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This work describes the collaboration practices of a community of interest in the UK that brings together cybersecurity professionals with a shared interest in improving supply chain cybersecurity for Operational Technology (OT) environments. This research emphasizes the need for collective responsibility between organizations and provides a set of principles for adopting a code of practice and partnership approach to supply chain cybersecurity. This work has enabled cybersecurity experience from several critical infrastructure sectors, including energy, rail, aviation, water, health, and food, to analyze the uptake and practical use of existing supply chain guidance, identifying gaps and challenges. The community has examined touch points with the supply chain and identified improvements related to the communication of cybersecurity requirements, technical and commercial engagement between customers and suppliers, and in the tailoring of implementations towards operational technology contexts. Communicating the context of securing cyber-physical systems is an essential perspective for this community. This work exemplifies a partnership framework and is translating experiences into useful guidance, particularly for OT systems, to improve cybersecurity levels across multiple contributors to critical infrastructure systems.

Precision agriculture (PA) is a management concept that makes use of digital techniques to monitor and optimise agricultural production processes and represents a field of growing economic and social importance. Within this area of knowledge, there is a topic not yet fully explored: outlining a road map towards the definition of an affordable cobot solution (i.e., a low-cost robot able to safely coexist with humans) able to perform automatic chemical treatments. The present study narrows its scope to viticulture technologies, and targets small/medium-sized winemakers and producers, for whom innovative technological advancements in the production chain are often precluded by financial factors. The aim is to detail the realization of such an integrated solution and to discuss the promising results achieved. The results of this study are: (i) The definition of a methodology for integrating a cobot in the process of grape chemicals spraying under the constraints of a low-cost apparatus; (ii) the realization of a proof-of-concept of such a cobotic system; (iii) the experimental analysis of the visual apparatus of this system in an indoor and outdoor controlled environment as well as in the field.

In real-time production planning, there are exceptional events that can cause problems and deviations in the production schedule. These circumstances can be solved with real-time production planning, which is able to quickly reschedule the operations at each work centre. Mobile autonomous robots are a key element in this real-time planning and are a fundamental link between production centres. Work centres in Industry 4.0 environments can use current technology, i.e., a biomimetic strategy that emulates echolocation, with the aim of establishing bidirectional communication with other work centres through the application of agile algorithms. Taking advantage of these communication capabilities, the basic idea is to distribute the execution of the algorithm among different work centres that interact like a parasympathetic system that makes automatic movements to reorder the production schedule. The aim is to use algorithms with an optimal solution based on the simplicity of the task distribution, trying to avoid heuristic algorithms or heavy computations. This paper presents the following result: the development of an Industrial Sonar algorithm which allows real-time scheduling and obtains the optimal solution at all times. The objective of this is to reduce the makespan, reduce energy costs and carbon footprint, and reduce the waiting and transport times for autonomous mobile robots using the Internet of Things, cloud computing and machine learning technologies to emulate echolocation.

In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs, resulting in malicious files being misclassified. Cyber-Physical Systems (CPS) may be compromised by malicious files, which can have catastrophic consequences. This paper presents a method for classifying Windows portable executables (PEs) using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To generate malware executable adversarial examples of PE, we conduct two white-box attacks, Jacobian-based Saliency Map Attack (JSMA) and Carlini and Wagner attack (C&W). An adversarial payload was injected into the DOS header, and a section was added to the file to preserve the PE functionality. The attacks successfully evaded the CNN model with a 91% evasion rate, whereas the RNN model evaded attacks at an 84.6% rate. Two defense mechanisms based on distillation and training techniques are examined in this study for overcoming adversarial example challenges. Distillation and training against JSMA resulted in the highest reductions in the evasion rates of 48.1% and 41.49%, respectively. Distillation and training against C&W resulted in the highest decrease in evasion rates, at 48.1% and 49.9%, respectively.

The potential application of the digital twins concept and its principles to all industries, including agriculture, has recently been the subject of considerable research interest. Since livestock farming is one of the mainstays of the agricultural industry, contributing to food security as well as employment and income generation, digital twins appear to be a promising technology to address issues related to the intensive nature of livestock production systems, which are typically characterized by high levels of input and output. The objective of this study is to identify and analyze the latest scientific and technological contributions in the field of digital twins technology, with a view to its adoption and assimilation in livestock production systems in the context of Agriculture 5.0. In particular, the aim of this study is to synthesize the existing research and identify the opportunities and open issues related to the implementation of digital twins in livestock production systems. The results derived in the literature review indicate that digital twins is still in the early stages of development in livestock farming, but it has the potential to transform the industry. As Agriculture 5.0 evolves and the digital twins technology matures, it is expected to play an increasingly important role in the livestock farming industry.

