Cyber-Physical Systems Components Research Articles

Deterministic Coordination across Multiple Timelines

We discuss a novel approach for constructing deterministic reactive systems that revolves around a temporal model that incorporates a multiplicity of timelines. This model is central to Lingua Franca ( LF ), a polyglot coordination language and compiler toolchain we are developing for the definition and composition of concurrent components called reactors, which are objects that react to and emit discrete events. Our temporal model differs from existing models like the logical execution time (LET) paradigm and synchronous languages in that it reflects that there are always at least two distinct timelines involved in a reactive system; a logical one and a physical one—and possibly multiple of each kind. This article explains how the relationship between events across timelines facilitates reasoning about consistency and availability across components in cyber-physical systems (CPSs).

Improving Engineering Studentsʼ Understanding of Components of Cyber-Physical Systems Using a Team Project-Based Teaching Method

This article describes a teaching method that was used to teach second-year mechatronics and robotics students the components of cyber-physical systems. In view of the importance of team learning, the group was divided into five sub-groups (student teams) in order to increase efficiency by comparing the tasks undertaken by the teams. A line following robot was used for the teaching. This course was designed to last 48 academic hours. As a result, the students noted that after building the robot in teams, their attitude towards cyber-physical systems changed, and they became motivated to carry out larger projects related to cyber-physical systems.

A novel adaptive sampling algorithm for cyber-physical systems

AbstractSensors are the main components in Cyber-Physical Systems (CPS), which transmit large amounts of physical values and big data to computing platforms for processing. On the other hand, the embedded processors (as edge devices in fog computing) spend most of their time reading the sensor signals as compared with computing time. The impact of sensors on the performance of fog computing is very great, thus, the enhancement of the reading time of sensors will positively affect the performance of fog computing, and solves the CPS challenges such as delay, timed precision, temporal behavior, energy, and cost. In this paper, we propose an algorithm based on the 1st derivative of the sensor signal to generate an adaptive sampling frequency. The proposed algorithm uses an adaptive frequency to capture the sudden and rapid change in sensor signal in the steady state. Finally, we realize and tested it using the Ptolemy II Modeling Environment.

The Theory of Structuring Multifunctional Elements of Complex Systems

This article considers the problem of formalization of elements and binary connections of multifunctional data, which are components of cyber-physical systems. The concept of a free and active element of a complex system and its classes relatively interact with environment resources and consumer information messages. Six attributes of binary relationship elements such as information, material, energy, optics, management, and general have been classified. The table shows four classes of active interaction between elements of complex systems of signs. The concept of an element of a complex system has been defined. The basics of the theory of solving the task of structuring multifunctional data have been outlined, which made it possible to improve the system characteristics of the components of cyberphysical systems due to the reduction of structural, hardware, and time complexity.

Internet of Things Driven Automated Production Systems using Machine Learning

With the advent of Industry 4.0, integrating Cyber-Physical Systems (CPS) and the Internet of Things (IoT) has become crucial in automated production. This study examines the interplay between technological advancements and human factors, particularly the social aspects within this dynamic environment. In the context of Industry 4.0, where CPS and IoT intersect, human factors significantly impact the design, implementation, and operation of automated production systems. This research highlights the mutually beneficial interactions among CPS, IoT, and human-centric components, focusing on how these technologies influence social interactions in automated industrial settings. The integration of intelligent robots and human operators fosters new collaboration and communication models. Understanding these social interactions is key to enhancing the effectiveness and productivity of Industry 4.0 manufacturing. Additionally, the study examines the role of social networks, where CPS and IoT technologies facilitate essential connections between humans and machines. In summary, this research emphasizes the need to incorporate social aspects into developing and implementing Industry 4.0 technologies. Recognizing the impact of CPS, IoT, and human factors on social interactions and networks enables a more comprehensive and efficient technological integration in Industry 4.0.

Security strategy for autonomous vehicle cyber-physical systems using transfer learning

