Cyber-physical System Framework Research Articles

Disaster recovery strategies for cyber-physical systems considering the Interdependence

Improving the resilience of Cyber-Physical Systems (CPS) against extreme events is vital. Current research, overly focused on power system restoration, often misses the complex interplay between the physical and cyber aspects of these systems, leading to potential vulnerabilities. This paper first proposes a framework for CPS considering interdependence. The framework accounts for cyber-physical collaborative recovery and the connectivity between cyber nodes and control center in the cyber system. The model is built considering factors such as repair crew, repair time, and network reconstruction, aimed at rapidly restoring the structural and functional resilience of CPS in the shortest possible time. Next, it integrates complex network theory to propose different repair strategies based on structural and functional indicators. Finally, the repair efficiency of the proposed strategies is comprehensively evaluated using structural and functional metrics. Simulation results show that the repair efficiency of CPS is superior to traditional power grids, and moderate reduction in the repair time of cyber nodes and a decrease in network size both contribute to enhancing repair efficiency.

Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Smart manufacturing (SM) systems utilize run-time data to improve productivity via intelligent decision-making and analysis mechanisms on both machine and system levels. The increased adoption of cyber-physical systems in SM leads to the comprehensive framework of cyber-physical manufacturing systems (CPMS) where data-enabled decision-making mechanisms are coupled with cyber-physical resources on the plant floor. Due to their cyber-physical nature, CPMS are susceptible to cyber-attacks that may cause harm to the manufacturing system, products, or even the human workers involved in this context. Therefore, detecting cyber-attacks efficiently and timely is a crucial step toward implementing and securing high-performance CPMS in practice. This paper addresses two key challenges to CPMS cyber-attack detection. The first challenge is distinguishing expected anomalies in the system from cyber-attacks. The second challenge is the identification of cyber-attacks during the transient response of CPMS due to closed-loop controllers. Digital twin (DT) technology emerges as a promising solution for providing additional insights into the physical process (twin) by leveraging run-time data, models, and analytics. In this work, we propose a DT framework for detecting cyber-attacks in CPMS during controlled transient behavior as well as expected anomalies of the physical process. We present a DT framework and provide details on structuring the architecture to support cyber-attack detection. Additionally, we present an experimental case study on off-the-shelf 3D printers to detect cyber-attacks utilizing the proposed DT framework to illustrate the effectiveness of our proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by developing a general-purpose and extensible digital twin-enabled cyber-attack detection framework for manufacturing systems. Existing works in the field consider specialized attack scenarios and models that may not be extensible in practical manufacturing scenarios. We utilize digital twin (DT) technology as a key enabler to develop a systematic and extensible framework where we identify the abnormality of a resource and detect if the abnormality is due to an attack or an expected anomaly. We provide several remarks on how our proposed framework can extend existing industrial control systems (ICS) and can accommodate further extensions. The presented DTs utilize data-driven machine learning models, physics-based models, and subject matter expert knowledge to perform detection and differentiation tasks in the context of expected anomalies and model-based controllers that control the manufacturing process between multiple setpoints. We utilize a model predictive controller on an off-the-shelf 3D printer to run the process, and stage anomalies and cyber-attacks that are successfully detected by the proposed framework.

Bloom’s IoT Taxonomy towards an effective Industry 4.0 education: Case study on Open-source IoT laboratory

AbstractIn the rapidly evolving landscape of Industry 4.0, the demand for skilled professionals well-versed in Internet of Things (IoT) technologies is escalating. However, a significant challenge faced in educational settings is the lack of comprehensive and effective methodologies for imparting practical knowledge and skills in IoT. This paper presents an approach for designing and implementing an Internet of Things (IoT) laboratory in which students may practice and comprehend many components of an IoT environment, such as analysis of sensor data, IoT platform development, and setup of messaging protocols. The teaching methodology adopts a Cyber-Physical System (CPS) framework, which integrates teachers, classrooms, and resources to create a comprehensive learning environment. Bloom's taxonomy is employed to assess the efficacy of the suggested technique in terms of cognitive skills and knowledge acquisition. The evaluation procedure demonstrated the advantages of a hybrid learning environment, which integrated both face-to-face and remote instruction. The case study is conducted in an engineering higher education course on first year students. The findings of this paper lay the groundwork for a remotely deliverable IoT training course, contributing to the advancement of IoT education and equipping students with the necessary skills for the evolving landscape of Industry 4.0.

ГЕНЕЗИС ПАРАДИГМЫ УПРАВЛЕНИЯ ИННОВАЦИЯМИ С УЧЕТОМ СОЦИОТЕХНИЧЕСКОГО ПОДХОДА

The article examines the main stages in development of models of the innovation process, reflecting changes in experts’ ideas about the nature of innovation. It is shown that classical models of the innovation process, which have gone through a number of stages of their development, do not fully reflect modern features of external environment and trends in its development in the context of digitalization. The leading role of knowledge for making effective innovation-oriented management decisions and the need for further convergence of the methods of innovation theory and the sociotechnical approach are substantiated. The influence of digitalization on development of modern ideas about the nature of innovation management during transition to a knowledge society is considered. A direction for further development of developed approach is proposed in direction of studying the nature of artificial intelligence and its application in making management decisions within the framework of cyber-physical systems.

FC-CACPHS: fog-cloud assisted context-aware framework for cyber-physical healthcare system

FC-CACPHS: fog-cloud assisted context-aware framework for cyber-physical healthcare system

Block-Chain based Trustful Data-Driven Cyber-Physical Social System Framework for Urban Air Mobility

This paper provides an overview of existing research on urban mobility issues and highlights the importance of incorporating smart technologies, particularly in the context of introducing air-based transportation, to enhance the efficiency of urban mobility systems. The integration of air mobility will bring about increased complexities in managing urban transportation due to the growing volume of data, services, and infrastructure that governing bodies will have to handle. Urban air mobility infrastructure consists of interconnected and intricate systems that demonstrate the seamless integration of physical and software components. To address these complexities, the Cyber-physical social system (CPSS) is proposed as an effective approach to minimize complications within and between these subsystems. Establishing public-private partnerships and ensuring secure transactions are vital prerequisites for embracing current technological advancements and shaping the future of urban air mobility services. In this regard, blockchain technology emerges as a reliable protocol that guarantees the integrity of data exchanged among the decentralized network of stakeholders, eliminating the need for a trusted intermediary.

Ulgen: A Runtime Assurance Framework for Programming Safe Cyber–Physical Systems

We present ULGEN, a runtime assurance (RTA) framework for programming safe cyber-physical systems (CPS). In ULGEN, a system is implemented as a collection of asynchronous processes executing RTA modules which are generalizations of the well-known Simplex architecture. An RTA module is composed of a set of safe controllers (SCs), designed to guarantee certain safety specifications, and a set of advanced controllers (ACs), optimized for performance, each defined to run under the specific conditions of the operating environment, and a decision module implementing the switching logic between the controllers. A source of complexity in achieving safe CPS is that these systems often involve concurrently interacting components with different execution semantics. To this end, ULGEN allows for the definition of RTA modules with either event-driven or time-driven execution semantics and encapsulates such components into RTA modules. It further provides primitives for implementing priority-based communication between asynchronous processes, which is a necessary feature for task prioritization mechanisms such as contingency plans and interrupt service routines. The framework also provides formal guarantees on the safe execution of RTA modules based on a formal definition of well-formedness. In ULGEN, a well-formed RTA module combines SCs and ACs in a way that guarantees the underlying safety specifications assured by the SCs while delivering the desired performance offered by the ACs. We compare the safety guarantees of ULGEN against other state-of-the-art RTA frameworks and demonstrate its efficacy in implementing safe and performant CPS by presenting an extensive experimental evaluation of five case studies both in a simulation environment and on a real robotic platform.