Cybersecurity finds widespread applications across diverse domains, encompassing intelligent industrial systems, residential environments, personal gadgets, and automobiles. This has spurred groundbreaking advancements while concurrently posing persistent challenges in addressing security concerns tied to IoT devices. IoT intrusion detection involves using sophisticated techniques, including deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and anomaly detection algorithms, to identify unauthorized or malicious activities within IoT ecosystems. These systems continuously monitor and analyze network traffic and device behavior, seeking patterns that deviate from established norms. When anomalies are detected, security measures are triggered to thwart potential threats. IoT intrusion detection is vital for safeguarding data integrity, ensuring users’ privacy, and maintaining critical systems’ reliability and safety. As the IoT landscape evolves, effective intrusion detection mechanisms become increasingly essential to mitigate the ever-growing spectrum of cyber threats. Practical security approaches, notably deep learning-based intrusion detection, have been introduced to tackle these issues. This study utilizes deep learning models, including convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs), while introducing an ensemble deep learning architectural framework that integrates a voting policy within the model’s structure, thereby facilitating the computation and learning of hierarchical patterns. In our analysis, we compared the performance of ensemble deep learning classifiers with traditional deep learning techniques. The standout models were CNN-LSTM and CNN-GRU, achieving impressive accuracies of 99.7% and 99.6%, along with exceptional F1-scores of 0.998 and 0.997, respectively.

Cyber–physical systems use digital twins to provide advanced monitoring and control functions, including self-diagnosis. The digital twin is often conceptualized as a 3D model, but mathematical models implemented in numerical simulations are required to reproduce the dynamical and functional characteristics of physical systems. In this work, a cyber–physical system scheme is proposed to monitor and diagnose failures. The virtual system, embedded at the supervisory control level, combines concepts from complex networks and hybrid automata to detect failures in the hardware components and in the execution of the sequential logic control. An automated storage and retrieval system is presented as a case study to show the applicability of the proposed scheme. The functional test and the obtained results validate the implemented system that is shown to be capable of fault diagnosis and location in real time. The online execution of the digital twin present several advantages for diagnosing multiple concurrent failures in sensors, actuators, and the control unit. This approach can be incorporate into diverse manufacturing systems.

This work proposes a method for hardware reduction in circuits of Mealy finite state machines (FSMs). The circuits are implemented as networks of interconnected look-up table (LUT) elements. The FSMs with twofold state assignment and encoding of output collections are discussed. The method is based on using two LUT-based cores to implement systems of partial Boolean functions. One of the cores uses only maximum binary codes, while the second core is based on the use of extended state codes. The hardware reduction is based on diminishing the number of transformed maximum binary codes. This leads to FPGA-based FSM circuits with three levels of logic blocks. Each logic block has a single level of LUTs. As a result, partial functions are represented by single-LUT circuits. The article shows a step-by-step procedure for the transition from the initial form of the FSM representation to its logical circuit (a network of programmable look-up table elements, flip-flops, and interconnects). The results of experiments conducted with standard benchmarks show that the proposed approach produces LUT-based FSM circuits with significantly better area characteristics than for circuits produced by such methods as Auto and One-Hot of Vivado, JEDI, and twofold state assignment. Compared to these methods, the number of LUTs is reduced from 9.44% to 69.98%. Additionally, the proposed method leads to the following phenomenon: the maximum operating frequency is slightly improved as compared with FSM circuits based on twofold state assignment (up to 0.6%). The negative effect of these improvements is an increase in power consumption. However, it is extremely insignificant (up to 1.56%). As the values of the FSM’s main characteristics grow, there is an increase in the gain from the application of the proposed method. The conditions for applying the proposed method are determined. A generalized architecture consisting of three blocks of partial functions and a method for synthesizing an FSM with this architecture are proposed. A method for selecting one of the seven architectures generated by the generalized architecture is proposed.

Industry 4.0 (I4.0) grants businesses in the manufacturing sector significant benefits in terms of productivity and efficiency; however, industries must strive to reach a level of technological readiness and acquire the necessary expertise to implement and integrate I4.0 technologies to enhance their processes. This paper presents a framework designed to provide guidance to businesses within the manufacturing industry to improve efficiency and productivity. The framework was developed and refined in collaboration with a leading cold-chain insulated packaging manufacturer based in Melbourne, Australia. The proposed framework was designed and implemented utilising off-the-shelf sensing, processing, and visualisation components, which can be integrated into existing machinery, thereby achieving a cost-effective retrofit. Experimental results showed that a system developed using the proposed framework can accurately measure material consumption in terms of length. The system provides the building blocks for the manufacturer to avoid downtime by preventing the machine from running out of raw materials.

