Industrial Cyber-physical Systems Research Articles

Multi-Agent Modeling of Cyber-Physical Systems for IEC 61499 Based Distributed Automation

The traditional industrial automation systems developed under IEC 61131-3 in centralized control are statically programmed with determined procedures to perform predefined behaviors/tasks in structured environments. A major challenge for the traditional system is the frequent changes and constant uncertainties of the system itself, its operations and the operating environments. Therefore, in this paper we are trying to develop a two-layer architecture for modelling industrial cyber-physical systems, in which the multi-agent computing model is designed for the high-level architecture and the IEC 61499 function block model is applied for the low-level architecture. It aims to integrate system intelligence by communicating and computing cores from the high-level cyber modules and real-time adaptation by distributed and intelligent control of the low-level physical modules. The proposed modeling framework is tested for the feasibility study through preliminary experiments on Jetson Nano and Raspberry Pi by using agent modeling tool SPADE and function block modeling tool Eclipse 4diac.

Real-Time Monitoring and Control of Industrial Cyberphysical Systems: With Integrated Plant-Wide Monitoring and Control Framework

Industrial cyberphysical systems (ICPSs) are the cornerstone research subject in the era of Industry 4.0 [1]. The study of ICPSs has, therefore, become a worldwide research focus [2]-[4]. ICPSs integrate physical entities with cyber networks to build systems that can work more harmoniously, benefiting from integrated design and system-wide optimization [5]. The safety and performance of industrial systems can be improved by developing specific information infrastructure, monitoring, and control approaches aimed at maintaining controllability under external disturbances and unexpected faults [6]. Based on these observations, the design and deployment of ICPSs have both theoretical and practical significance.

Stealthy Attack Against Redundant Controller Architecture of Industrial Cyber-Physical System

In an industrial cyber-physical system (iCPS), the controller plays a critical role in guaranteeing reliability and stability. Therefore, redundant controller architecture is a well-adopted approach by distributed control systems (DCS), supervisory control and data acquisition (SCADA), and other typical iCPSs. They monitor and control the critical industrial process, such as power generation, chemical industry, water treatment plant, etc. Redundant controller architecture has been designed and largely implemented in response to unpredictable mechanical failures. However, this structure initially proposed for guaranteeing reliability and safety may expand the cyber-attack surface, posing the risk that an attacker may take advantage of this architecture for stealthy attacks. In this article, we analyze the vulnerability arising from the redundant controller architecture and propose a combined attack methodology against these redundant controller architecture systems in a stealthy manner. We find several 0-day vulnerabilities of the real-world devices from three manufacturers and further implement the combined attack over these devices. Our experimental results over various types of real-world devices show that the redundant controller architecture can be exploited to compromise all tested systems stealthily. We also present guidelines for mitigating this risk.

Threshold-Free Physical Layer Authentication Based on Machine Learning for Industrial Wireless CPS

Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy.

TrustData: Trustworthy and Secured Data Collection for Event Detection in Industrial Cyber-Physical System

In this article, an industrial cyber-physical system (ICPS) is utilized for monitoring critical events such as structural equipment conditions in industrial environments. Such a system can easily be a point of attraction for the cyberattackers, in addition to system faults, severe resource constraints (e.g., bandwidth and energy), and environmental problems. This makes data collection in the ICPS untrustworthy, even the data are altered after the data forwarding. Without validating this before data aggregation, detection of an event through the aggregation in the ICPS can be difficult. This article introduces TrustData, a scheme for high-quality data collection for event detection in the ICPS, referred to as “Trust worthy and secured Data collection” scheme. It alleviates authentic data for accumulation at groups of sensor devices in the ICPS. Based on the application requirements, a reduced quantity of data is delivered to an upstream node, say, a cluster head. We consider that these data might have sensitive information, which is vulnerable to being altered before/after transmission. The contribution of this article is threefold. First, we provide the concept of TrustData to verify whether or not the acquired data are trustworthy (unaltered) before transmission, and whether or not the transmitted data are secured (data privacy is preserved) before aggregation. Second, we utilize a general measurement model that helps to verify acquired signal untrustworthy before transmitting toward upstream nodes. Finally, we provide an extensive performance analysis through a real-world dataset, and our results prove the effectiveness of TrustData.

