Smart Homes, Smart Vehicular Systems and Smart Grid. Such systems are interdisciplinary in nature with multitude of technologies contributing to its effective working. This paper presents a case study on healthcare cyber physical systems presenting its characteristics, role of various technologies in its growth and major challenges in successful implementation of cyber physical medication systems.

This paper is concerned with optimal security problem of cyber‐physical systems with state attacks. Consider a linear physical system, assuming that the control input signal of the network layer is vulnerable to state attack. For a system that cannot measure all states, by combining output integral sliding mode, robust observer, and adaptive methods, an adaptive output integral sliding mode control method is proposed to maintain the safe operation of cyber physical systems under state attacks. Moreover, by optimizing the linear control gain matrix, the control strategy the authors designed can use the minimum cost to compensate the impact on the CPS system. Different from the existing results, (1) It is assumed that the system information of the attack signal is not fully known, we assume that the attacker can only measure a small amount of state information. (2) Not only the stability of ideal sliding mode is proved, but also the upper bound of the augmented state composed of system states and the error dynamic is given. (3) The sliding mode compensator can eliminate the impact of state attack, on the one hand, the damage caused by the state attack has been eliminated by using the upper bound of error, and on the other hand, the cost is reduced by optimizing the linear control gain. Finally, a power system with 3 generators and 6 buses is used to prove the effectiveness of the adaptive output integral sliding mode control scheme. This paper focuses on the security problem of cyber‐physical systems with state attacks. Consider a linear physical system, assuming that the control input signal of the network layer is vulnerable to cyber attacks. For a system that cannot measure all states, by combining output integral sliding mode, robust observer, and adaptive methods, an adaptive output integral sliding mode control method is proposed to maintain the safe operation of cyber physical systems under process attacks. Moreover, by using optimal control theory, the linear control law can minimize the upper bound of system performance.

With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined.

As part of the fourth industrial revolution, the movement to apply various enabling technologies under the name of Industry 4.0 is being promoted worldwide. Because of the wide range of applications and the capacity of manufacturing workpieces flexibly, machine tools are regarded as essential industrial elements. Hence, much research has been concerned with applying various enabling technologies such as cyber-physical systems to machine tools. To realize a machine tool suitable for Industry 4.0, development should be done in a systematic manner rather than the ad-hoc application of enabling technologies. In this paper, we propose a functional architecture for the Industry 4.0 version of machine tools, namely smart machine tool system. To reflect the voices of various stakeholders, stakeholder requirements are identified and transformed into design considerations. The design considerations are incorporated into the conceptual model and functional modeling, both of which are used to derive the functional architecture. The implementation procedure and an illustrative case study are presented for the application of the functional architecture.

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.

Natural disasters and cyber intrusions threaten the normal operation of the critical Multi‐Energy Systems (MESs) infrastructures. There is still no universally‐accepted definition of MESs resilience under the integration of cyber and physical, and lack of a widely accepted methodology to quantify and assess the resilience in MESs. Hence, this paper introduces an extensive review of the state‐of‐the‐art research of power systems’ resilience. Then, this work proposes the definition of the Multi‐Energy Cyber‐Physical Systems (MECPSs) resilience and its related characteristics. To improve the resilience of MECPSs, this paper investigates extreme natural disaster models and analyses the vulnerability of the system to find the key constraint factors. Furthermore, this work presents the qualitative assessment curve, quantitative indexes, and assessment framework of the MECPSs resilience. Finally, the key improvement measures for the planning and operation of MECPSs resilience and the focus of future research are presented.

Cloud services supported by the cyber-physical system (CPS) have been emerged as a proactive and efficient solution to facilitate the alliance of IoT devices. The CPS assimilates, analyses and shares the processed information among IoT applications. The traditional network infrastructure is not designed to support the increasing demand for high scalability and real-time delivery with ultra-low delay. The proposed system addresses these issues by utilising the optical resources as fog nodes and software-defined networking in the 5G network. A scalable OpticalFog node is proposed that creates the cyberspace near the IoT devices, thus providing an ultra-low delay, minimising the energy consumption. Moreover, for providing service assurance to the various CPS-based applications, an algorithm is proposed to place the task in the software-defined networking. Finally, the performance of the algorithm is evaluated through the simulation in iFogSim toolkit that is used to implement the OpticalFog node in the 5G environment. The results showed the effectiveness of the proposed system.