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The research of energy consumption in an Internet of Things network and its analytical evaluation is the goal of this work. The authors of this work concentrate on developing a model for calculating the actual gain in power consumption in order to estimate the actual energy required. The method suggests measuring the difference in energy usage under three primary battery-powered working modes to maximize a device's lifetime. Due to the fact that each CPS device state has its own energy metrics, it is feasible to choose the best operation course for entire network. The presented technique is certainly viable, as demonstrated by the experimental examination of Zigbee and BLE devices. The comparison of power levels using a temperature sensor in three basic scenarios (power modes) dictates how the CPS device lifetime can be optimized. Multi-regime consumption models, in which the rates of charging and discharging are dependent upon the energy level, are analyzed in this paper. This work aimed to state an optimal energy consumption by finding the right balance between operational power and battery lifetime through mathematical modeling. Therefore, it is easy to determine the energy cost of power stage, for instance, to send data by setting the minimal duration of each working condition in terms of power consumption. Moreover, a reasonable balance of power consumption and battery lifetime which impacts the data collection from sensors is vital to the development of data extraction algorithms. The practical results depict how device should be accessible to be able to lose less power even during switching on/off or how operate more effective if it used for a short period of time. A long-term network could become a reality once battery life is optimized enough to not disturb a user.

The modern power system has evolved into a cyber-physical system with deep coupling of physical and information domains, which brings new security risks. Aiming at the problem that the “information-physical” cross-domain attacks with key nodes as springboards seriously threaten the safe and stable operation of power grids, a risk propagation model considering key nodes of power communication coupling networks is proposed to study the risk propagation characteristics of malicious attacks on key nodes and the impact on the system. First, combined with the complex network theory, a topological model of the power communication coupling network is established, and the key nodes of the coupling network are screened out by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method under the comprehensive evaluation index based on topological characteristics and physical characteristics. Second, a risk propagation model is established for malicious attacks on key nodes to study its propagation characteristics and analyze the state changes of each node in the coupled network. Then, two loss-causing factors: the minimum load loss ratio and transmission delay factor are constructed to quantify the impact of risk propagation on the coupled network. Finally, simulation analysis based on the IEEE 39-node system shows that the probability of node being breached (α) and the security tolerance of the system (β) are the key factors affecting the risk propagation characteristics of the coupled network, as well as the criticality of the node is positively correlated with the damage-causing factor. The proposed methodological model can provide an effective exploration of the diffusion of security risks in control systems on a macro level.

Owing to the integration of energy digitization and artificial intelligence technology, smart energy grids can realize the stable, efficient and clean operation of power systems. However, the emergence of cyber-physical attacks, such as dynamic load-altering attacks (DLAAs) has introduced great challenges to the security of smart energy grids. Thus, this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids. The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer. First, a data-driven method was proposed to predict the DLAA sequence in the cyber layer. By designing a double radial basis function network, the influence of disturbances on attack prediction can be eliminated. Based on the prediction results, an unknown input observer-based detection and localization method was further developed for the physical layer. In addition, an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs. Consequently, through the collaborative work of the cyber-physics layer, injected DLAAs were effectively detected and located. Compared with existing methodologies, the simulation results on IEEE 14-bus and 118- bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Systems that integrate between computers, communication and control systems are known as Cyber-Physical Systems (CPSs), and effective control of such systems is challenging. With advancement of the related areas and many systems are inter-connected, CPSs are becoming important elements especially in Industry 4.0 and 5.0. This paper presents a review of co-design approaches for control of CPS, in which desired performance in both layers (cyber and physical) are considered during the controller design. The approach is found to be suited well for CPS. Moreover, in this paper, a special focus is given to event-based control approaches as promising results are reported. In this case, event-based state-space and PID controllers are reviewed. In addition, tools used for the development and performance analyses of co-design approaches are discussed. Several control challenges related to CPS control have also been identified. It is envisaged that this paper can provide valuable knowledge for researchers to embark into the research of control of CPS.