Business in the digital economy: russian and foreign experience

The concept of digital economics is entering our life more confidently every day. In the near future, it can completely supplant the material. Many people have become accustomed to using cashless payments and making online purchases. This type of economy is usually considered as a kind of model that is already functioning to a certain extent. Digital economy is such an economy, where an industrial cyber-physical system acts as an industrial complex, a production system creating products and services, ensuring the life and comfort of a person, the population. The aim of the work is to study the digital economy and its role in the development of entrepreneurship on the example of Russian and foreign experience. The article examines is also given the priorities and main tools for the development of the Russian digital economy, for comparison, the experience of foreign countries. The study identified problems that impede the acceleration of these processes, presented the prospects for the development of the digital economy. The problem of the study lies in the lag of the development of the digital economy of Russia from foreign countries. The object of the study is the digital economy in the development of entrepreneurship. The article used such general scientific methods as analysis and synthesis.

M-folding method–based elliptic curve cryptosystem for industrial cyber-physical system

Recently, cyber-physical system is widely used for smart system control in various fields. Various functions of the cyber-physical system must overcome the limited hardware resources constraint of an embedded system. In addition, the data required from the industrial cyber-physical system are critical; therefore, a highly secure encryption technique is required. However, security and computational throughput are incompatible with each other in the cryptographic technique; therefore, the industrial cyber-physical system needs to adopt a highly efficient and secure encryption technique considering the limited available resources. This study applies the m-folding method to the highly secure elliptic curve algorithm to improve efficiency and proposes the cryptosystem optimized for the resource-constrained industrial cyber-physical system. The proposed m-folding method–based elliptic curve encryption showed 50% faster encryption than the existing methods.

A modified neighborhood preserving embedding-based incipient fault detection with applications to small-scale cyber–physical systems

Industrial cyber–physical systems (ICPSs) are backbones of the Industrial 4.0 where control, physical entities, and monitoring are intensively interacted. Aiming to improve safety of a small-scale ICPS whose physical entity is an electrical drive system, this paper will develop a new detection strategy for incipient faults in neighborhood preserving embedding (NPE) framework that can provide stable solutions. The proposed modified NPE can not only extract local information effectively on data manifold of the ICPS but also solve the singularity problem caused by generalized eigenvalue decomposition skills. Additional advantages of this design for ICPSs include the enhanced fault detectability, inherent scalability, and accelerated computation efficiency. The proposed method is firstly evaluated by mathematical deviations and then is evaluated by its application to a small-scale ICPS. Three sets of experimental results show the efficacy of the proposed method in dealing with online detection of incipient faults in the ICPS.

Robust model predictive control with randomly occurred networked packet loss in industrial cyber physical systems

for a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.

A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications

Industrial cyber-physical systems are the primary enabling technology for Industry 4.0, which combine legacy industrial and control engineering, with emerging technology paradigms (e.g. big data, internet-of-things, artificial intelligence, and machine learning), to derive self-aware and self-configuring factories capable of delivering major production innovations. However, the technologies and architectures needed to connect and extend physical factory operations to the cyber world have not been fully resolved. Although cloud computing and service-oriented architectures demonstrate strong adoption, such implementations are commonly produced using information technology perspectives, which can overlook engineering, control and Industry 4.0 design concerns relating to real-time performance, reliability or resilience. Hence, this research compares the latency and reliability performance of cyber-physical interfaces implemented using traditional cloud computing (i.e. centralised), and emerging fog computing (i.e. decentralised) paradigms, to deliver real-time embedded machine learning engineering applications for Industry 4.0. The findings highlight that despite the cloud’s highly scalable processing capacity, the fog’s decentralised, localised and autonomous topology may provide greater consistency, reliability, privacy and security for Industry 4.0 engineering applications, with the difference in observed maximum latency ranging from 67.7%–99.4%. In addition, communication failures rates highlighted differences in both consistency and reliability, with the fog interface successfully responding to 900,000 communication requests (i.e. 0% failure rate), and the cloud interface recording failure rates of 0.11%, 1.42%, and 6.6% under varying levels of stress.

A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems

Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.

Industrial Cyber-Physical System Evolution Detection and Alert Generation

Industrial Cyber-Physical System (ICPS) monitoring is increasingly being used to make decisions that impact the operation of the industry. Industrial manufacturing environments such as production lines are dynamic and evolve over time due to new requirements (new customer needs, conformance to standards, maintenance, etc.) or due to the anomalies detected. When an evolution happens (e.g., new devices are introduced), monitoring systems must be aware of it in order to inform the user and to provide updated and reliable information. In this article, CALENDAR is presented, a software module for a monitoring system that addresses ICPS evolutions. The solution is based on a data metamodel that captures the structure of an ICPS in different timestamps. By comparing the data model in two subsequent timestamps, CALENDAR is able to detect and effectively classify the evolution of ICPSs at runtime to finally generate alerts about the detected evolution. In order to evaluate CALENDAR with different ICPS topologies (e.g., different ICPS sizes), a scalability test was performed considering the information captured from the production lines domain.

