Cyber-physical Systems Research Articles

Artificial intelligence for cybersecurity monitoring of cyber-physical power electronic converters: a DC/DC power converter case study.

Power electronic converters are widely implemented in many types of power applications such as microgrids. Power converters can make a physical connection between the power resources and the power application. To control a power converter, required data such as the voltage and the current of that should be measured to be used in a control application. Therefore, a communication-based structure including sensors and communication links can be used to measure the desired data and transmit that to the controllers. So, a power converter-based system can be considered as a type of cyber-physical system, and it can be vulnerable to cyber-attacks. Then, it can strongly be recommended to use a strategy for a power converter-based system to monitor the system and identify the existence of cyber-attack in the system. In this study, artificial intelligence (AI) is deployed to calculate the value of the false data (i.e., constant false data, and time-varying false data) and detect false data injection cyber-attacks on power converters. Besides, to have a precise technical evaluation of the proposed methodology, that is evaluated under other issues, i.e., noise, and communication link delay. In the case of noise, the proposed strategy is examined under noises with different signal-to-noise ratios . Further, for the case of the communication delay, the system is examined under both symmetrical (i.e., same communication delay on all inputs) and unsymmetrical communication delays (i.e., different communication delay/delays on the inputs). In this work, artificial neural networks are implemented as the AI-based application, and two types of the networks, i.e., feedforward (as a basic type) and long short-term memory (LSTM)-based network as a more complex network are tested. Finally, three important AI-based techniques (regression, classification, and clustering) are examined. Based on the obtained results, this work can properly identify and calculate the false data in the system.

AI-Enabled Internet of Things and Cyber-Physical Systems for Enhanced Disease Detection: A Survey

AI-Enabled Internet of Things and Cyber-Physical Systems for Enhanced Disease Detection: A Survey

SUSTAINABLE ARCHITECTURE AND THE ROLE OF CYBER-PHYSICAL SYSTEMS: A BIBLIOMETRIC ANALYSIS

Cyber-Physical Systems are next-generation digital systems that focus on the intricate connections and integration between the physical and digital worlds. These systems have highly integrated physical, communication, control, and computing components. The study's purpose is to carry out a bibliometric analysis to explore the research trends, collaboration network, and thematic evaluation of papers on cyber-physical systems. The most prolific countries, authors, sources, highest citations received by authors, and co-occurrences in this research domain were identified. The bibliometric analysis method was employed to empirically analyze the research on CPS in architecture published between 2007 and 2022 in the Web of Science database. All retrieved records were initially filtered by excluding 364 non–non-peer-reviewed publications and another type of languages except English. After that, we excluded 8 records based on the criteria and scope, and again excluded records for duplicates 12 records. Finally, 1336 records met the eligibility criteria. The VOS viewer and biblioshiny software was used to construct the scientific maps. A total of 1336 publications were analyzed. The study results show that the highly cited papers have been published in 297 journals by 4520 authors from 1483 organizations in 83 countries. The most prolific journal was IEEE Access, with 104 publications, 3823 citations, and an h-index of 24. The number of documents progressively increased from 2010 to 2021. Further, 2009 has no publications, and the largest number of articles was published in 2021 (n=272). Furthermore, China led the way with the most publications, active organizations, prolific authors, and international collaborations. This is the first study to look at the global trends in research on CPS in architecture and provides valuable guidelines and motivations for further research. Future studies may also examine the main hazards connected with using cyber-physical systems to manage operations across various industries and sectors.