The fundamental aim of this paper is to investigate and explore the design and simulation methods for a cyber-physical system (CPS) for the monitoring and control of drinking water capture, treatment and distribution processes. This research aims to provide a deep understanding of the theoretical concepts underlying CPS systems, as well as a detailed analysis of the technologies and methodologies used in the design and simulation of these complex systems. Through this investigation, we aim to identify effective solutions for the management of drinking water resources, given the critical importance of water in modern society. By analyzing case studies and current practices, we will assess the advantages and challenges associated with implementing a CPS system in the real-world context of water supply infrastructure. This work not only aims to provide a theoretical perspective on the concept of CPS applied in the field of drinking water supply, but also to contribute to the development and implementation of practical solutions in order to ensure universal access to quality water and to protect the environment. Through academic approach and rigorous research, we hope to make a contribution to the field of mechatronic engineering and environmental technologies.

Cyber-physical systems (CPSs) are affected by cyberattacks once they are more connected to cyberspace. Advanced CPSs are highly complex and susceptible to attacks such as false data injection attacks (FDIA) targeted to mislead the systems and make them unstable. Leveraging an integration of anomaly detection methods, real-time monitoring, and machine learning (ML) algorithms, research workers are developing robust frameworks to recognize and alleviate the effect of FDIA. These methods often scrutinize deviations from predictable system behavior, using statistical analysis and anomaly detection systems to determine abnormalities that can indicate malicious activities. This manuscript offers the design of an election-based optimization algorithm with a deep learning-enabled false data injection attack detection (EBODL-FDIAD) method in the CPS infrastructure. The purpose of the EBODL-FDIAD technique is to enhance security in the CPS environment via the detection of FDIAs. In the EBODL-FDIAD technique, the linear scaling normalization (LSN) approach can be used to scale the input data into valuable formats. Besides, the EBODL-FDIAD system performs ensemble learning classification comprising three classifiers, namely the kernel extreme learning machine (KELM), long short-term memory (LSTM), and attention-based bidirectional recurrent neural network (ABiRNN) model. For optimal hyperparameter selection of the ensemble classifiers, the EBO algorithm can be applied. To validate the enriched performance of the EBODL-FDIAD technique, wide-ranging simulations were involved. The extensive results highlighted that the EBODL-FDIAD algorithm performed well over other systems concerning numerous measures.

Cyber–physical systems (CPSs), which combine computer science, control systems, and physical elements, have become essential in modern industrial and societal contexts. However, their extensive integration presents increasing security challenges, particularly due to recurring cyber attacks. Therefore, it is crucial to explore CPS security control. In this review, we systematically examine the prevalent cyber attacks affecting CPSs, such as denial of service, false data injection, and replay attacks, explaining their impacts on CPSs' operation and integrity, as well as summarizing classic attack detection methods. Regarding CPSs' security control approaches, we comprehensively outline protective strategies and technologies, including event-triggered control, switching control, predictive control, and optimal control. These approaches aim to effectively counter various cyber threats and strengthen CPSs' security and resilience. Lastly, we anticipate future advancements in CPS security control, envisioning strategies to address emerging cyber risks and innovations in intelligent security control techniques. [ABSTRACT FROM AUTHOR]

One of the goals of developing and implementing Industry 4.0 solutions is to significantly increase the level of flexibility and autonomy of production systems. It is intended to provide the possibility of self-reconfiguration of systems to create more efficient and adaptive manufacturing processes. Achieving such goals requires the comprehensive integration of digital technologies with real production processes towards the creation of the so-called Cyber–Physical Production Systems (CPPSs). Their architecture is based on physical and cybernetic elements, with a digital twin as the central element of the "cyber" layer. However, for the responses obtained from the cyber layer, to allow for a quick response to changes in the environment of the production system, its virtual counterpart must be supplemented with advanced analytical modules. This paper proposes the method of creating a digital twin production system based on discrete simulation models integrated with deep reinforcement learning (DRL) techniques for CPPSs. Here, the digital twin is the environment with which the reinforcement learning agent communicates to find a strategy for allocating processes to production resources. Asynchronous Advantage Actor–Critic and Proximal Policy Optimization algorithms were selected for this research. [ABSTRACT FROM AUTHOR]