A Game-Theoretic Approach to Cross-Layer Security Decision-Making in Industrial Cyber-Physical Systems

Current security measures in industrial cyber-physical systems (ICPS) lack the active decision capability to defend against highly-organized cyber-attacks. In this paper, a security decision-making approach based on stochastic game model is proposed to characterize the interaction between attackers and defenders in ICPSs and generate optimal defense strategies to minimize system losses. The major distinction of this approach is that it presents a practical way to build a cross-layer security game model for ICPSs by means of quantitative vulnerability analysis and time-based unified payoff quantification. A case study on a hardware-in-the-loop simulation testbed is carried out to demonstrate the feasibility of the proposed approach.

Research about DoS Attack against ICPS.

This paper studies denial-of-services (DoS) attacks against industrial cyber-physical systems (ICPSs) for which we built a proper ICPS model and attack model. According to the impact of different attack rates on systems, instead of directly studying the time delay caused by the attacks some security zones are identified, which display how a DoS attack destroys the stable status of the ICPS. Research on security zone division is consistent with the fact that ICPSs’ communication devices actually have some capacity for large network traffic. The research on DoS attacks’ impacts on ICPSs by studying their operation conditions in different security zones is simplified further. Then, a detection method and a mimicry security switch strategy are proposed to defend against malicious DoS attacks and bring the ICPS under attack back to normal. Lastly, practical implementation experiments have been carried out to illustrate the effectiveness and efficiency of the method we propose.

Uncertainty-wise test case generation and minimization for Cyber-Physical Systems

Cyber-Physical Systems (CPSs) typically operate in highly indeterminate environmental conditions, which require the development of testing methods that must explicitly consider uncertainty in test design, test generation, and test optimization. Towards this direction, we propose a set of uncertainty-wise test case generation and test case minimization strategies that rely on test ready models explicitly specifying subjective uncertainty. We propose two test case generation strategies and four test case minimization strategies based on the Uncertainty Theory and multi-objective search. These strategies include a novel methodology for designing and introducing indeterminacy sources in the environment during test execution and a novel set of uncertainty-wise test verdicts. We performed an extensive empirical study to select the best algorithm out of eight commonly used multi-objective search algorithms, for each of the four minimization strategies, with five use cases of two industrial CPS case studies. The minimized set of test cases obtained with the best algorithm for each minimization strategy were executed on the two real CPSs. The results showed that our best test strategy managed to observe 51% more uncertainties due to unknown indeterminate behaviors of the physical environments of the CPSs as compared to the other test strategies. Also, the same test strategy managed to observe 118% more unknown uncertainties as compared to the unique number of known uncertainties.

Toward Industry 4.0 Components: Insights Into and Implementation of Asset Administration Shells

Diverse customer demands are increasing the need for improved flexibility, adaptability, and transparency of production processes; traditional manufacturing systems must change. To meet these requirements, cyberphysical system (CPS) technologies are being increasingly applied in industrial manufacturing, equipping production facilities with autonomous adaptation capabilities. This is achieved by incorporating physical objects and their virtual representations and enabling bidirectional information exchange between them. The Industry 4.0 (I4.0) Component proposed by Germany's 14.0 initiative is an emerging CPS category. The concept of an administration shell or an asset administration shell (AAS) is a virtual digital representation of the 14.0 asset that plays a pivotal role in establishing communication among 14.0 Components. To date, the granularity of information modeled in an AAS remains undefined. Therefore, this article focuses on not only the general form but also the fine-grained details of the AAS. The goal is to present a high-level introduction to this topic, substantiated by an implementation example in a manufacturing system; to provide different 14.0 stakeholders (e.g., manufacturers, integrators, and operators) with best-practice guidelines for carrying out their own AASrelated activities; and to strengthen the interaction between academic research and industrial practice to accelerate the digitalization of industrial production through deploying AASs. We live in the time of the fourth industrial revolution, which is associated with rapid changes in production operations. The CPS approach decentralizes production, rendering production systems autonomous, flexible, and adaptive. The 14.0 Component philosophy links physical assets to their virtual representations, facilitating information interaction. This article provides meaningful insights into an implementation of the AAS within a real manufacturing use case. This implies that an industrial CPS system that contains several 14.0 Components can be developed successfully.