The increased adoption of information and communication technology for smart grid applications will require innovative cyber–physical system (CPS) testbeds to support research and education in the field. Groundbreaking CPS testbeds with realistic and scalable platforms have progressively gained interest in recent years, with electric power flowing in the physical layer and information flowing in the network layer. However, CPSs are critical infrastructures and not designed for testing or direct training, as any misbehaving in an actual system operation could cause a catastrophic impact on its operation. Based on that, it is not easy to efficiently train professionals in CPSs. Aiming to support the advancement and encourage the training of industry professionals, this paper proposes and develops a complete testbed using a real-time simulator, protection and automation devices, and a supervisory control and data acquisition (SCADA) system. The testbed replicated the performance of smart grids, and the main potential cyber threats that electric grids may face. Different case scenarios include a distribution system protection study, a denial of service (DoS) attack, a jamming attack, a network packet manipulation attack, a sensor data manipulation attack, a false trip command attack, etc. The system’s performance before and after the cyberattacks are studied using packet-sniffing tools and a network packet analyzer. The impact on the grid is analyzed using metrics such as voltage oscillation, frequency deviation, and loss of active power generation. Moreover, the complex interdependencies between the cyber and physical domains are discussed in detail, providing insightful guidelines for key features and design decisions for future smart grid testbeds.

This study is concerned with the imperfect virtual-reality mapping relationship in cyber–physical systems (CPSs) and the challenge faced in knowledge-based decisions. Regarding those problems, a double-layer mapping Petri net (DMPN) model is proposed. By deploying the programmable automation gateway PAG200, combining the CPS technology with the principle of Petri net and establishing the monitoring Petri net in the cyber space, this model realizes mapping between the physical entity and the digital object. Meanwhile, the knowledge-based decision problem in CPS is defined as a Petri net conflict. In accordance with this, a control network for resolving the conflict is established. Finally, through a practical case, the workflow of DMPN is illustrated and a task allocation algorithm and a quality decision algorithm are proposed to resolve Petri net conflicts. Consequently, it is proven that DMPN is feasible in solving actual production process flow control. At the same time, it also provides a solution for enterprise workflow analysis.

Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Nevertheless, AI applications for such grids as cyber-physical systems encounter multifaceted challenges, e.g., high requirements for the quality and quantity of training data, data efficiency, physical inconsistency, interpretability, and privacy concerns. This paper provides a systematic overview of the state-of-the-art AI methodologies in the post-pandemic era, represented by transfer learning, deep attention mechanism, graph learning, and their combination with reinforcement learning and physics-guided neural networks. Dedicated research efforts on harnessing such recent advances, including power flow, state estimation, voltage control, topology identification, and line parameter calibration, are categorized and investigated in detail. Revolving around the characteristics of distribution system operation and integration of distributed energy resources, this paper also illuminates prospects and challenges typified by the privacy, explainability, and interpretability of such AI applications in smart grids. Finally, this paper attempts to shed light on the deeper and broader prospects in the realm of smart distribution grids by interoperating them with smart building and transportation electrification

With the continuous development of information technology, a spontaneous interdependent network has formed within the air traffic control network. Due to the internal interdependence, any small, failed node may trigger a cascade failure of the entire system. The purpose of this study is to investigate the resilience of air traffic control networks. Based on air traffic management regulations, a new cascading failure model for air traffic control networks is proposed, which is based on the theory of interdependent networks. The model establishes a dual-layer dependency relationship between the control coordination network and the air route facility network, including control dependency and service dependency. Through experiments, targeted measures are proposed to improve the safety and reliability of air traffic control. This model introduces parameters such as control cost and node control capability, and reflects the resilience of the air traffic control network, based on the final number of failed nodes after the steady-state of the cascade failure, the network’s cascade failure rate, and the system’s load failure threshold. Simulation results show that enhancing the control capability and increasing the number of control positions can improve the control cost of the air traffic control network. The higher the control cost, the better the resilience of the air traffic control network. Improving the control capability of control nodes has a greater impact on the resilience of the air traffic control network, compared to increasing the number of control nodes. The degree attack on route nodes has a greater impact on the cascade failure of the air traffic control network, compared to random attacks and facility node degree attacks. The cascade failure model proposed in this paper provides a new method for guiding the air traffic control network to resist cascade failure attacks and enhance its resilience.

