Search Results (1,653)

In the past, providing an online and real-time response to cyber–physical attacks in large-scale power microgrids was considered a fundamental challenge by operators and managers of power distribution networks. To address this issue, an innovative framework is proposed in this paper, enabling real-time responsiveness to cyberattacks while focusing on the techno-economic energy management of large-scale power microgrids. This framework leverages the large change sensitivity (LCS) method to receive immediate updates to the system’s optimal state under disturbances, eliminating the need for the full recalculation of power flow equations. This significantly reduces computational complexity and enhances real-time adaptability compared to traditional approaches. Additionally, this framework optimizes operational points, including resource generation and network reconfiguration, by simultaneously considering technical, economic, and reliability parameters—a comprehensive integration often overlooked in recent studies. Performance evaluation on large-scale systems, such as IEEE 33-bus, 69-bus, and 118-bus networks, demonstrates that the proposed method achieves optimization in less than 2 s, ensuring superior computational efficiency, scalability, and resilience. The results highlight significant improvements over state-of-the-art methods, establishing the proposed framework as a robust solution for real-time, cost-effective, and resilient energy management in large-scale power microgrids under cyber–physical disturbances. Full article

As we progress towards Industry 5.0, technological advancements are converging; this movement is realised by the increasing collaboration between humans and intelligent digital platforms and further enabled by the interactive visualisation modes provided by Metaverse technology. This research examines the practical applications and limitations of Metaverse technology providing insights into the transformative possibilities it offers for the manufacturing sector. Specifically, the research was guided by the core objective to trace the evolution of Metaverse technology within manufacturing. This study provides a comprehensive and state-of-the-art analysis of the adoption and impact of Metaverse technologies in the manufacturing sector. While previous research has explored aspects of Industry 4.0 and digital transformation, this study specifically focuses on human-centric manufacturing (human-in-the-loop) applications of Metaverse technology, including augmented reality, virtual reality, digital twins, and cyber-physical robotic systems. Findings from the systematic literature review indicate that Metaverse technologies, primarily augmented reality and virtual reality, have evolved into powerful tools in manufacturing. They are widely adopted across sectors in the industry, transforming processes such as product design, quality control, and maintenance. Augmented reality and virtual reality offer intuitive ways to visualise data and interact with digital twins, bridging the gap between physical and virtual realms in manufacturing. A roadmap and scenarios for the introduction of Metaverse technology in manufacturing are provided with suggested adoption timespans. Furthermore, the systematic literature review identified barriers hindering the wider adoption of Metaverse technology in manufacturing. Full article

The Internet of Vehicles (IoV), a key component of smart transportation systems, leverages 5G communication for low-latency data transmission, facilitating real-time interactions between vehicles, roadside units (RSUs), and sensor networks. However, the open nature of 5G communication channels exposes IoV systems to significant security threats, such as eavesdropping, replay attacks, and message tampering. To address these challenges, this paper proposes the Efficient Cluster-based Mutual Authentication and Key Update Protocol (ECAUP) designed to secure IoV systems within 5G-enabled sensor networks. The ECAUP meets the unique mobility and security demands of IoV by enabling fine-grained access control and dynamic key updates for RSUs through a factorial tree structure, ensuring both forward and backward secrecy. Additionally, physical unclonable functions (PUFs) are utilized to provide end-to-end authentication and physical layer security, further enhancing the system’s resilience against sophisticated cyber-attacks. The security of the ECAUP is formally verified using BAN Logic and ProVerif, and a comparative analysis demonstrates its superiority in terms of overhead efficiency (more than 50%) and security features over existing protocols. This work contributes to the development of secure, resilient, and efficient intelligent transportation systems, ensuring robust communication and protection in sensor-based IoV environments. Full article