Critic-only adaptive dynamic programming algorithms’ applications to the secure control of cyber–physical systems

Industrial cyber–physical systems generally suffer from the malicious attacks and unmatched perturbation, and thus the security issue is always the core research topic in the related fields. This paper proposes a novel intelligent secure control scheme, which integrates optimal control theory, zero-sum game theory, reinforcement learning and neural networks. First, the secure control problem of the compromised system is converted into the zero-sum game issue of the nominal auxiliary system, and then both policy-iteration-based and value-iteration-based adaptive dynamic programming methods are introduced to solve the Hamilton–Jacobi–Isaacs equations. The proposed secure control scheme can mitigate the effects of actuator attacks and unmatched perturbation, and stabilize the compromised cyber–physical systems by tuning the system performance parameters, which is proved through the Lyapunov stability theory. Finally, the proposed approach is applied to the Quanser helicopter to verify the effectiveness.

Security of controlled manufacturing systems in the connected factory: the case of industrial robots

In modern factories, “controlled” manufacturing systems, such as industrial robots, CNC machines, or 3D printers, are often connected in a control network, together with a plethora of heterogeneous control devices. Despite the obvious advantages in terms of production and ease of maintenance, this trend raises non-trivial cybersecurity concerns. Often, the devices employed are not designed for an interconnected world, but cannot be promptly replaced: In fact, they have essentially become legacy systems, embodying design patterns where components and networks are accounted as trusted elements. In this paper, we take a holistic view of the security issues (and challenges) that arise in designing and securely deploying controlled manufacturing systems, using industrial robots as a case study—indeed, robots are the most representative instance of a complex automatically controlled industrial device. Following up to our previous experimental analysis, we take a broad look at the deployment of industrial robots in a typical factory network and at the security challenges that arise from the interaction between operators and machines; then, we propose actionable points to secure industrial cyber-physical systems, and we discuss the limitations of the current standards in industrial robotics to account for active attackers.

Using a semi-automated job-shop production system model to prepare students for the challenges of Industrial Cyber-Physical Systems

Abstract Though the engineering methods, tools, and standards for Industrial Cyber-Physical Systems (ICPS) have become mature and get more defined, the challenges to apply them to develop production systems towards ICPS remain difficult to grasp for experts and practitioners and even more for students. “Digitalization and Virtualization of ICPS” (DVoICPS) is a subject relevant to different student groups in informatics, electronics, mechanical engineering, and others. To teach the idea behind DVoICPS and to sensitize students to the different challenges of the cyber and of the physical world, a semi-automated job-shop production system (model) was adopted. Three types of products were manufactured with different operations. The student tasks included evolving the legacy system toward an ICPS system with improved production performance indicators. The improvements had to be feasible for the physical model, consisting of fischertechnik© elements. Students also had to describe modules of the system as ICPS components and build an executable virtual model of the evolved system, simulating it. Additionally, the students needed to plan and describe aspects for digitalizing the system itself. This paper describes the learning concept of the teaching module, the challenges of teaching students with different disciplinary backgrounds, and the students’ deliverables and learning effects. The paper concludes with summary and proposed changes to the teaching concepts for the next round of teaching.

State Estimation Oriented Wireless Transmission for Ubiquitous Monitoring in Industrial Cyber-Physical Systems

State estimation over wireless sensor networks (WSNs) plays an important role for the ubiquitous monitoring in industrial cyber-physical systems (ICPSs). However, the unreliable wireless channels lead to the transmitted measurements arriving at the remote estimator intermittently, which will deteriorate the estimation performance. Question of how to improve the transmission reliability in the hostile industrial environment to guarantee the pre-defined estimation performance for ICPSs is largely unexplored. This paper is concerned with a redundant transmission strategy to meet the reliability requirement for state estimation. This strategy incorporates the ISM channels with the opportunistically harvested channels to provide adequate spectrum opportunities for redundant transmissions. First, we explore the relationship between the estimation performance and the transmission reliability, based on which a joint optimization of channel allocation and power control is then developed to guarantee the estimation performance and maximize the sum rate of the WSN. Second, we formulate the optimization into a mix-integer nonlinear programming problem, which is solved efficiently by decomposing it into the channel allocation and power control subproblems. Ultimately, simulation study demonstrates that the proposed strategy not only ensures the required state estimation performance, but also increases the sum rate of the WSN.