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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.

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 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.

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.

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.

The fingerprint-based authentication systems are being extensively deployed as security tool for providing access to the critical Cyber Physical Systems (CPS). However, this rapid relocation to fingerprint-based recognition poses many security concerns that are primarily due to presentation or spoofing attacks on sensor module. To alleviate these attacks, presentation attack detection (PAD) mechanisms are developed that mainly rely on micro-textural properties of the fingerprint images. In this paper, we expound a novel intelligent fingerprint PAD (IFPAD) approach for securing typical CPS that exploits two micro-textural features from an image. We propose a new Local Adaptive Binary Patterns (LABP) descriptor that extracts more refined local information by using dynamically adapted threshold. Moreover, to augment more discriminative power, the Uniform Local Binary patterns (ULBP) descriptor is coalesced with our LABP feature. To yield better detection accuracy, an Adaptive Boosting (AdaBoost) ensemble is created on two base estimators using Support Vector Machine (SVM) that learns LABP and ULBP features. The IFPAD is experimentally evaluated on three benchmark datasets; LivDet 2009, LivDet 2011, and LivDet 2013 where an average classification error rate (ACER) of 4.23%, 3.83% and 3.57% is achieved respectively. In addition, the promising performance of IFPAD in case of cross-database and cross-sensor scenario confirms its good generalization capabilities in unknown environment. Besides, overall analysis of the proposed IFPAD demonstrates superiority as compared to both handcrafted and deep learning-based state-of-the-art PAD methods.

Industry 5.0 and Healthcare 5.0 converge towards a human centered society, having technological advancement as a lever. In Society 5.0, decentralized autonomous cities and a convergence of physical and cyberspace are the foundations of the new chapter of society’s development. The idea of creating digital replicas and legitimate representatives of human beings in cyberspace has become a pillar of digitalization. Society 5.0 introduces Human Digital Twins as a central element of Cyber Physical Systems that include human factors or are designed to interact with humans in a personalized fashion. Overall, the HDT and neighboring concepts are applied to depict how humans can be represented in a cyberspace. However, there are clear challenges in determining which human characteristics should take precedence, how much autonomy should be granted to HDTs to optimize their functionality and how to conceptualize the digital environment in which HDTs interact with various entities, including other digital agents and stakeholders with agency and decisional power. To harness similarities and differences of current approaches, we propose a classification of HDTs based on meta-characteristics and ethical implications. We discuss ethical implication by focusing on emerging risks and paradigm shifts and anchor the previous discussion in the vision for Society 5.0, questioning whether societal development relying on disruptive technologies, instead of leading to more human-centered societies might be driving humanized societies away from humanity.

Cyber Physical Systems (CPSs) play a crucial role in the Industry 4.0 paradigm. The application of CPSs in production and manufacturing environments gave rise to the term Cyber Physical Production Systems (CPPSs). There is a growing interest in CPPSs, yet research in this area is scattered and needs to be reviewed for understanding their development status and maturity. The aim of this study is to carry out a systematic literature review (SLR) to analyze the current research activities on CPPSs according to their contributions to the engineering life cycle of such production system. Firstly, a method for SLR is presented. Then, literature analysis of CPPSs is conducted to present research activities in the light of the concept development and engineering development stages. Finally, based on the results of the literature analysis, a concept map of CPPSs research is proposed, which depicts the existing research topics in the engineering life cycle of CPPSs. And we exploit it to propose a research agenda of the CPPSs integration process required to ensure their efficient industrial use. Findings of this review can help researchers to examine the maturity of the development status of CPPSs, to discover which phases require improvement, and to know the future research directions for their industrial practices.

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.