This paper provides the complete details of current challenges and solutions in the cybersecurity of cyber-physical systems (CPS) within the context of the IIoT and its integration with edge computing (IIoT–edge computing). We systematically collected and analyzed the relevant literature from the past five years, applying a rigorous methodology to identify key sources. Our study highlights the prevalent IIoT layer attacks, common intrusion methods, and critical threats facing IIoT–edge computing environments. Additionally, we examine various types of cyberattacks targeting CPS, outlining their significant impact on industrial operations. A detailed taxonomy of primary security mechanisms for CPS within IIoT–edge computing is developed, followed by a comparative analysis of our approach against existing research. The findings underscore the widespread vulnerabilities across the IIoT architecture, particularly in relation to DoS, ransomware, malware, and MITM attacks. The review emphasizes the integration of advanced security technologies, including machine learning (ML), federated learning (FL), blockchain, blockchain–ML, deep learning (DL), encryption, cryptography, IT/OT convergence, and digital twins, as essential for enhancing the security and real-time data protection of CPS in IIoT–edge computing. Finally, the paper outlines potential future research directions aimed at advancing cybersecurity in this rapidly evolving domain. Full article

Despite the fact that countless IoT applications are arising frequently in various fields, such as green cities, net-zero decarbonization, healthcare systems, and smart vehicles, the smart grid is considered the most critical cyber–physical IoT application. With emerging technologies supporting the much-anticipated smart energy systems, particularly the smart grid, these smart systems will continue to profoundly transform our way of life and the environment. Energy systems have improved over the past ten years in terms of intelligence, efficiency, decentralization, and ICT usage. On the other hand, cyber threats and attacks against these systems have greatly expanded as a result of the enormous spread of sensors and smart IoT devices inside the energy sector as well as traditional power grids. In order to detect and mitigate these vulnerabilities while increasing the security of energy systems and power grids, a thorough investigation and in-depth research are highly required. This study offers a comprehensive overview of state-of-the-art smart grid cybersecurity research. In this work, we primarily concentrate on examining the numerous threats and cyberattacks that have recently invaded the developing smart energy systems in general and smart grids in particular. This study begins by introducing smart grid architecture, it key components, and its security issues. Then, we present the spectrum of cyberattacks against energy systems while highlighting the most significant research studies that have been documented in the literature. The categorization of smart grid cyberattacks, while taking into account key information security characteristics, can help make it possible to provide organized and effective solutions for the present and potential attacks in smart grid applications. This cyberattack classification is covered thoroughly in this paper. This study also discusses the historical incidents against energy systems, which depicts how harsh and disastrous these attacks can go if not detected and mitigated. Finally, we provide a summary of the latest emerging future research trend and open research issues. Full article

This paper investigated the effect of automation processes in an industrial company engineering complex cyber-physical systems. The authors used an industry-as-laboratory approach as the research method, exploring an ongoing development project. The automation efforts focused on four areas: (1) test setup, (2) test execution, (3) test result analysis, and (4) documentation. All four areas showed promising results on increased effectiveness and/or efficiency. In particular, the automation of test result analysis will help the industrial company, KONGSBERG, reduce their main bottleneck in the test process, as well as reduce the risk of costly project delays. An automated system integration test process, facilitating iterative regression testing, will leverage the efficiency of the verification test process. Full article

Water distribution networks (WDNs) are critical infrastructures that directly impact urban development and citizens’ quality of life. Due to digitalization technologies, modern networks have evolved towards cyber-physical systems, allowing real-time management and monitoring of network components. However, the increasing volume of data from monitoring poses significant challenges to accurately estimate the hydraulic status of the system, mainly when anomalous events or unreliable readings occur. This paper presents a novel methodology for state estimation (SE) in WDNs by integrating convolutional graph networks (GCNs) with long short-term memory (LSTM) networks. The methodology is validated on two WDNs of different scales and complexities, evaluating the SE of the sensors. The capability of the GCN-LSTM model was assessed during the last two months of the time series by simulating failures to analyze its impact on sensor readings and estimation accuracy. The smaller network showed higher sensitivity of the sensors to detect failures, while the larger one evidenced more challenges in SE due to the sensor dispersion. Overall, the model achieved low prediction errors and high coefficient of determination values between the actual and simulated values, showing good performance. Likewise, the simulated failures showed that replacing the missing data with the hourly mean of the last week significantly improved the accuracy of the predictions, guaranteeing a robust SE in the event of sensor failures. This methodology provides a reliable tool for addressing various network configurations’ operational challenges. Full article