The sustainable provision of mankind with energy and mineral raw materials is associated with an increase not only in industrial but also in the ecological and economic development of the raw material sector. Expanding demand for energy, metals, building and chemical raw materials on the one hand, and the deterioration of the living environment along with a growth of raw materials extraction on the other, put the human-centric development of mining at the forefront. This forms a transition trend from Mining 4.0 technologies such as artificial intelligence, big data, smart sensors and robots, machine vision, etc., to Mining 5.0, presented with collaborative robots and deserted enterprises, bioextraction of useful minerals, postmining, and revitalization of mining areas. This “bridge” is formed by the technological convergence of information, cognitive, and biochemical technologies with traditional geotechnology, which should radically change the role of the resource sector in the economy and society of the 21st century. The transition from Mining 3.0 to 4.0 cannot be considered complete. However, at the same time, the foundation is already being laid for the transition to Mining 5.0, inspired, on the one hand, by an unprecedented gain in productivity, labor safety, and predictability of commodity markets, on the other hand, by the upcoming onset of Industry 5.0. This review provides a multilateral observation of the conditions, processes, and features of the current transition to Mining 4.0 and the upcoming transformation on the Mining 5.0 platform, highlighting its core and prospects for replacing humans with collaborated robots and artificial intelligence. In addition, the main limitations of the transition to Mining 5.0 are discussed, the overcoming of which is associated with the development of green mining and ESG (environment, social, and governance) investment.

A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%.

Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, which often provide one separate model per device or per attack. These solutions are not suited to the scale and dynamism of modern IoT networks. This paper proposes a novel IoT-driven cross-device method, which allows learning a single IDS model instead of many separate models atop the traffic of different IoT devices. A semi-supervised approach is adopted due to its wider applicability for unanticipated attacks. The solution is based on an all-in-one deep autoencoder, which consists of training a single deep neural network with the normal traffic from different IoT devices. Extensive experimentation performed with a widely used benchmarking dataset indicates that the all-in-one approach achieves within 0.9994–0.9997 recall, 0.9999–1.0 precision, 0.0–0.0071 false positive rate and 0.9996–0.9998 F1 score, depending on the device. The results obtained demonstrate the validity of the proposal, which represents a lightweight and device-independent solution with considerable advantages in terms of transferability and adaptability.

Wireless sensors and actuator networks (WSNs) are the physical layer implementation used for many smart applications in this decade in the form of the Internet of Things (IoT) and cyber-physical systems (CPS). Even though many research concerns in WSNs have been answered, the evolution of the WSN into an IoT network has exposed it to many new technical issues, including data security, multi-sensory multi-communication capabilities, energy utilization, and the age of information. Cluster-based data collecting in the Internet of Things has the potential to address concerns with data freshness and energy efficiency. However, it may not offer reliable network data security. This research presents an improved method for data sharing and cluster head (CH) selection using the hybrid Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method in conjunction with glowworm swarm optimization (GSO) strategies based on the energy, trust value, bandwidth, and memory to address this security-enabled, cluster-based data aggregation in the IoT. Next, we aggregate the data after the cluster has been built using a genetic algorithm (GA). After aggregation, the data are encrypted and delivered securely using the TIGSO-EDS architecture. Cuckoo search is used to analyze the data and choose the best route for sending them. The proposed model’s analysis of the results is analyzed, and its uniqueness has been demonstrated via comparison with existing models. TIGSO-EDS reduces energy consumption each round by 12.71–19.96% and increases the percentage of successfully delivered data packets from 2.50% to 5.66%.

Occupational safety and health (OSH) should be regarded as a crucial challenge that affects the public world widely. Work-related accidents and occupational illness contribute to considerable mortality and morbidity. As technology advances, mixed reality (MR) has gained popularity. To minimize occupational accidents occurring in the workplace and reduce human training time, an MR-based platform for OSH training combined with CPS and IoT technology is proposed in this paper. Multi-criteria decision-making (MCDM) and fuzzy-analytic hierarchy process (FAHP) were applied to evaluate and select suitable gloves. Only when the MR wearable devices are improved can a more powerful MR-based OSH training program be established. A higher immersive level of OSH training offers people a more realistic experience. They will better understand possible risks in workers’ future work, resulting in a lower occupational accident rate in the workplace.

Environmental monitoring is a crucial part of environmental management on construction sites. With the increasing integration of environmental-monitoring systems and cyber-physical systems (CPS), the environmental-monitoring cyber-physical system (E-CPS) has been developed, but it still suffers from uncertainty problems and a lack of robustness. In this study, ontology is utilized to establish an E-CPS model that can realize the integration and interaction of physical space, cyberspace, and social space, and the E-CPS model contains perception, transportation, fusion, and decision-making layers. Three uncertainty scenarios are then identified in four layers of the E-CPS to address the current E-CPS shortcomings. The proposed E-CPS model is applied in a construction project, and simulation experiments are then conducted on construction sites. The results show that the abnormal-data-recognition algorithm based on spatiotemporal correlation, whose detection rate is stable around 96%, improves the system’s anti-interference ability against anomalous data entering the perception layer and the transportation layer. This algorithm ensures the accuracy of environmental monitoring for early warning. The sensory data-fusion results based on the belief function method vary from 52.16 to 52.50, with a decrease rate reduced to 0.65%. Finally, the decision-fusion algorithm based on the improved Dempster–Shafer (D-S) evidence theory achieves robust performance. This study could enhance the robustness of the E-CPS in uncertainty conditions and aid the project managers to make decisions and take targeted measures according to the environmental monitoring results and experts’ decisions.

Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.

Active buildings can be briefly described as smart buildings with distributed and renewable energy resources able to energise other premises in their neighbourhood. As their energy capacity is significant, they can provide ancillary services to the traditional power grid. As such, they can be a worthy target of cyber-attacks potentially more devastating than if targeting traditional smart buildings. Furthermore, to handshake energy transfers, they need additional communications that add up to their attack surface. In such a context, security analysis would benefit from collection of cyber threat intelligence (CTI). To facilitate the analysis, we provide a base active building model in STIX in the tool cyberaCTIve that handles complex models. Active buildings are expected to implement standard network security measures, such as intrusion-detection systems. However, to timely respond to incidents, real-time detection should promptly update CTI, as it would significantly speed up the understanding of the nature of incidents and, as such, allow for a more effective response. To fill this gap, we propose an extension to the tool cyberaCTIve with a web service able to accept (incursion) feeds in real-time and apply the necessary modifications to a STIX model of interest.

Controller failures can result in unsafe physical plant operations and deteriorated performance in industrial cyber–physical systems. In this paper, we present a controller switching mechanism over wireless sensor–actuator networks to enhance the resiliency of control systems against problems and potential physical failures. The proposed mechanism detects controller failures and quickly switches to the backup controller to ensure the stability of the control system in case the primary controller fails. To show the efficacy of our proposed method, we conduct a performance evaluation using a hardware-in-the-loop testbed that considers both the actual wireless network protocol and the simulated physical system. Results demonstrate that the proposed scheme recovers quickly by switching to a backup controller in the case of controller failure.

Cyber-physical systems (CPSs) have greatly contributed to many applications. A CPS is capable of integrating physical and computational capabilities to interact with individuals through various new modalities. However, there is a need for such a paradigm to focus on the human central nervous system to provide faster data access. This paper introduces the CPS paradigm that consists of CPS enabled human brain monitoring (CPS-HBM) and efficient data-balancing for CPS (EDB-CPS). The CPS-HBM provides architectural support to make an efficient and secure transfer and storage of the sensed data over fog cloud computing. The CPS-HBM consists of four components: physical domain and data processing (PDDP), brain sensor network (BSN), Service-oriented architecture (SOA), and data management domain (DMD). The EDB-CPS module aims to balance data flow for obtaining better throughput and lower hop-to-hop delay. The EDB-CPS accomplishes the goal by employing three processes: A node advertisement (NA), A node selection and recruitment (NSR), and optimal distance determination with mid-point (ODDMP). The processes of the EDB-CPS are performed on the PDDP of the CPS-HBM module. Thus, to determine the validity of EDB-CPS, the paradigm was programmed with C++ and implemented on a network simulator-3 (NS3). Finally, the performance of the proposed EDB-CPS was compared with state-of-the-art methods in terms of hop-to-hop delay and throughput. The proposed EDB-CPS produced better throughput between 443.2–445.2 KB/s and 0.05–0.078 ms hop-to-hop delay.

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.

Radon gas is a harmful pollutant with a well-documented adverse influence on public health. In poorly ventilated environments, that are often prone to significant radon levels, studies indicate a known relationship between human radon exposure and lung cancer. Recent technology advances, notably on the Internet of Things (IoT) ecosystem, allow the integration of sensors, computing, and communication capabilities into low-cost and small-scale devices that can be used for implementing specific cyber-physical systems (CPS) for online and real-time radon management. These technologies are crucial for improving the overall building indoor air quality (IAQ), contributing toward the so-called cognitive buildings, where human-based control is tending to decline, and building management systems (BMS) are focused on balancing critical factors, such as energy efficiency, human radon exposure management, and user experience, to achieve a more transparent and harmonious integration between technology and the built environment. This work surveys recent IoT technologies for indoor radon exposure management (monitoring, assessment and mitigation), and discusses its main challenges and opportunities, by focusing on methods, techniques, and technologies to answer the following questions: (i) What technologies have been recently in use for radon exposure management; (ii) how they operate; (iii) what type of radon detection mechanisms do they use; and (iv) what type of system architectures, components, and communication technologies have been used to assist the referred technologies. This contribution is relevant to pave the way for designing more intelligent and sustainable systems that rely on IoT and Information and Communications Technology (ICT), to achieve an optimal balance between these two critical factors: human radon exposure management and building energy efficiency.