A Simulation-Based Study on Securing Data Sharing for Situational Awareness in a Port Accident Case

The cyber–physical systems (CPSs) of various stakeholders from the mobility, logistics, and security sectors are needed to enable smart and secure situational awareness operations in a port environment. The motivation for this research arises from the challenges caused by some unexpected events, such as accidents, in such a multi-stakeholder critical environment. Due to the scale, complexity, and cost and safety challenges, a simulation-based approach was selected as the basis for the study. Prototype-level experimental solutions for dataspaces for secure data sharing and visualization of situational awareness were developed. The secure data-sharing solution relies on the application of verifiable credentials (VCs) to ensure that data consumers have the required access rights to the data/information shared by the data prosumer. A 3D virtual digital twin model is applied for visualizing situational awareness for people in the port. The solutions were evaluated in a simulation-based execution of an accident scenario where a forklift catches fire while loading a docked ship in a port environment. The simulation-based approach and the provided solutions proved to be practical and enabled the smooth study of disaster-type situations. The realized concept of dataspaces is successfully applied here for both daily routine operations and information sharing during accidents in the simulation-based environment. During the evaluation, needs for future research related to perception, comprehension, projection, trust, and security as well as performance and quality of experience were detected. Especially, distributed and secure viewpoints of objects and stakeholders toward real-time situational awareness seem to require further studies.

Cyber-Physical Scheduling System for Multiobjective Scheduling Optimization of a Suspension Chain Workshop Using the Improved Non-Dominated Sorting Genetic Algorithm II

Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm’s results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization.

An Overview of Information and Cyber Security Standards

Advances in digitalization, particularly those regarding cyber-physical systems (CPS) have stimulated the adoption of digital capabilities such as Industrial IoT, machine learning, cloud services, and the use of digital twins. The increased digital sophistication of CPS is not without risk, particularly regarding the potential for information/cyber security incidents. Whilst the need for security of enterprise information security is not new, A significant challenge is understanding what security standards may be available and applicable when developing security controls and technical measures to protect CPS. This paper explores what research is available regarding the choice and comparison of information/cyber security standards. It provides a snapshot of the security standards landscape at the start of 2024. Issues relating to development and adoption of security standards are examined, illustrated using inconsistencies in language regarding three key terms: availability, integrity, and confidentiality.

CYBER SECURITY FOR POWER GRIDS: THREATS, MITIGATION, AND FUTURE OUTLOOK

Cybersecurity is often confused with information security, although the latter focuses on human involvement, while the former sees individuals as potential targets and considers this an additional dimension. Cybersecurity discussions highlight important ethical issues impacting society and have led to the development of various frameworks addressing challenges like workforce development and personal data protection. This paper reviews these models, their limitations, and past mitigation techniques while offering future research recommendations. It explores vulnerabilities in wireless communication systems, the evolving nature of cyberattacks, quantum cryptography, and advanced key management schemes. Furthermore, it emphasizes the growing cybersecurity risks in power grids due to the integration of computing and communication capabilities into cyber-physical systems (CPS). A notable example is the 2015 cyberattack on Ukraine's power grid, illustrating the urgent need for improved security. This paper presents a comprehensive review of cybersecurity standards, emerging threats, and challenges in power systems. Index Terms—Cybersecurity, Cyber Threats frameworks, workforces, threats, techniques web 3.0, Implications

Air vehicle system health and asset management: modeling, simulation, and decision support

AbstractEffective Asset Management (AM) is essential to achieve operational excellence in the context of large complex Cyber-Physical Systems (CPSs). It involves coordinated activities across all life-cycle stages, including information and service exchange within and between adjacent air operations domains, for effective air operations and collaboration. For enterprises and military air operations, AM presuppose information and service exchange between the adjacent domains of air operations. Important aspects of aviation AM are that CPSs include Integrated Vehicle Health Management (IVHM), CPS models, and Digital Twin (DT), as central concepts used to predict and optimize asset performance. This article provides an overview of the aviation AM phenomenology, environment, and challenges, and how they may impact envisioned realisations of effective AM solutions for support to enterprise air vehicle operations. The article serves as an extension of the paper Cyber-physical Asset Management of Air Vehicle Systems presented at the International Congress and Workshop of Industrial AI and Maintenance (Candell et al., in Proceedings of the IAI2023–7th international congress and workshop on industrial AI and eMaintenance, Luleå, Sweden, 2023). A comprehensive approach is presented, comprising interdependent dimensions of the problem domain, and how they may be integrated into a framework and a platform concept addressing aviation AM needs.