Cyber-physical systems (CPSs) are emergent systems that enable effective real-time communication and collaboration (C&C) of physical components such as control systems, sensors, actuators, and the surrounding environment through a cyber communication infrastructure. As such, autonomous vehicles (AVs) are one of the fields that have significantly adopted the CPS approach to improving people's lives in smart cities by reducing energy consumption and air pollution. Therefore, autonomous vehicle-cyber physical systems (AV-CPSs) have attracted enormous investments from major corporations and are projected to be widely used. However, AV-CPS is vulnerable to cyber and physical threat vectors due to the deep integration of information technology (IT), including cloud computing, with the communication process. Cloud computing is critical in providing the scalable infrastructure required for real-time data processing, storage, and analysis in AV-CPS, allowing these systems to work seamlessly in smart cities. CPS components such as sensors and control systems through network infrastructure are particularly vulnerable to cyber-attacks targeted by attackers using the communication system. This paper proposes an intelligent intrusion detection system (IIDS) for AV-CPS using transfer learning to identify cyberattacks launched against connected physical components of AVs through a network infrastructure. First, AV-CPS was developed by implementing the controller area network (CAN) and integrating it into the AV simulation model. Second, the dataset was generated from the AV-CPS. The collected dataset was then preprocessed to be trained and tested via pre-trained CNNs. Third, eight pre-trained networks were implemented, namely, InceptionV3, ResNet-50, ShuffleNet, MobileNetV2, GoogLeNet, ResNet-18, SqueezeNet, and AlexNet. The performance of the implemented models was evaluated. According to the experimental evaluation results, GoogLeNet outperformed all other pre-rained networks, scoring an F1- score of 99.47%.

A Modeling and Verification Method of Cyber-Physical Systems Based on AADL and Process Algebra

Cyber-Physical Systems (CPS) are the next generation of intelligent systems that integrate information control devices with physical resources. With increasingly close connections between CPS components and frequent interactions, potential defects grow exponentially, rendering the operating environment of CPS unreliable. Therefore, research on methods and theories to ensure the correctness, safety and reliability of CPS is not only an important research topic but also an inevitable challenge. In this paper, we propose a CPS modeling and verification method based on Architecture Analysis & Design Language (AADL) and process algebra to address this challenge. Due to the continuous, time-constrained, stochastic, uncertain and concurrent characteristics of CPS, this paper considers both flexibility and rigor in the modeling process. We first extend the ability of AADL to describe the multiple characteristics of CPS and propose Hybrid Probability-AADL (HP-AADL). Second, this paper introduces conditional execution, conditional interruption and probability operators into Temporal Calculus of Communication Systems (TCCS) and designs a new formal modeling language Hybrid Probability-Temporal Calculus of Communication Systems (HP-TCCS). However, HP-AADL is a semi-formal modeling language that cannot be directly used for formal verification, it cannot strictly guarantee the quality of the established CPS models, including its functional correctness and safety. Therefore, this paper proposes transformation rules from HP-AADL to HP-TCCS, which enables model checking of CPS models described in HP-AADL within HP-TCCS. Additionally, this paper designs a new formal specification language HPAT-Spatial Temporal Logic (HPAT-STL) based on Probabilistic Computation Tree Logic (PCTL) and Spatial Logic, which characterizes the temporal, probabilistic and spatial properties of CPS model. To achieve formal verification of HP-TCCS model and HPAT-STL formulas, this paper proposes a model checking algorithm HPAT-Model Checking Algorithm (HPAT-MCA). Finally, we discuss a typical CPS example to demonstrate the effectiveness of our proposed method in ensuring correct, safe and reliable CPS.

A Novel Learning-Based Attack Detection System for Enhancing Security in Cyber-Physical Environments

An Intrusion Detection System (IDS) is an essential part of the network as it contributes towards securing the network against various vulnerabilities and threats. Over the past decades, there has been a comprehensive study in the field of IDS, and various approaches have been developed to design intrusion detection and classification system. With the proliferation in the usage of Deep Learning (DL) techniques and their ability to learn data extensively, we aim to design Deep Neural Network (DNN) based IDS. In this study, we aim to focus on enhancing the performance of DNN-based IDS in Cyber-Physical Systems (CPS). CPS combine physical processes, networking, and computation. The integration of CPS components could seriously jeopardise the security of CPS settings because of the physical limitations. The vulnerability of CPS to cyberattacks has grown with the development of IoT and other physical systems. As cyber-physical systems refer to the intersection of your organization’s technology and IT infrastructure and its physical assets, ensuring access and data security through advanced methods prevents any cyber-attack from damaging your physical assets and thereby disrupting your business flow. Conventional cyber and network security procedures fail to guarantee data privacy and security in CPS contexts. This research aims to provide a cutting-edge attack detection method based on learning for CPS environments. The paper suggests using MLP-based smart attack control systems to increase the CPSs' security. Performance analysis is presented in terms of different evaluation metrics such as accuracy, precision, recall, f-score, and False Positive Rate (FPR), and the results are compared with existing feature selection techniques. The effectiveness of the suggested model was confirmed by comparing the outcomes with those of other successful deep learning-based algorithms, including the Gaussian Naive Bayes algorithm, SVM, and logistic regression. Comparative results demonstrate that the suggested method outperforms existing learning models with an exceptional accuracy of 99.52%.