Efficient Person Reidentification for IoT-Assisted Cyber–Physical Systems

The main objective of this study is to propose a cyber-physical system (CPS) based person re-identification framework for smart surveillance. The Internet of Things (IoT) based interconnected vision sensors in smart cities are considered essential elements of a CPS, and contribute significantly to urban security. However, the re-identification of targeted persons using emerging edge AI techniques still faces certain challenges. To improve efficiency at the edge and overcome the traditional sensing of video cameras, we employed an AI-based person re-identification framework for CPS that is functional in IoT environments. In addition, we present dual attention dilated network (DADNet), which integrates an energy-efficient convolutional neural network (CNN) with a self-attention module to substantially improve the person matching probability. Further, we applied dual feature fusion to intelligently integrate discriminative and robust features using early and late fusion strategies that allow DADNet to significantly consider the foreground and marginally utilize the background information. Furthermore, we impose diversity orthogonality regularization over several CNN layers, which boosts the performance of DADNet, resulting in an appropriate usage over IoT networks. A comprehensive set of ablation studies, comparison with other state-of-the-art approaches, and a time complexity analysis confirm the strength of our DADNet for re-identification tasks in AI-enabled IoT settings that are well-suited for a CPS.

Detection and Performance Compensation for Linear $\epsilon$-Stealthy Attacks in Cyber-Physical Systems

In this paper, a precisely designed system framework for linear cyber-physical systems (CPSs) is presented to address the detection and performance compensation problems caused by linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -stealthy attacks. In particular, this paper constructs the security modules (data preprocess and data recovery) to assist the detectors (main detector and auxiliary detector) to alert the system anomalies. When no information is leaked, a parameter selection problem is analyzed to help the detector discover the attack. For the cases of model information leakage, we resort to the difference between the main detector and auxiliary detector to reveal the existence of linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -stealthy attacks. Under three different attack scenarios, this paper derives the corresponding attack-compensation mechanisms to ensure that the performance losses of systems are bounded. Finally, a three-tank system is provided to verify the effectiveness of the theoretical results.

A sequence-to-sequence model for joint bridge response forecasting

Knowledge of the structural response of bridges is extremely important for highway asset management and bridge structural health monitoring. Instrumenting every bridge in a road network and maintaining the monitoring instrumentation over decades of service can be financially infeasible. Mechanical intuition suggests a significant relationship exists between responses of two sets of bridges reasonably similar in design exposed to an identical load. This study explores the use of data-driven models to forecast the response of one bridge to a given truck load using the response of another bridge to the same loading profile. By deploying a modern monitoring system in multiple bridges in the same highway corridor integrated in a cyber-physical systems (CPS) framework, and utilizing advanced computer vision algorithms, the authors have gathered a unique dataset consisting of pairs of bridge responses to the same truck load from live traffic moving across a 32.2 km (20 miles) stretch of the I-275 highway in southeast Michigan. Signal processing techniques have been employed to isolate the response of the bridges to a single truck load in a time series of recorded responses. Then, a deep-learning-based time series forecasting framework using the encoder-decoder architecture with gated recurrent unit (GRU) and long short-term memory (LSTM) cells has been used for bridge response forecasting. Baseline models based on linear time series models are also developed to which the deep-learning forecasting models can be compared. After training the models, it is observed that deep-learning-based models can accurately forecast the response of one bridge using the response of another and reduce the forecasting root-mean-squared error (RMSE) by at least 20% relative to baseline linear models. The forecasting capabilities of the encoder-decoder architecture proposed herein outperform traditional approaches to response forecasting. Trained versions of the encoder-decoder forecasting model can be used to provide reliable estimates of bridge response using a single instrumented bridge in a corridor, thereby enhancing the value of data from instrumented bridges for asset management of bridge networks.

An Intelligent Framework for Cyber-Physical Satellite System and IoT-Aided Aerial Vehicle Security Threat Detection.

The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current architecture of small drones necessitates modifications to their data transformation and privacy mechanisms to align with domain requirements. This research paper investigates the latest trends in safety, security, and privacy related to drones, and the Internet of Drones (IoD), highlighting the importance of secure drone networks that are impervious to interceptions and intrusions. To mitigate cyber-security threats, the proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology. Furthermore, in this work, a new dataset is constructed, a merged dataset comprising a drone dataset and two benchmark datasets. The proposed strategy outperforms the previous algorithms and achieves 99.89% accuracy on the drone dataset and 91.64% on the merged dataset. Overall, this intelligent framework gives a potential approach to improving the security and resilience of cyber-physical satellite systems, and IoT-aided aerial vehicle systems, addressing the rising security challenges in an interconnected world.

A Timing-Based Framework for Designing Resilient Cyber-Physical Systems under Safety Constraint

Cyber-physical systems (CPS) are required to satisfy safety constraints in various application domains such as robotics, industrial manufacturing systems, and power systems. Faults and cyber attacks have been shown to cause safety violations, which can damage the system and endanger human lives. Resilient architectures have been proposed to ensure safety of CPS under such faults and attacks via methodologies including redundancy and restarting from safe operating conditions. The existing resilient architectures for CPS utilize different mechanisms to guarantee safety, and currently, there is no common framework to compare them. Moreover, the analysis and design undertaken for CPS employing one architecture is not readily extendable to another. In this article, we propose a timing-based framework for CPS employing various resilient architectures and develop a common methodology for safety analysis and computation of control policies and design parameters. Using the insight that the cyber subsystem operates in one out of a finite number of statuses, we first develop a hybrid system model that captures CPS adopting any of these architectures. Based on the hybrid system, we formulate the problem of joint computation of control policies and associated timing parameters for CPS to satisfy a given safety constraint and derive sufficient conditions for the solution. Utilizing the derived conditions, we provide an algorithm to compute control policies and timing parameters relevant to the employed architecture. We also note that our solution can be applied to a wide class of CPS with polynomial dynamics and also allows incorporation of new architectures. We verify our proposed framework by performing a case study on adaptive cruise control of vehicles.

A framework for AI-based self-adaptive cyber-physical process systems

AbstractDigital transformation is both an opportunity and a challenge. To take advantage of this opportunity for humans and the environment, the transformation process must be understood as a design process that affects almost all areas of life. In this paper, we investigate AI-Based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS) as an extension of the traditional CPS view. As contribution, we present a framework that addresses challenges that arise from recent literature. The aim of the AI-CPPS framework is to enable an adaptive integration of IoT environments with higher-level process-oriented systems. In addition, the framework integrates humans as actors into the system, which is often neglected by recent related approaches. The framework consists of three layers, i.e., processes, semantic modeling, and systems and actors, and we describe for each layer challenges and solution outlines for application. We also address the requirement to enable the integration of new networked devices under the premise of a targeted process that is optimally designed for humans, while profitably integrating AI and IoT. It is expected that AI-CPPS can contribute significantly to increasing sustainability and quality of life and offer solutions to pressing problems such as environmental protection, mobility, or demographic change. Thus, it is all the more important that the systems themselves do not become a driver of resource consumption.

Concurrent Receding Horizon Control and Estimation Against Stealthy Attacks

In this work, we consider a game-theoretic framework for cyber-physical systems, where a defender develops a mitigation strategy against an intelligent attacker who exploits the system&#x0027;s uncertainty to remain undetected. The goal of the defender is to optimize a performance cost constructed specifically to account for robustness against stealthy attacks, so that the system is regulated. Conversely, the goal of the attacker is to disrupt the system&#x0027;s performance by leveraging its significant information advantage against the defender. Both players implement their policies in a moving horizon fashion, according to the principles of receding horizon control. However, because the defender has no access to the full state of the system, it concurrently employs receding horizon estimation to overcome this limitation. A rigorous theoretical analysis shows that such a concurrent policy can guarantee closed-loop boundedness, despite the stealthy attacks and the information disadvantage. Simulations verify and clarify these findings.

Cyber-Physical Optimization-Based Fuzzy Control Strategy for Plug-in Hybrid Electric Buses Using Iterative Modified Particle Swarm Optimization

The fuel economy of plug-in hybrid electric bus (PHEB) is highly dependent on its energy management strategy (EMS). In practice, the fuzzy control (FC) is widely used in EMS due to its real-time performance and robustness. However, the FC with fixed parameters is difficult to obtain the optimal fuel economy under changing traffic conditions. Regarding this, the control parameters of FC need to be optimized, but this scheme needs to overcome the subsequent calculation burden and time consumption. Therefore, the design of a real-time EMS with parameter optimization is a challenging problem. Inspired by this issue, a cyber-physical optimization-based fuzzy EMS is proposed in this paper. Firstly, a cyber-physical system framework is formulated for PHEB to eliminate the conflict between parameter optimization and real-time operation of EMS. Secondly, considering the uncertainty of the vehicle environment, an IT2 FC with optimization parameters is designed for real-time torque allocation. Thirdly, an iterative modified particle swarm optimization (IMPSO) algorithm is proposed to optimize parameters to accurately and quickly converge to the optimal solution. Additionally, the optimization problem with multi-objective that takes battery life into account is introduced. Finally, simulation and hardware in loop test are used to discuss the performance of the proposed EMS. The results reveal that the IMPSO algorithm can improve the optimization effect. Compared to conventional rule-based and fuzzy-based strategies, the proposed EMS can reduce fuel consumption at least 10% and 4.5%, respectively. Meanwhile, it shows the proposed EMS could reduce the battery capacity loss by 6.42%∼9.72% with a slight increase in fuel consumption.