Prior experience from the authors has shown that a heavily theoretical approach for cybersecurity training has multiple shortcomings, mostly due to the demanding and diversified nature of the prerequisites, often involving concepts about operating system design, networking and computer architecture, among others. In such circumstances, the quest for trainee engagement often turns into a delicate balancing act between managing their expectations and providing an adequate progression path. In this perspective, hands-on exercises and contact with high-fidelity environments play a vital part in fostering interest and promoting a rewarding learning experience. Making this possible requires having the ability to design and deploy different use case training scenarios in a flexible way, tailored to the specific needs of classroom-based, blended or e-learning teaching models. This paper presents a flexible framework for the creation of laboratory and cyber range environments for training purposes, detailing the development, implementation and exploration of a cyber range scenario, within the scope of a course on cyber-physical systems security. Moreover, the course structure, curricular aspects and teaching methods are also detailed, as well as the feedback obtained from the students.

Amid the current industrial revolution, a total disruption of the existing production lines may seem to be the easiest approach, as the potential possibilities seem limitless when starting from the ground up. On the business side, an adaptation of existing production lines is always a preferred option. In support of adaptation as opposed to disruption, this paper presents a new approach of using production process orchestration in a smart factory, discussed in an industrial case-study example. A proposed smart factory has the Orchestrator component in its core, responsible for complete semantical orchestration of production processes on one hand, and various factory resources on the other hand, in order to produce the desired product. The Orchestrator is a complex, modular, highly scalable, and pluggable software product responsible for automatised planning, scheduling, and execution of the complete production process. According to their offered capabilities, non-smart and smart resources—machines, robots, humans—are simultaneously and dynamically assigned to execute their dedicated production steps.

Advancement of IoT and ICT provide infrastructure to manage, monitor and control Cyber-Physical Systems (CPS) through timely provision of real-time information from the shop floor. Although real-time information in CPS such as resource failures can be detected based on IoT and ICT, improper response to resource failures may cripple CPS and degrade performance. Effective operations of CPS relies on an effective scheme to evaluate the impact of resource failures, support decision making needed and take proper actions to respond to resource failures. This motivates us to develop a methodology to assess the impact of resource failures on operations of CPS and provide the decision support as needed. The goal of this study is to propose solution algorithms to analyze robustness of CPS with respect to resource failures in terms of the impact on temporal properties. Given CPS modeled by a class of discrete timed Petri nets (DTPNs), we develop theory to analyze robustness of CPS by transforming the models to residual spatial-temporal network (RSTN) models in which capacity loss due to resources is reflected. We formulate an optimization problem to determine the influence of resource failures on CPS based on RSTNs and analyze the feasibility to meet the order deadline. To study the feasibility to solve a real problem, we analyze the computational complexity of the proposed algorithms. We illustrate the proposed method by application scenarios. We conduct experiments to study efficiency and verify computational feasibility of the proposed method to solve a real problem.

A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks.

The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.

Digitisation of the built environment is seen as a significant factor for innovation in the Architecture, Engineering, Construction and Operation sector. However, lack of data and information in as-built digital models considerably limits the potential of Building Information Modelling in Facility Management. Therefore, optimisation of data collection and management is needed, all the more so now that Industry 4.0 has widened the use of sensors into buildings and infrastructures. A literature review on the two main pillars of digitalisation in construction, Building Information Modelling and Internet of Things, is presented, along with a bibliographic analysis of two citations and abstracts databases focusing on the operations stage. The bibliographic research has been carried out using Web of Science and Scopus databases. The article is aimed at providing a detailed analysis of BIM–IoT integration for Facility Management (FM) process improvements. Issues, opportunities and areas where further research efforts are required are outlined. Finally, four key areas of further research development in FM management have been proposed, focusing on optimising data collection and management.

Petri nets are an important tool for creation of new platforms for digitised production systems due to their versatility in modelling discrete event systems. For the development of modern complex production processes for Industry 4.0, using advanced mathematical models based on Petri nets is an appropriate and effective option. The main aim of the proposed article is to design a new software tool for modelling and control of discrete event systems using Arduino-type microcontrollers and code generation techniques. To accomplish this task, a new tool called “PetriNet editor + PetriNet engine” based on Petri nets is proposed able to generate the code for the microcontroller according to the modelled Petri net. The developed software tool was successfully verified in control of a laboratory plant. Offering a graphical environment for the design of discrete event system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0).