Artificial intelligence as the cognitive value of heuristic models

This article is devoted to heuristic modeling of the structure of interconnections in nano-, bio- and information technologies and in cyber-physical systems, graphic images of which allow studying the fine structure of information sources of various natures. It is shown here that the information content of models of the structure of interconnections of nano-, bio-, information technologies and components of cyber-physical systems is due to the use of a fractal triangle and logic. The current work harmonizes human-computer interaction through digital complementary methods for studying the structure of information sources of various natures, as well as using the circle of natural colors by I. Goethe and a new interpretation of the Star of David. Here we show that balanced conjugate triangles in heuristic models allow inversion of transition states of information sources of different nature. Static, dynamic and statistical generalized heuristic models are based on the balance of resources and the asymmetry of feedback between the elements of a complex dynamic system. Their relationship determines the variability of cyclic processes, and the complementarity of dynamic and statistical heuristic models is associated with the dualism of nature. A systematic analysis of explicit and hidden relationships in information flows of various natures opens up qualitatively new opportunities for a cognitive dialogue with nature and an understanding of reality. The complementarity of heuristic and cognitive models in the transdisciplinary cognitive space provides innovative potential for solving urgent problems of education, science and new technologies. This is especially important for the further development of artificial intelligence and the harmonization of human-computer interaction. In particular, online forecasting of the transient functional states of information sources of various nature under unforeseen conditions simplifies the interdisciplinary exchange of ideas, methods and technologies. Since the number of honeycomb structures in nature is two to three orders of magnitude greater than that of artificial models, their connection with the harmony of colors in nature contributes to the development of emotional intelligence, creating new opportunities for solving relevant security problems.

Deep Learning for Secure Wireless Communications in Cyber Physical Systems

Deep learning has strong potential using data-driven solutions to improve the performance of cyber physical systems (CPS) in utilizing limited spectrum resources in wireless communications. Deep learning is also a powerful method of data exploration to learn about “normal” and “abnormal” behavior according to how CPS components and devices interact with each other. Furthermore, deep learning methods are important in predicting new attacks, which are often mutations of previous attacks because they can intelligently predict future unknown attacks by learning from existing examples. The topic of deep learning for secure wireless communications in CPS has gained much momentum in the industry and the research community very recently. In this issue of IEEE Wireless Communications, we are pleased to present a Feature Topic on “Deep Learning Driven Secure Communication for Cyber Physical Systems” to bring together researchers, industry practitioners, and individuals working on the related areas to address some of the technical challenges faced by the research topics. This Feature Topic includes a collection of 10 articles chosen by Guest Editors W. Wei, C.-H. Hsu, V. Piuri, and A. Rayes. We are also pleased to present a second Feature Topic on “Enhanced Fronthaul for 5G and Beyond” that includes a collection of four articles chosen by Guest Editors Y. Luo, P. Chanclou, K. E. Skouby, H. Zhao, and K. Asaka. Thanks to the two Guest Editor teams who did an excellent job in editing these two Feature Topics for our readers. Please stay tuned for new developments in these research areas, and read the editorials and articles in the two Feature Topics.

Security Perspective Analysis of Industrial Cyber Physical Systems (I-CPS): A Decade-wide Survey

Considering the exceptional growth of Cyber Physical Systems (CPSs), multiple and potentially grave security challenges have emerged in this field. Different vulnerabilities and attacks are present in front of new generation CPSs, such as Industrial CPS (I-CPS). The underlying non-uniform standards, device heterogeneity, network complexity, etc., make it difficult to offer a systematized coverage on CPS security in an industrial environment. This work considers the security perspective of I-CPSs, and offers a decade-wide survey including different vulnerabilities, attacks, CPS components, and various other aspects. The comparative year-wise analysis of the existing works w.r.t objective, approach referred, testbed used and derived inference, is also presented over a decade. Additionally, the work details different security issues and research challenges present in I-CPS. This work attempts to offer a concise and precise literature study focused on the state-of-the-art I-CPS security. This work also encourages the young researchers to explore the wide possibilities present in this emerging field.

A guide to design uncertainty-aware self-adaptive components in Cyber–Physical Systems

Cyber–physical systems (CPS) need to be designed to deal with various forms of uncertainties associated with data contributing to the system’s knowledge and its belief of the environment. Dealing with uncertainties requires adopting appropriate methods, which then allow for making the right decisions and carrying out the right actions based on imperfect information. However, CPS design is difficult and requires attention to many variables during the design process. For instance, choosing and incorporating methods for dealing with uncertainties into CPS design requires identifying the kinds of uncertainties at hand as well as knowledge of suitable methods and their application. While inspiration can be found in other CPS designs, the details of dealing with uncertainties in other CPSs can be confounded by domain-specific terminology, context, and requirements. To make this aspect of CPS design less daunting, we aim at providing an overview of approaches dealing with uncertainties in the design of CPS targeting collective behavior. To this end, we present a systematic review of relevant scientific projects with industrial leadership and synthesis of relations between system features, the kinds of uncertainties, and methods used to deal with them. The results provide an overview of uncertainties and challenges across different domains, and reason about a guide for designing uncertainty-aware self-adaptive components in CPS.

VERCASM-CPS: Vulnerability Analysis and Cyber Risk Assessment for Cyber-Physical Systems

Since Cyber-Physical Systems (CPS) are widely used in critical infrastructures, it is essential to protect their assets from cyber attacks to increase the level of security, safety and trustworthiness, prevent failure developments, and minimize losses. It is necessary to analyze the CPS configuration in an automatic mode to detect the most vulnerable CPS components and reconfigure or replace them promptly. In this paper, we present a methodology to determine the most secure CPS configuration by using a public database of cyber vulnerabilities to identify the most secure CPS components. We also integrate the CPS cyber risk analysis with a Controlled Moving Target Defense, which either replaces the vulnerable CPS components or re-configures the CPS to harden it, while the vulnerable components are being replaced. Our solution helps to design a more secure CPS by updating the configuration of existing CPS to make them more resilient against cyber attacks. In this paper, we will compare cyber risk scores for different CPS configurations and show that the Windows® 10 build 20H2 operating system is more secure than Linux Ubuntu® 20.04, while Red Hat® Enterprise® Linux is the most secure in some system configurations.

Analysing Mission-critical Cyber-physical Systems with AND/OR Graphs and MaxSAT

Cyber-Physical Systems (CPS) often involve complex networks of interconnected software and hardware components that are logically combined to achieve a common goal or mission; for example, keeping a plane in the air or providing energy to a city. Failures in these components may jeopardise the mission of the system. Therefore, identifying the minimal set of critical CPS components that is most likely to fail, and prevent the global system from accomplishing its mission, becomes essential to ensure reliability. In this article, we present a novel approach to identifying the Most Likely Mission-critical Component Set (MLMCS) using AND/OR dependency graphs enriched with independent failure probabilities. We address the MLMCS problem as a Maximum Satisfiability (MaxSAT) problem. We translate probabilities into a negative logarithmic space to linearise the problem within MaxSAT. The experimental results conducted with our open source tool LDA4CPS indicate that the approach is both effective and efficient. We also present a case study on complex aircraft systems that shows the feasibility of our approach and its applicability to mission-critical cyber-physical systems. Finally, we present two MLMCS-based security applications focused on system hardening and forensic investigations.

Digital Twin Generation: Re-Conceptualizing Agent Systems for Behavior-Centered Cyber-Physical System Development.

Cyber-Physical Systems (CPS) form the new backbone of digital ecosystems. Upcoming CPS will be operated on a unifying basis, the Internet of Behaviors (IoB). It features autonomous while federated CPS architectures and requires corresponding behavior modeling for design and control. CPS design and control involves stakeholders in different roles with different expertise accessing behavior models, termed Digital twins. They mirror the physical CPS part and integrate it with the digital part. Representing role-specific behaviors and provided with automated execution capabilities Digital twins facilitate dynamic adaptation and (re-)configuration. This paper proposes to conceptualize agent-based design for behavior-based Digital twins through subject-oriented models. These models can be executed and, thus, increase the transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate the configuration of CPS. Subject-oriented runtime support enables dynamic adaptation and the federated use of CPS components.

Model-Driven Engineering Tools and Languages for Cyber-Physical Systems–A Systematic Literature Review

The development of Cyber-physical Systems (CPS) draws more interest from both researchers and industrial practitioners considering the opportunities they offer in almost all areas of industry. However, the engineering and management of CPS are challenging tasks due to their inherent heterogeneity and complexity characteristics. Regarding the development of CPS, there currently exists no standard methodology owing to the complexity of the domain. One of the key approaches to reduce the development complexity for CPS is Model-driven Engineering (MDE), which is frequently used in many industrial domains for software development to increase the level of platform abstraction. Nevertheless, it is always almost challenging, especially for the new researchers in this field, to determine the appropriate tools and languages to perform a particular MDE activity during CPS development. To the best of our knowledge, there is no guideline that demonstrates which language(s)/tool(s) to use for the various MDE techniques/phases for the development of CPS. This paper presents a Systematic Literature Review (SLR) study that focuses on identifying and classifying the recent research practices pertaining to CPS development by applying MDE approaches. With the objective of providing a general overview of the field, the study evaluates 140 research papers published during 2010–2018. Accordingly, a precise view of the various MDE tools and languages used in the development life-cycle of CPS, addressed MDE techniques/activities, and targeted CPS components is presented. We believe that the conducted study will guide researchers and practitioners to identify appropriate tools and languages according to the system requirements. It may also help in getting an overall understanding of the research trends for further research and development on the MDE of CPS.

Cyber-Physical Systems: Challenges and Future Directions

Cyber-Physical Systems (CPS) integrate computational algorithms with physical components, enabling advanced functionalities in various domains. This paper explores the challenges and future directions of CPS, focusing on security, safety, privacy, and interoperability. In terms of security, CPS face threats to confidentiality, integrity, and availability, necessitating advancements in intrusion detection, prevention systems, and secure communication protocols. Safety improvements include predictive maintenance and autonomous decision-making systems to enhance reliability and resilience. Privacy-enhancing techniques like anonymization and user-centric controls are crucial for data protection. Interoperability solutions, such as middleware and semantic frameworks, facilitate seamless integration among heterogeneous CPS components. Future directions involve leveraging machine learning and AI for security, integrating digital twins for predictive maintenance, and enhancing user-centric privacy controls. These advancements are vital for the continued development and adoption of CPS in diverse applications.

Cybersecurity Attacks in Vehicular Sensors

Today's modern vehicles contain anywhere from sixty to one-hundred sensors and exhibit the characteristics of Cyber-Physical-Systems (CPS). There is a high degree of coupling, cohesiveness, and interactions among vehicle's CPS components (e.g., sensors, devices, systems, systems-of-systems) across sensing, communication, and control layers. Cyber-attacks in the sensing or communication layers can compromise the security of the control layer. This paper provides a detailed review of potential cyber threats related to the sensing layer. Notably, the focus is mainly towards two categories of sensors: vehicle dynamics sensors (e.g., Tire Pressure Monitoring Systems (TPMS), magnetic encoders, and inertial sensors) and environment sensors (e.g., Light Detection and Ranging (LiDAR), ultrasonic, camera, Radio Detection and Ranging (Radar) systems, and Global Positioning System (GPS) units). The paper also offers perspectives through existing countermeasures from literature and stresses the need for data-driven cybersecurity solutions.

Frost Monitoring Cyber–Physical System: A Survey on Prediction and Active Protection Methods

Frost damage in broadacre cropping and horticulture (including viticulture) results in substantial economic losses to producers and may also disrupt associated product value chains. Frost risk windows are changing in timing, frequency, and duration. Faced with the increasing cost of mitigation infrastructure and competition for resources (e.g., water and energy), multiperil insurance, and the need for supply chain certainty, producers are under pressure to innovate in order to manage and mitigate risk. Frost protection systems are cyber-physical systems (CPSs) consisting of sensors (event detection), intelligence (prediction), and actuators (active protection methods). The Internet-of-Things communication protocols joining the CPS components are also evaluated. In this context, this article introduces and reviews existing methods of frost management. This article focuses on active protection methods because of their potential for real-time deployment during frost events. For integrated frost prediction and active protection systems, prediction method, sensor types, and integration architecture are assessed, research gaps are identified and future research directions proposed.

Graphical modeling notation for data collection and analysis architectures in cyber-physical systems of systems

Abstract Industrie 4.0 and data analytics blur the separation of operational and information technology that prevailed for industrial automation over the last decades. Decentralized control systems for production plants and robot cells collaborate actively with higher-level systems for big data analytics. In parallel, the complexity of designing and operating a system architecture for data collection and analysis increases dramatically as more experts from different domains get involved. Graphical modeling notations facilitate the design process by formalizing implicit knowledge, but currently do not exist for the combined description of field layer and data analytics. Modeling the system architecture, relevant constraints, and requirements early during the design process can increase the efficiency of the system development and deployment, especially as experts with various backgrounds are involved. In this contribution, a new graphical notation is introduced and evaluated in three industrial case-studies. The notation describes the underlying hardware and software components of cyber-physical systems of systems, the flow of data, and relevant constraints. The evaluation proved that the notation is powerful in supporting the engineering of data collection and analysis architectures in industrial automation. Future work is related to extending the scope of the modeling approach to include safety applications and real-time considerations on the field level.