A blockchain-enabled collaborative intrusion detection framework for SDN-assisted cyber-physical systems

A blockchain-enabled collaborative intrusion detection framework for SDN-assisted cyber-physical systems

An Automatic Assembly SMT Production Line Operation Technological Process Simulation Model Development

This article describes an automatic assembly SMT production line operation technological process simulation model development within the framework of modern cyber-physical production systems. In the course of the research, the authors conduct an analysis of modern publications on the topic of research, and the need to find new approaches for modeling the work of a production line at the initial stages of design. This makes it possible to assess the load on each element of the system at the design stage, and identify disadvantages at the early stages of production planning. The authors give an example of the task solution for modeling a fragment of an automated production line of SMT assembly, based on the use of typical mathematical schemes of mass service systems (MSS). A description of the functioning of the technological link of the process is carried out, and a Q-diagram of the technological process of SMT assembly is developed. Using the simulation modeling language GPSS and the technological parameters of the selected equipment, a block diagram was developed, on the basis of which the program was written and the work of the SMT assembly production line was simulated. The obtained results further allow us to estimate the production capacity, the loading of each equipment, the functioning processes of both the selected fragment, and the developed model and they can be applied to the simulation of large production lines within the framework of PCB production.

Architectural framework of digital twin-based cyber-physical production system for resilient rechargeable battery production

AbstractRechargeable battery production should yield highly diversified batteries, overcoming performance degradation caused by the complexity of production processes, dynamic disturbances, and uncertainties. Resilience must be achieved to overcome these limitations while satisfying the core technical requirements. This study developed an architectural framework for a cyber-physical production system (CPPS) using a digital twin (DT) to achieve resilience. Activities for resilience, operational characteristics, and CPPS were analysed to determine the core requirements. This analysis presents a novel model of activities for resilience. Moreover, the DT-based CPPS architecture, service composition procedures, and the asset description for providing inputs to the elements in the CPPS were designed according to these requirements. The proposed architectural framework applies the asset administration shell principles for efficient interoperability. The service composition procedures are classified into the type and instance phases to ensure static and dynamic technical functionalities. Moreover, the asset description is suitable to indicate the required information elements of rechargeable battery production. The DT-based CPPS was applied in a rechargeable battery production for an industrial case study to verify and validate the proposed method. The average accuracy of the DT application was 95.24%, indicating that it can provide technical functions with high accuracy. As a result, these technical functions can be executed within a sufficient action time, and the high simulation accuracy prevents performance degradation during production. Additionally, the DT is suitable for event diagnosis and provides a dynamic response. Furthermore, the proposed method can eliminate the data, analysis, and decision latencies.

A lightweight blockchain-based framework for medical cyber-physical system

A medical cyber-physical system (MCPS) combines intelligent medical devices with a network. Nowadays, MCPS is extensively used in the healthcare system. While sharing patient data into MCPS, data security and privacy are significant challenges in healthcare as most of the devices suffer from data leaking, data manipulation, data forgery, denial of service attacks, etc. In this work, a reputation-based blockchain framework has been proposed to ensure patient data security while preserving privacy. A detailed security analysis discusses the possible attacks and the countermeasures of the attacks. The result and performance analysis of the proposed framework show low latency in data sharing and high throughput with an increasing number of users in the network. The calculated results established that the average latency time is reduced to (46–50%) while throughput is increased by (44–50%) as the number of users increases.

A connective framework to minimize the anxiety of collaborative Cyber-Physical System

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