The field of prognostic maintenance aims at predicting the remaining time for a system or component to continue being used under the desired performance. This time is usually named as Remaining Useful Life (RUL). The current study proposes a novel approach for the RUL estimation of coating segments placed on a hot rolling mill machine. A prediction method was developed, providing real-time updates of the RUL prediction during the rolling milling process. The proposed approach performs energy analysis on measurements of segment surface temperatures and hydraulic forces. It uses nonparametric statistical processes to update the predictions, within a prediction horizon/window, indicating the number of remaining products to be processed. To assess the probability of failure within the defined prediction window, Maximum Likelihood Estimation is used. The proposed methodology was implemented in a software prototype in the MATLAB environment and tested in an industrial use case coming from a steel parts manufacturer, facilitating testing and validation of the suggested approach. Real-world data were acquired from the operational machine, while the validation results support that the proposed methodology demonstrates reasonable performance and robustness against product type variations.

For the development of modern complex production processes in Industry 4.0, it is appropriate to effectively use advanced mathematical models based on Petri nets. Due to their versatility in modeling discrete-event systems, Petri nets are an important support in creating new platforms for digitized production systems. The main aim of the proposed article is to design a new software tool for modeling and control of discrete-event and hybrid systems using Arduino and similar microcontrollers. To accomplish these tasks, a new tool called PN2ARDUINO based on Petri nets is proposed able to communicate with the microcontroller. Communication with the microcontroller is based on the modified Firmata protocol hence, the control algorithm can be implemented on all microcontrollers that support this type of protocol. The developed software tool was successfully verified in control of laboratory systems. In addition, it can be used for education and research purposes as it offers a graphical environment for designing control algorithms for hybrid and mainly discrete-event systems. The proposed software tool can improve education and practice in cyber-physical systems (Industry 4.0).

Cyber Physical Systems (CPSs) are systems that are developed by seamlessly integrating computational algorithms and physical components, and they are a result of the technological advancement in the embedded systems and distributed systems domains, as well as the availability of sophisticated networking technology. Many industrial CPSs are subject to timing predictability, security and functional safety requirements, due to which the developers of these systems are required to verify these requirements during the their development. This position paper starts by exploring the state of the art with respect to developing timing predictable and secure embedded systems. Thereafter, the paper extends the discussion to time-critical and secure CPSs and highlights the key issues that are faced when verifying the timing predictability requirements during the development of these systems. In this context, the paper takes the position to advocate paramount importance of security as a prerequisite for timing predictability, as well as both security and timing predictability as prerequisites for functional safety. Moreover, the paper identifies the gaps in the existing frameworks and techniques for the development of time- and safety-critical CPSs and describes our viewpoint on ensuring timing predictability and security in these systems. Finally, the paper emphasises the opportunities that artificial intelligence can provide in the development of these systems.

In 2011, the concept of Industry 4.0 was introduced and later adopted by the German government, paving the way for a new industrial revolution in Germany. The high significance of this topic is reflected by the large number of corresponding publications. Additionally, the regional focus of research is widespread on a global level and often differs even at a national level. This paper generates transparency regarding the adoption of the concept of Industry 4.0 by analyzing the locations of main contributors within the research field on an international, European, and German-national level. Further, it examines the regionally different foci concerning the concept of Industry 4.0. Having identified four main aspects linked to Industry 4.0 within a pre-study, a quantitative literature research was conducted based on over 800 published papers. The results were further visualized with QGIS. Looking at the results, it can be concluded that the German research community is virtually the only user of the term Industry 4.0, while other institutions seem to link their research to other related concepts. On a German level, the majority of the analyzed studies originate from Southern and Western Germany. North Rhine-Westphalia and the Aachen/Jülich region, in particular, represent main contributors.

Recent developments in the area of cyber-physical systems (CPSs) and Internet of Things (IoT) are among the drivers for the emergence of the Industry 4.0 concept, setting new requirements for the architecture, technology, and design approaches of modern industrial systems. Industry 4.0 assumes a higher level of intelligence, and thus autonomy of the systems and subsystems, and a larger focus on the analysis of gathered data for further utilization. The Virtual Factory Open Operating System (vf-OS) project is intended to respond to some of these key challenges, in particular for the smart factory application domain. Complementarily, data and knowledge storage and processing are also in the scope of vf-OS. This article introduces the semantic management component of vf-OS, which aims to analyze the interrelations among stored entities, as well as to define the closeness among them to generate meaningful suggestions, which can be later used by other subsystems or operators in a user-friendly way. The semantic managing system makes use of non relational approaches, namely a graph database, which enables data to be represented as graphs for further semantic querying. The developed prototype and an illustrative application case are also presented.

With the help of community participants, smartphones can become useful wireless sensor network (WSN) components, form a self-governing structural health monitoring (SHM) system, and merge structural mechanics with participatory sensing and server computing. This paper presents a methodology and framework of such a cyber-physical system (CPS) that generates a bridge finite element model (FEM) integrated with vibration measurements from smartphone WSNs and centralized/distributed computational facilities, then assesses structural reliability based on updated FEMs. Structural vibration data obtained from smartphones are processed on a server to identify modal frequencies of an existing bridge. Without design drawings and supportive documentation but field measurements and observations, FEM of the bridge is drafted with uncertainties in the structural mass, stiffness, and boundary conditions (BCs). Then, 2700 FEMs are autonomously generated, and the baseline FEM is updated by minimizing the error between the crowdsourcing-based modal identification results and the FEM analysis. Furthermore, using 151 strong ground motion records from databases, the bridge response time history simulations are conducted to obtain displacement demand distribution. Finally, based on reference performance criteria, structural reliability of the bridge is estimated. Integrating the cyber (FEM analysis) and the physical (the bridge structure and measured vibration characteristics) worlds, this crowdsourcing-based CPS can provide a powerful tool for supporting rapid, remote, autonomous, and objective infrastructure-related decision-making. This study presents a new example of the emerging fourth industrial revolution from structural engineering and SHM perspective.

The paper presents the issue of metamodeling of Domain-Specific Languages (DSL) for the purpose of designing complex mechatronics systems. Usually, one of the problems during the development of such projects is an interdisciplinary character of the team that is involved in this endeavour. The success of a complex machine project (e.g. Computer Numerically Controlled machine (CNC), loading crane, forestry crane) often depends on a proper communication between team members. The domain-specific modelling languages developed using one of the two approaches discussed in the work, lead to a machine design that can be carried out much more efficiently than with conventional approaches. Within the paper, the Meta-Object Facility (MOF) approach to metamodeling is presented; it is much more prevalent in modern modelling software tools than Graph-Object-Property-Relationship-Role (GOPRR). The main outcome of this work is the first presentation of researchML modelling language that is the result of more than twenty ambitious research and development projects. It is effectively used within new enterprises and leads to improved traceability of the project goals. It enables for fully-featured automatic code generation which is one of the main pillars of the agile management within mechatronic system design projects.

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

In recent years maritime logistics infrastructures are the global links among societies and economies. This challenges adversaries to intrude on the cyber-dependent ICTs by performing high-level intelligent techniques. A potential cyber-attack on such infrastructures can cause tremendous damages starting from supply chain service disruption ending up with threatening the whole human welfare. Current risk management policies embed significant limitations in terms of capturing the specific security requirements of ICTs and control/monitoring devices, such as IoT platforms, satellites and time installations, which are primary functioning for the provision of Maritime Logistics and Supply Chain (MLoSC) services. This work presents a novel risk assessment methodology capable of addressing the security particularities and specificities of the complex nature of SCADA infrastructures and Cyber-Physical Systems (CPSs) of the Maritime Logistics Industry. The methodology identifies asset vulnerabilities and threats to estimate the cyber-risks and their cascading effects within the supply chain, introducing a set of subsequent security assessment services. The utilization of these services is demonstrated via a critical, real-life SCADA scenario indicating how they can facilitate supply chain operators in comprehending the threat landscape of their infrastructures and guide them how to adopt optimal mitigation strategies to counter or eliminate their cyber-risks.

A cyber-physical system (CPS) is a combination of physical system components with cyber capabilities that have a very tight interconnectivity. CPS is a widely used technology in many applications, including electric power systems, communications, and transportation, and healthcare systems. These are critical national infrastructures. Cybersecurity attack is one of the major threats for a CPS because of many reasons, including complexity and interdependencies among various system components, integration of communication, computing, and control technology. Cybersecurity attacks may lead to various risks affecting the critical infrastructure business continuity, including degradation of production and performance, unavailability of critical services, and violation of the regulation. Managing cybersecurity risks is very important to protect CPS. However, risk management is challenging due to the inherent complex and evolving nature of the CPS system and recent attack trends. This paper presents an integrated cybersecurity risk management framework to assess and manage the risks in a proactive manner. Our work follows the existing risk management practice and standard and considers risks from the stakeholder model, cyber, and physical system components along with their dependencies. The approach enables identification of critical CPS assets and assesses the impact of vulnerabilities that affect the assets. It also presents a cybersecurity attack scenario that incorporates a cascading effect of threats and vulnerabilities to the assets. The attack model helps to determine the appropriate risk levels and their corresponding mitigation process. We present a power grid system to illustrate the applicability of our work. The result suggests that risk in a CPS of a critical infrastructure depends mainly on cyber-physical attack scenarios and the context of the organization. The involved risks in the studied context are both from the technical and nontechnical aspects of the CPS.