The growing prevalence of cybersecurity threats is a significant concern for railway systems, which rely on an extensive network of onboard and trackside sensors. These threats have the potential to compromise the safety of railway operations and the integrity of the railway infrastructure itself. This paper aims to examine the current cybersecurity measures in use, identify the key vulnerabilities that they address, and propose solutions for enhancing the security of railway infrastructures. The report evaluates the effectiveness of existing security protocols by reviewing current standards, including IEC62443 and NIST, as well as case histories of recent rail cyberattacks. Significant gaps have been identified, especially where modern and legacy systems need to be integrated. Weaknesses in communication protocols such as MVB, CAN and TCP/IP are identified. To address these challenges, the paper proposes a layered security framework specific to railways that incorporate continuous monitoring, risk-based cybersecurity modeling, AI-assisted threat detection, and stronger authentication methodologies. The aim of these recommendations is to improve the resilience of railway networks and ensure a safer, more secure infrastructure for future operations. Full article

This systematic literature review explores the intersection of AI-driven digital twins and IoT in creating a sustainable building environment. A comprehensive analysis of 125 papers focuses on four major themes. First, digital twins are examined in construction, facility management, and their role in fostering sustainability and smart cities. The integration of IoT and AI with digital twins and energy optimization for zero-energy buildings is discussed. Second, the application of AI and automation in manufacturing, particularly in Industry 4.0 and cyber-physical systems, is evaluated. Third, emerging technologies in urban development, including blockchain, cybersecurity, and EEG-driven systems for sustainable buildings, are highlighted. The study underscores the role of data-driven approaches in flood resilience and urban digital ecosystems. This review contributes to sustainability by identifying how digital technologies and AI can optimize energy use and enhance resilience in both urban and industrial contexts. Full article

This paper presents a conceptual framework aimed at integrating Digital Twins and cyber-physical production systems into the energy management of manufacturing facilities. To address the challenges of rising energy costs and environmental impacts, this framework combines digital modeling and customized energy management for direct manufacturing operations. Through a review of the existing literature, essential components such as physical models, a data platform, an energy optimization platform, and various interfaces are identified. Key requirements are defined in terms of functionality, performance, reliability, safety, and additional factors. The proposed framework includes the physical system, data platform, energy management system, and interfaces for both operators and external parties. The goal of this framework is to set the basis for allowing manufacturers to reduce energy consumption and costs during the lifecycle of assets more effectively, thereby improving energy efficiency in smart manufacturing. The study highlights opportunities for further research, such as real-world applications and sophisticated optimization methods. The advancement of Digital Twin technologies holds significant potential for creating more sustainable factories. Full article

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The concept of the Internet of Robotic Things (IoRT) is introduced as a potential solution, leveraging technologies like Artificial Intelligence, Cloud Computing, and Mesh Networks. This paper proposes an IoRT-based middleware that allows the communication of different types of robot operating systems in dynamic environments, using a cloud-based protocol. This middleware facilitates task assignment, training, and planning for heterogeneous robots, while enabling distributed communication via WiFi. The system operates in two control modes: local and cloud-based, for flexible communication and information distribution. This work highlights the challenges of current communication methods, particularly in ensuring information reach, agility, and handling diverse robots. To demonstrate the middleware suitability and applicability, an implementation of a proof-of-concept is shown in a touristic scenario where several guide robots can collaborate by effectively sharing information gathered from their heterogeneous sensor systems, with the aid of cloud processing or even internal communication processes. Results show that the performance of the middleware allows real-time applications for heterogeneous multi-robot systems in different domains. Full article