A Comprehensive Review on Deep Learning Techniques on Cyber Attacks on Cyber Physical Systems

A Comprehensive Review on Deep Learning Techniques on Cyber Attacks on Cyber Physical Systems

A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City

A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City

Low-altitude intelligent transportation: System architecture, infrastructure, and key technologies

In the context of the burgeoning low-altitude economy, low-altitude intelligent transportation (LAIT) has emerged as the focal point of research. This study comprehensively explores the current state, challenges, and future development prospects of LAIT from three key aspects: system architecture, infrastructure, and critical technologies. First, we propose a future LAIT system framework based on a cyber-physical system (CPS) layered architecture to provide a potential solution for urban air transport. Second, we introduce a framework for the entire lifecycle and management chain of the LAIT system, offering an in-depth analysis of each stage from design, construction, and operation to management, with the aim of realizing intelligent management and operation of low-altitude transportation. Finally, this study discusses the technical, security, and social challenges that future LAIT will face in the context of Industry 4.0, while envisioning the pathways of technological innovation. It summarizes the need for advanced infrastructure and breakthroughs in key technologies such as the Internet of Things (IoT), 6G, artificial intelligence (AI), and low-altitude geographic information system. This study provides a systematic framework and technical guidelines for the future development of low-altitude intelligent transportation, supporting continuous innovation, and upgrading the low-altitude economy.

Robust and efficient three-factor authentication solution for WSN-based industrial IoT deployment

The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.

Robust Optimization Research of Cyber-Physical Power System Considering Wind Power Uncertainty and Coupled Relationship.

To mitigate the impact of wind power uncertainty and power-communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupled relationship of energy supply and control dependencies between the power and communication networks. Next, a bi-level mixed-integer robust optimization model is developed to improve power system resilience, incorporating constraints related to the coupling strength, electrical characteristics, and traffic characteristics of the information network. The upper-level model seeks to minimize load shedding by optimizing DC power flow using fuzzy chance constraints, thereby reducing the risk of power imbalances caused by random fluctuations in wind power generation. Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology-function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. Finally, a modified IEEE 39-bus test system with intermittent wind power is used as a case study. Random attack simulations demonstrate that, under the highest link failure rate and wind power penetration, Model 2 outperforms Model 1 by reducing the load loss ratio by 23.6% and improving the node survival ratio by 5.3%.

A deep learning deviation-based scheme to defend against false data injection attacks in power distribution systems

Defending against false data injection attacks (FDIAs) in cyber-physical power systems is crucial. Detection in power distribution systems is complex due to load variations, uncertainties, and fewer meters. Defense strategies include model-driven and data-driven approaches, but model-based methods can trigger false alarms due to threshold setting issues. The current research proposes a novel data-driven method to address threshold setting issues in detecting and localizing FDIAs in power distribution systems. First, a dataset is created by recording estimated measurement values using an unscented Kalman filter and weighted least squares across various attack scenarios. These estimated measurements are then fed into a deep artificial neural network (ANN) for binary classification to detect attacks. The output, along with the estimated measurements, is used by another ANN to localize the corrupted meter zone. This deep learning-based approach improves threshold setting over the common chi-square method. Results show that the proposed deep learning method for FDIA detection and localization outperforms a recently proposed ensemble of shallow models. The area under the curve value increases by about 5% with lower training time. The approach is also effective against previously unseen attack strategies and different feeder topologies.