This work presents a resilient distributed optimization algorithm based on the event-triggering mechanism for cyber–physical systems (CPSs) to optimize an average of convex cost functions corresponding to multiple agents under adversarial environments. Two attack scenarios, including the f -total (each agent is affected by at most f malicious agents in the whole network) and the f -local (each agent is affected by at most f malicious agents in its in-neighbor set) attacks are considered. Subsequently, the convergence conditions under these two attack scenarios are provided, respectively, both of which guarantee that the state values of benign agents converge to a bounded error range. The optimality conditions are also presented by theoretical analysis, which guarantee that the state values of benign agents converge to a safety interval constructed by local optimal values under certain graph conditions, despite the misbehavior of malicious agents. In addition, four numerical examples are presented to show the effectiveness and superiority of the event-triggering resilient distributed optimization (RDO-E) algorithm. Compared to existing resilient algorithms, the proposed method achieves resilient distributed optimization with higher accuracy and less demanding communication overheads. Finally, by applying the proposed method to the multi-microgrid system, a resilient economic dispatch problem (REDP) is successfully solved, which validates the practical viability of the RDO-E algorithm. • An event-based resilient distributed optimization (RDO-E) algorithm is developed. • The convergence and optimality analysis are conducted under different attack scenarios • The RDO-E algorithm outperforms existing algorithms in accuracy and communication resource saving. • The RDO-E algorithm is applicable to practical muli-microgrid systems. [ABSTRACT FROM AUTHOR]

The research of energy consumption in an Internet of Things network and its analytical evaluation is the goal of this work. The authors of this work concentrate on developing a model for calculating the actual gain in power consumption in order to estimate the actual energy required. The method suggests measuring the difference in energy usage under three primary battery-powered working modes to maximize a device’s lifetime. Due to the fact that each CPS device state has its own energy metrics, it is feasible to choose the best operation course for entire network. The presented technique is certainly viable, as demonstrated by the experimental examination of Zigbee and BLE devices. The comparison of power levels using a temperature sensor in three basic scenarios (power modes) dictates how the CPS device lifetime can be optimized. Multi-regime consumption models, in which the rates of charging and discharging are dependent upon the energy level, are analyzed in this paper. This work aimed to state an optimal energy consumption by finding the right balance between operational power and battery lifetime through mathematical modeling. Therefore, it is easy to determine the energy cost of power stage, for instance, to send data by setting the minimal duration of each working condition in terms of power consumption. Moreover, a reasonable balance of power consumption and battery lifetime which impacts the data collection from sensors is vital to the development of data extraction algorithms. The practical results depict how device should be accessible to be able to lose less power even during switching on/off or how operate more effective if it used for a short period of time. A long-term network could become a reality once battery life is optimized enough to not disturb a user. [ABSTRACT FROM AUTHOR]

In the industrial revolution 4.0 the applicability of cyber physical systems (CPS) extensively increased in various sectors including logistics, health, and manufacturing. The integration of CPS in logistics has revolutionized the way goods are transported, managed, and monitored. CPS combines digital and physical components to enhance the efficiency and effectiveness of logistics operations. However, to ensure the reliable and continuous operation of CPS, optimizing the availability of its subsystems is paramount. Hence, in present study, an effort is made to explore the applicability of metaheuristic approaches in the performance evaluation of cyber physical systems and a comprehensive evaluation framework is also proposed to compare the performance of metaheuristic algorithms. For this purpose, a Markov model of cyber physical system developed by considering constant failure and repair rates for all components. The failure and repair rates are followed exponential distribution. The concept of cold standby redundancy is utilized for two components including analog components and sensors & actuators unit. All components of cyber physical system can face failure during the working process and are perfectly repairable. The mathematical expression of system is derived and optimized by using metaheuristic approaches namely grey wolf optimization (GWO), cuckoo search algorithm (CS), dragonfly optimization (DA), grasshopper optimization algorithm (GOA) and cat swarm optimization (CSO). The numerical expression of the system availability obtained at various population sizes and estimated parameters derived. It is revealed from the numerical investigation that GWO and CSO outperform all optimization techniques. GWO attains its maximum availability 0.9983290 at population size 140 over 500 iterations and CSO attains its maximum availability 0.9983297 at population size 120 over 1000 iterations. The results of present study provide valuable insights into the strengths and weaknesses of each metaheuristic approach in the context of CPS subsystem optimization. This comparative analysis can guide logistics professionals and researchers in selecting the most suitable optimization algorithm for their specific CPS applications. [ABSTRACT FROM AUTHOR]

This paper presents a modular hardware control system tailored for industrial applications. The system presented is designed with electrical protection, guaranteeing the reliable operation of its modules in the presence of various field noises and external disturbances. The modular architecture comprises a principal module (mP) and dedicated expansion modules (mEXs). The principal module serves as the network administrator and facilitates interaction with production and control processes. The mEXs are equipped with sensors, conditioning circuits, analog-to-digital converters, and digital signal processing capabilities. The mEX's primary function is to acquire local processing field signals and ensure their reliable transmission to the mP. Two specific mEXs were developed for industrial environments: an electrical signal expansion module (mSE) and the vibration signals expansion module (mSV). The EtherCAT protocol serves as a means of communication between the modules, fostering deterministic and real-time interactions while also simplifying the integration and replacement of modules within the modular architecture. The proposed system incorporates local and distributed processing in which data acquisition, processing, and data analysis are carried out closer to where data are generated. Locally processing the acquired data close to the production in the mEX increases the mP availability and network reliability. For the local processing, feature extraction algorithms were developed on the mEX based on a Fast Fourier Transform (FFT) algorithm and a curve-fitting algorithm that accurately represents a given FFT curve by significantly reducing the amount of data that needs to be transmitted over the mP. The proposed system offers a promising solution to use computational intelligence methodologies and meet the growing need for a modular industrial control system with reliable local data processing to reach a smart industry. The case study of acquiring and processing vibration signals from a real cement ball mill showed a good capacity for processing data and reducing the amount of data. [ABSTRACT FROM AUTHOR]

This study outlines the assessment of the cyber–physical system SPERTA, which was designed to evaluate the real-time performance of Taekwondo athletes. The system conducts performance analyses focusing on speed, acceleration, strength, and identifying and quantifying the athlete's movements. The research involved administering an online questionnaire to athletes and coaches to evaluate the system's acceptance and usability. The methodology included using a questionnaire with open and closed questions to assess participant satisfaction and system usability. The results showed a positive response to the system, with participants emphasizing its reliability and ease of use. An analysis of the responses revealed a strong internal consistency, as indicated by the Cronbach's alpha coefficient, which enhances the research instrument's reliability. Additionally, the analysis of open-ended questions was conducted through thematic analysis to gain a deeper understanding of participants' experiences and perceptions of the system. These results highlight the effectiveness of the SPERTA system as a valuable tool for the real-time performance evaluation of Taekwondo athletes, providing insights for future improvements and the development of more effective training techniques. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPSs) are affected by cyberattacks once they are more connected to cyberspace. Advanced CPSs are highly complex and susceptible to attacks such as false data injection attacks (FDIA) targeted to mislead the systems and make them unstable. Leveraging an integration of anomaly detection methods, real-time monitoring, and machine learning (ML) algorithms, research workers are developing robust frameworks to recognize and alleviate the effect of FDIA. These methods often scrutinize deviations from predictable system behavior, using statistical analysis and anomaly detection systems to determine abnormalities that can indicate malicious activities. This manuscript offers the design of an election-based optimization algorithm with a deep learning-enabled false data injection attack detection (EBODL-FDIAD) method in the CPS infrastructure. The purpose of the EBODL-FDIAD technique is to enhance security in the CPS environment via the detection of FDIAs. In the EBODL-FDIAD technique, the linear scaling normalization (LSN) approach can be used to scale the input data into valuable formats. Besides, the EBODL-FDIAD system performs ensemble learning classification comprising three classifiers, namely the kernel extreme learning machine (KELM), long short-term memory (LSTM), and attention-based bidirectional recurrent neural network (ABiRNN) model. For optimal hyperparameter selection of the ensemble classifiers, the EBO algorithm can be applied. To validate the enriched performance of the EBODL-FDIAD technique, wide-ranging simulations were involved. The extensive results highlighted that the EBODL-FDIAD algorithm performed well over other systems concerning numerous measures. [ABSTRACT FROM AUTHOR]

Smart agriculture modifies traditional farming practices, and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies. In today's world where technology is everything, these technologies are utilized to streamline regular tasks and procedures in agriculture, one of the largest and most significant industries in every nation. This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems. Divided into three main sections--security analysis, system architecture and design and risk assessment of Cyber-Physical Systems (CPS) applications--the study delves into various elements crucial for smart farming, such as data sources, infrastructure components, communication protocols, and the roles of different stakeholders such as farmers, agricultural scientists and researchers, technology providers, government agencies, consumers and many others. In contrast to earlier research, this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling, penetration testing, and vulnerability assessments. Important discoveries highlight the concerns connected to unsecured communication protocols, possible threats from malevolent actors, and vulnerabilities in IoT devices. Furthermore, the study suggests enhancements for CPS applications, such as strong access controls, intrusion detection systems, and encryption protocols. In addition, risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards, addressing issues like data breaches, system outages, and automated farming process sabotage. The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor. This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards. The CPS application's defenses against potential threats and vulnerabilities are strengthened by this integrated approach, which distinguishes this research for its depth and usefulness in the field of smart agriculture security. [ABSTRACT FROM AUTHOR]

Digital twin (DT) technology has been a topic with academic and industrial coverage in recent years. DTs are intended to be a virtual high‐fidelity representation of a physical counterpart. Its complex nature requires several components to create and run a DT, and that is why many DT frameworks have been proposed in the literature. There are also many surveys of DTs, but none that is bottom‐up with concrete examples and focused on open‐source software. This survey analyzes 14 open‐source DT frameworks in 10 different dimensions, which are then categorized in six different groups according to their modeling and technological domain, to present the reader different options for creating and managing DT applications, and to understand potential combinations, uses, and limitations of the tools. It also presents a case study with five of the explored DT frameworks, describing the process on how the DT is set up and comparing their capabilities based on the services to be provided by the DT. Finally, it discusses advantages and limitations of the tools according to domain, requirements, and scope, relevant aspects regarding built‐in simulations and data analytics, theory‐to‐practice transition, and advantages/disadvantages of using open‐source software instead of commercial. Main limitations of the study due to its narrow niche, conclusions, and opportunities for future research regarding the potential room for improvement in terms of out‐of‐the‐box features and services for DTs, are also shown. [ABSTRACT FROM AUTHOR]

This article investigates the security ensured state estimation problem for cyber‐physical system via a data‐driven method. First, based on the fact that different packets possess varying a degree of significance, that is, some packets play more important roles in the state estimation than others, a novel stochastic important‐data‐based (IDB) attack mechanism is constructed from the attacker's perspective, which can focus on attacking the important packets thus is expected to achieve more destructiveness. Second, as a countermeasure to the proposed IDB attack, a new data‐driven compensation method is proposed, for the first attempt, to compensate for the attack effect and enhance the estimation quality. The designed defense strategy has the following two advantages: (1) only system input and output data are utilized to establish the novel estimator, without knowing the actual system model knowledge, and (2) by constructing a data‐driven output predictor to compensate for the data loss, the accuracy of the state estimation can be efficiently improved. With the aid of the least squares technique and completing square technique, a minimum upper bound matrix for the estimation error covariance is obtained by properly designing the estimator gain. Finally, an illustrative example is given to highlight the destructiveness of the designed stochastic IDB attack and the effectiveness of the proposed novel data‐based compensation method. [ABSTRACT FROM AUTHOR]

One primary objective of drone simulation is to evaluate diverse drone configurations and contexts aligned with specific user objectives. The initial challenge for simulator designers involves managing the heterogeneity of drone components, encompassing both software and hardware systems, as well as the drone's behavior. To facilitate the integration of these diverse models, the Functional Mock-Up Interface (FMI) for co-simulation proposes a generic data-oriented interface. However, an additional challenge lies in simplifying the configuration of co-simulation, necessitating an approach to guide the modeling of parametric features and operational conditions such as failures or environment changes. The article addresses this challenge by introducing CARES, a model-driven engineering and component-based approach for designing drone simulators, integrating the FMI for co-simulation. The proposed models incorporate concepts from component-based software engineering and FMI. The NAVIDRO architectural style is presented for designing and configuring drone co-simulation. CARES utilizes a code generator to produce structural glue code (Java or C++), facilitating the integration of FMI-based domain-specific code. The approach is evaluated through the development of a simulator for navigation functions in an autonomous underwater vehicle, demonstrating its effectiveness in assessing various autonomous underwater vehicle configurations and contexts. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPSs) are characterized by their integration of physical processes with computational and communication components. These systems are utilized in various critical infrastructure sectors, including energy, healthcare, transportation, and manufacturing, making them attractive targets for cyberattacks. Intrusion detection system (IDS) has played a pivotal role in identifying and mitigating cyber threats in CPS environments. Intrusion detection in secure CPSs is a critical component of ensuring the integrity, availability, and safety of these systems. The deep learning (DL) algorithm is extremely applicable for detecting cyberattacks on IDS in CPS systems. As a core element of network security defense, cyberattacks can change and breach the security of network systems, and then an objective of IDS is to identify anomalous behaviors and act properly to defend the network from outside attacks. Deep learning (DL) and Machine learning (ML) algorithms are crucial for the present IDS. We introduced an Equilibrium Optimizer with a Deep Recurrent Neural Networks Enabled Intrusion Detection (EODRNN-ID) technique in the Secure CPS platform. The main objective of the EODRNN-ID method concentrates mostly on the detection and classification of intrusive actions from the platform of CPS. During the proposed EODRNN-ID method, a min-max normalization algorithm takes place to scale the input dataset. Besides, the EODRNN-ID method involves EO-based feature selection approach to choose the feature and lessen high dimensionality problem. For intrusion detection, the EODRNN-ID technique exploits the DRNN model. Finally, the hyperparameter related to the DRNN model can be tuned by the chimp optimization algorithm (COA). The simulation study of the EODRNN-ID methodology is verified on a benchmark data. Extensive results display the significant performance of the EODRNN-ID algorithm when compared to existing techniques. [ABSTRACT FROM AUTHOR]

The increasing personalized product demands bring reformation to the manufacturing paradigm. Traditional manufacturing systems seldom analyze and give feedback on the data collected during production. The bottleneck between the physical and digital worlds of manufacturing systems is the lack of interoperability. In this paper, a digital twin-based self-organizing manufacturing system (DT-SOMS) is presented under the individualization paradigm. On the basis of the interconnection between smart workpieces and smart resources via decentralized digital twin models, a decentralized self-organizing network is established to achieve intelligent collaboration between tasks and resources. The mechanism of job-machine optimal assignment and adaptive optimization control is constructed to improve the capabilities of reconfiguration and responsiveness of the DT-SOMS. An implement case is designed to illustrate that the proposed DT-SOMS can realize synchronized online intelligence in the configuration of resources and response to disturbances. [ABSTRACT FROM AUTHOR]

The increase in port scale and business complexity has led to an increased demand for comprehensive and lean control on ports. The current operation mode is facing the bottleneck of the increasingly significant production efficiency and performance. Digital twin (DT) technology realizes holographic visual management and control patterns using cyber-physical fusion and promotes the transformation of a port to an intelligent operation mode. In this paper, the framework of a digital twin application system is proposed based on the analysis of business characteristics of large-scale comprehensive ports. Construction methods and technologies such as digital twin modeling, global ubiquitous perception, data mapping, and model fusion are analyzed. With regard to the construction needs of Qingdao Port's digital twin system, this paper presents a case study and illustrates the overall design process and function of the digital twin system for typical terminals. The system realizes the intelligent operation of the port with the core functions of three-dimensional visual monitoring and optimal dispatching based on real-time perception data. This paper serves as a feasible reference for future intelligent development of large ports and the application of digital twin technology. [ABSTRACT FROM AUTHOR]

Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative adaptive nonsingular Second-Order Terminal Sliding Mode (SOTSM) control strategy specifically designed to stabilise disturbed nonlinear Cyber-Physical Systems (CPSs) within a finite timeframe. Unlike the conventional methods, our approach incorporates a novel nonlinear sliding surface that eliminates the need for a reaching step, significantly enhancing the system's robustness. Novel real-time adaptive control laws are developed to cope with external perturbations, time-varying delays, and cyber-attack on actuators without requiring the identification of their upper bounds. The proposed method ensures robust performance for time-varying delayed and disturbed nonlinear CPSs, even in the face of strong cyber-attacks targeting the actuators. Moreover, the proposed method has distinct advantages: it delivers rapid response times, enhanced flexibility, exceptional accuracy, smooth and robust control devoid of transient fluctuations or chattering, and ensures finite-time convergence. Simulation results comprehensively demonstrate the superior efficiency and success of our proposed technique compared to traditional methods such as integral Sliding Mode Control (SMC) and State-Feedback Control (SFC) schemes. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for transferring the entire set of low-level control systems from one robot bus, i.e., an autonomous vehicle (AV) shuttle, to another with distinct electronics and mechanical specifications. The research entails a series of experiments to assess the reliability and safety of the AV shuttle after integrating the critical control systems responsible for steering, accelerating, and braking into the target shuttle. The ultimate goal is to meet the necessary requirements for registering the target AV shuttle as a legal vehicle on the roads in Estonia. Consequently, several crucial tests of the shuttle's low-level control system were conducted, e.g., intentionally disconnecting different subsystems to simulate sudden failures and evaluate whether the shuttle responds in accordance with the appropriate protocols. As a case study, the upgraded autonomous shuttle was tested on the streets of Tallinn. The most relevant findings are introduced in the second part of this paper. The outcomes of the study demonstrate the feasibility of seamlessly transferring low-level control systems between various models of autonomous shuttles, eliminating the risk of encountering safety or reliability issues. [ABSTRACT FROM AUTHOR]

The role of Cyber-Physical systems (CPS) is well recognized in the context of Industry 4.0, which consists of human operators working with machines/robots. The interactions among them can be quite demanding in terms of cognitive resources. Existing systems do not yet consider the psychological aspects of safety in the domain. This lack can lead to hazardous situations, thus compromising the performance of the working system. This work proposes a connective decision-making framework for a flexible CPS, which can quickly respond to dynamic changes and be resilient to emergent hazards. First, Anxiety is defined and categorized for expected/unforeseen situations that a CPS could encounter through historical data using the Ishikawa method. Second, visual cues are used to gather the CPS's current state (such as human pose and object identification). Third, a mathematical model is developed using Mixed-integer programming (MIP) to allocate optimal resources, to tackle high-impact situations generating Anxiety. Finally, the logic is designed for an effective counter-mechanism to mitigate Anxiety. The proposed method was tested on a realistic industrial scenario incorporating a collaborative CPS. The results demonstrated that the proposed method improves the decision-making of a CPS facing a complex scenario, ensures physical safety, and effectively enhances the human-machine team's productivity. [ABSTRACT FROM AUTHOR]

The integration of Digital Twins (DTs) is becoming increasingly important in various industries. This entails the need for a comprehensive and practical IT infrastructure framework. This paper presents a modifiable medium-level architecture that serves as a link between established reference architectures such as RAMI 4.0 and the pragmatic implementation of Digital Twins. The functionalities of an IT infrastructure are considered, and functional hardware and software components for fulfilling these are described. The proposed architecture is suitable for various deployment scenarios, including local, cloud and hybrid cloud–edge configurations. In order to improve the applicability of the medium-level architecture, a step-by-step procedure is also proposed, which helps to transfer the overarching requirements for a Digital Twin into a suitable IT infrastructure. Finally, the results are demonstrated by an exemplary application to a two-stage industrial gearbox. [ABSTRACT FROM AUTHOR]