Current technologies could potentially solve many of the urban problems in today’s cities. Many cities already possess cameras, drones, thermometers, pollution air gauges, and other sensors. However, most of these have been designated for use in individual domains within City Hall, creating a maze of individual data domains that cannot connect to each other. This jumble of domains and stakeholders prevents collaboration and transparency. Cities need an integrated system in which data and dashboards can be shared by city administrators to better deal with urban problems that involve several sectors and to improve oversight. This paper presents a model of an integrative system to manage classes of problems within one administrative municipal domain. Our model contains the cyber-physical system’s elements: the physical object, the sensors and electronic devices attached to it, a database of collected problems, code running on the devices or remotely, and the human. We tested the model by using it on the recurring problem of potholes in city streets. An AI model for identifying potholes was integrated into applications available to citizens and operators so that they can feed the municipal system with images and the locations of potholes using their cell phone camera. Preliminary results indicate that these sensors can detect potholes with an accuracy of 91% and 99%, depending on the detection equipment used. In addition, the dashboards provide the manager and the citizen with a transparent view of the problems’ progress and support for their correct address. Full article

Rooftop PV-BESS installations often lose profitability despite policy support to accelerate capacity growth. This paper performs techno-economic analysis to assess the effect of heterogeneity in real-world conditions on the economic viability of residential rooftop PV-BESSs. The stochastic nature of generation and consumption is modeled as multiple deterministic scenarios that vary in the capacity rating of the PV system, climatic conditions (insolation and temperature), self-consumption ratio (SCR), generation–demand concurrence, and the presence/absence of capacity and storage subsidies. The results indicate that PV-BESSs are mostly profitable when operating at a capacity factor ≥ 18%. Furthermore, higher daytime electricity consumption enables greater savings with smaller storage capacities, thereby facilitating cost-effective installations at capacity factors ≥ 8%. However, low-yielding PV-BESSs and prosumers exhibiting low generation–demand concurrence require suitable subsidy allocations to become profitable. Full article

The steam turbine and its digital electro-hydraulic (DEH) control system constitute vital elements within thermal power generation. However, the complexity of the on-site environment and the high production costs of the equipment hinder users, especially novices, from fully understanding and mastering the operation mechanisms and production processes. In the realm of emerging technologies, the digital twin stands out as a powerful tool for enhancing industrial training and learning for students and operators in this field. This paper details the design and implementation of a web-based digital twin steam turbine system. Initially, a pioneering web-based digital twin architecture is proposed, featuring high-fidelity equipment modeling, web-based immersive 3D displays, algorithm design and networked implementation, and data-driven model synchronization. Subsequently, the functionalities and benefits of the digital twin system in facilitating industrial training are explained, covering aspects such as steam turbine cognitive learning, DEH system simulation learning, and condition monitoring. Finally, a case study in a real thermal power plant is presented to demonstrate the practicability and effectiveness of this web-based digital twin system. This research endeavors to contribute valuable insights and potential solutions to the growing field of web-based digital twin applications in industry. Full article

University 4.0 represents the adaptation of the Education 4.0 paradigm to the university context. The core principle is the automated supervision of the entire student learning process by an AI-driven computer assistant, allowing for timely adjustments based on the student’s progression. Critical to this process is the assistant’s ability to collect comprehensive information on all student activities within the curriculum, particularly overseeing pedagogical activities in real time to make necessary adaptations. This utilizes Educational Cyber–Physical Systems (ECPSs) to gather all relevant data and extract appropriate information effectively. This article examines a specific case of practical work involving students at two distinct geographical locations collaborating in a blended learning environment. A specialized ECPS is deployed to collect data from equipment at both sites, enabling the modeling of the pedagogical sequence, the cyber–physical system, and the data necessary for monitoring student progress in real time. Full article