463 Cyber-Physical Systems with robots and AI for precision dairy farming

Abstract The emergence of precision livestock farming (PLF) offers a transformative lens through which to prioritize animal welfare. At the heart of this transformation lies the rumen, a complex microbial ecosystem within dairy cows responsible for converting feed into nutrients and impacting cow health, productivity, and environmental footprint. Despite its pivotal role, our understanding of the inner workings of the rumen remains limited due to a lack of real-time monitoring tools. My research leverages AI and robotics in PLF to address this gap, focusing on developing a closed-loop system for continuous, in-vivo rumen monitoring. This system employs cyber-physical systems (CPS) embedded with modular sensors and adaptive algorithms. These sensor modules, powered by AI-based data tracking, autonomously monitor rumen temperature, humidity, pressure, and methane concentration, enabling early detection of imbalances and prompt interventions to maintain optimal rumen health. A key innovation is the self-powered in-vivo robot capable of traversing the rumen’s stratified layers. Guided by AI-powered localization, this robot unlocks unparalleled access to new data, aiding in identifying specific regions associated with conditions like subacute ruminal acidosis (SARA) and methane production. This non-invasive approach eliminates the need for traditional cannulation, minimizing stress on the animal and opening new avenues for animal scientists. The impact of this research extends beyond individual cow health. By optimizing rumen function, we can improve feed efficiency, reduce methane emissions, and promote overall sustainability in dairy farming. Additionally, real-time data insights pave the way for precision nutrition, tailored interventions, and improved disease prevention, directly enhancing animal welfare. This novel approach to rumen monitoring represents a significant step towards realizing the full potential of PLF. By integrating AI and robotics, we can usher in a future where animal welfare and environmental sustainability go hand-in-hand, ultimately contributing to a more responsible and ethical dairy industry.

Resilient Self-Triggered Model Predictive Control of Cyber-Physical Systems Under Two-Channel False Data Injection Attacks

Abstract This paper presents a novel resilient self-triggered model predictive control (ST-MPC) method to alleviate potential threats of false data injection (FDI) attacks on cyber-physical systems (CPSs). First, considering that the data transmitted via the sensor-to-controller (S–C) and controller-to-actuator (C–A) channels in CPS may be tampered with by FDI attacks, a novel input reconstruction strategy combined with the ST-MPC mechanism is proposed to alleviate the threats of FDI attacks while reducing the computational and communication resources, in which key optimal control signals are selected and protected based on systematic performance inequalities. Correspondingly, a resilient ST-MPC algorithm combined with the dual-mode strategy is further proposed. Moreover, the iterative feasibility and the closed-loop stability are strictly demonstrated. Finally, the effectiveness of the proposed strategy is verified via a simulation study.

Resilient adaptive quantized control for nonlinear cyber-physical systems under deception attacks

Resilient adaptive quantized control for nonlinear cyber-physical systems under deception attacks

SLACPSS: Secure Lightweight Authentication for Cyber–Physical–Social Systems

The concept of Cyber–Physical–Social Systems (CPSSs) has emerged as a response to the need to understand the interaction between Cyber–Physical Systems (CPSs) and humans. This shift from CPSs to CPSSs is primarily due to the widespread use of sensor-equipped smart devices that are closely connected to users. CPSSs have been a topic of interest for more than ten years, gaining increasing attention in recent years. The inclusion of human elements in CPS research has presented new challenges, particularly in understanding human dynamics, which adds complexity that has yet to be fully explored. CPSSs are a base class and consist of three basic components (cyberspace, physical space, and social space). We map the components of the metaverse with that of a CPSS, and we show that the metaverse is an implementation of a Cyber–Physical–Social System (CPSS). The metaverse is made up of computer systems with many elements, such as artificial intelligence, computer vision, image processing, mixed reality, augmented reality, and extended reality. It also comprises physical systems, controlled objects, and human interaction. The identification process in CPSSs suffers from weak security, and the authentication problem requires heavy computation. Therefore, we propose a new protocol for secure lightweight authentication in Cyber–Physical–Social Systems (SLACPSSs) to offer secure communication between platform servers and users as well as secure interactions between avatars. We perform a security analysis and compare the proposed protocol to the related previous ones. The analysis shows that the proposed protocol is lightweight and secure.

Time-Sensitive Networking (TSN) for Industrial Automation: Current Advances and Future Directions

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time-Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications.

A Self-Adaptive Neighborhood Search Differential Evolution Algorithm for Planning Sustainable Sequential Cyber–Physical Production Systems

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters.