Industrial Cyber-physical Systems Research Articles

A resilient hierarchical distributed model of a cyber physical system

ABSTRACT We present a novel modelling scheme to develop a resilient hierarchical distributed model of industrial cyber-physical systems amenable to feedback control. It advances the current research work on cyber-physical system security and resilience by integrating security and self-adaptation features into each of the self-contained units (subsystems) of a cyber-physical system. We compare the proposed modelling scheme with the extant schemes. The proposed scheme is more comprehensive than the extant research work on resilient modelling scheme. The modelling scheme can be used to design resilient cyber-physical systems and build models to assess their security and resilience.

Passivity-Based Robust Control Against Quantified False Data Injection Attacks in Cyber-Physical Systems

Secure control against cyber attacks becomes increasingly significant in cyber-physical systems (CPSs). False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control signals. For quantified false data injection attacks, this paper establishes an effective defense framework from the energy conversion perspective. Then, we design an energy controller to dynamically adjust the system energy changes caused by unknown attacks. The designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping injection. Moreover, with the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> disturbance attenuation technique, the burden of control system design is simplified because there is no need to design an attack observer. In addition, this secure control method is simple to implement because it avoids complicated mathematical operations. The effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.

Integrated security control method for industrial cyber-physical system with attack and fault

Integrated security control method for industrial cyber-physical system with attack and fault

Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems

IEC 61499 is a reference architecture for constructing Industrial Cyber-Physical Systems (ICPS). However, current function block development environments only provide limited fault-finding capabilities. There is a need for comprehensive diagnostic tools that help engineers identify faults, both during development and after deployment. This article presents the software architecture for an agent-based fault diagnostic engine that equips agents with domain-knowledge of IEC 61499. The engine encourages a Model-Driven Development with Diagnostics methodology where agents work alongside engineers during iterative cycles of design, development, diagnosis and refinement. Attribute-Driven Design (ADD) was used to propose the architecture to capture fault telemetry directly from the ICPS. A Views and Beyond Software Architecture Document presents the architecture. The Architecturally-Significant Requirement (ASRs) were used to design the views while an Architectural Trade-off Analysis Method (ATAM) evaluated critical parts of the architecture. The agents locate faults during both early-stage development and later provide long-term fault management. The architecture introduces dynamic, low-latency software-in-loop Diagnostic Points (DPs) that operate under the control of an agent to capture fault telemetry. Using sound architectural design approaches and documentation methods, coupled with rigorous evaluation and prototyping, the article demonstrates how quality attributes, risks and architectural trade-offs were identified and mitigated early before the construction of the engine commenced.

An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions.

The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.

Semantic Microservice Framework for Digital Twins

Digital Twins (DT) in industrial cyber-physical systems are the key enabling technology for Industry 4.0. Services are an essential part of almost every DT concept, but their interaction is usually implementation-specific since no common guidelines are available. This work identifies some fundamental requirements for a DT service framework based on applications identified in corresponding literature. Based on these requirements, a service framework architecture is proposed. The architecture utilizes Semantic Web technology and a workflow engine for service orchestration to support the fulfilment of the identified requirements. As a case study for sensor data evaluation of an industrial process, a proof-of-concept implementation is presented, showing the feasibility and suitability of the proposed DT service framework architecture.

Design, Analysis, and Experimental Evaluation of a New Secure Rejoin Mechanism for LoRaWAN Using Elliptic-Curve Cryptography

LoRaWAN (Long Range Wide Area Network) is a Low-Power Wide Area Networks (LPWAN) technology with very rapid uptake during the previous years, developed by the LoRa (Long Range) Alliance as an open standard operating over the unlicensed band. Current LoRaWAN architecture foresees specific techniques for bootstrapping end-to-end encryption during network initialization. In particular, this work focuses on the Over-The-Air Activation (OTAA) method, which uses two keys (Network key (NwkKey) and Application key (AppKey)) that are hard-coded into the device and do not change throughout the entire lifetime of the deployment. The inability to refresh these two keys is as a weak point in terms of the overall security of the network especially when considering deployments that are expected to operate for at least 10–15 years. In this paper, the security issues of OTAA are presented in detail highlighting the vulnerabilities against the specific type of attacks. A new scheme for network activation is proposed that builds upon the current LoRaWAN architecture in a way that maintains backwards compatibility while resolving certain vulnerabilities. Under the new mechanism, the devices periodically negotiate new keys securely based on elliptic-curve cryptography. The security properties of the proposed mechanism are analyzed against a specific type of attacks. The analysis indicates that the new secure rejoin mechanism guarantees (i) computational key secrecy, (ii) decisional key secrecy, and (iii) key independence, forward and backward, for both root keys thus properly addressing the considered security vulnerabilities of LoRaWAN. Moreover, the method is implemented in software using the RIOT-OS, a hardware-independent operating system that supports many different architectures for 8 bit, 16 bit, 32 bit and 64 bit processors. The resulting software is evaluated on the FIT IoT-Lab real-world experimentation facility under a diverse set of ARM Cortex-M* devices targeting a broad range of IoT applications, ranging from advanced wearable devices to interactive entertainment devices, home automation and industrial cyber-physical systems. The experiments indicate that the overall overhead incurred in terms of energy and time by the proposed rejoin mechanism is acceptable given the low frequency of execution and the improvements to the overall security of the LoRaWAN1.1 OTAA method.

Digital twins: artificial intelligence and the IoT cyber-physical systems in Industry 4.0

This paper presents a summary of mechanisms for the evolution of artificial intelligence in ‘internet of things’ networks. Firstly, the paper investigates how the use of new technologies in industrial systems improves organisational resilience supporting both a technical and human level. Secondly, the paper reports empirical results that correlate academic literature with Industry 4.0 interdependencies between edge components to both external and internal services and systems. The novelty of the paper is a new approach for creating a virtual representation operating as a real-time digital counterpart of a physical object or process (i.e., digital twin) outlined in a conceptual diagram. The methodology applied in this paper resembled a grounded theory analysis of complex interconnected and coupled systems. By connecting the human–computer interactions in different information knowledge management systems, this paper presents a summary of mechanisms for the evolution of artificial intelligence in internet of things networks.

Hybrid Statistical-Machine Learning for Real-Time Anomaly Detection in Industrial Cyber–Physical Systems

Critical industrial infrastructures are currently facing increasing cyberspace threats in their underlying information and communication systems. The advanced monitoring, control, and management functionalities of the industrial systems firmly rely on the reliable and secure operations of the industrial control system (ICS) network. This article characterizes the ICS network traffic and presents a scalable and efficient solution for real-time ICS network traffic anomaly detection, considering various forms of ICS anomaly events. The events due to the cyberattacks, malicious operating behaviors, and network anomalies can be effectively detected without sophisticated computational requirements and retrieval of communication protocols. The proposed hybrid statistical-machine learning model integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the abnormal traffic patterns with low false omission rates. The proposed solution is extensively evaluated at a realistic ICS cyber–physical system (CPS) testbed, and the numerical results confirm its high detection accuracy and low computational complexity. Note to Practitioners—This article was motivated by the challenge of real-time anomaly detection in industrial cyber–physical systems (CPSs). The existing industrial control system (ICS) network anomaly detection solutions are generally carried out based on a single model based on the historian database and cannot dynamically classify the abnormal conditions in a real-time fashion. A novel hybrid statistical-machine learning model is developed that integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the anomalous events through traffic pattern analysis. The proposed anomaly detection solution can efficiently provide accurate detection for cyberattacks, malicious operating behaviors, and network anomalies while meeting the real-time requirements of ICS networks. The proposed solution can be deployed in the realistic ICS CPSs, e.g., the power generation system, gas pipeline systems, and urban railway transportation systems. The preliminary numerical results obtained from the ICS-CPS testbed suggested that it can provide high detection accuracy with low computational complexity and, hence, can be adopted with minimal deployment hurdles.

Guest Editorial: Softwarized Networking for Next Generation Industrial Cyber-Physical Systems

The papers in this special section focus on softwarized networking for next generation industrial cyber-physical systems (CPSs). With the emergence of embedded and ubiquitous cyberphysical applications, the rationale of blending the physical and the virtual worlds has become ever promising. These papers examine several topics that are recently concerned in the community, including the software defined architectures and implementations, advanced machine learning and data analytics solutions, blockchain-based network services and applications, network function allocation, dependable and trustable solutions, energy efficient networks and services, and other enabling technologies for integrating softwarized networks into CPSs.

A Security Scheme Based on Intranal-Adding Links for Integrated Industrial Cyber-Physical Systems.

With the advent of the Internet of Everything era, the Industrial Internet is increasingly showing mutual integration and development. Its core framework, the industrial CPS (Cyber-Physical Systems), has received more and more attention and in-depth research in recent years. These complex industrial CPS systems are usually composed of multiple interdependent sub-networks (such as physical networks and control networks, etc.). Minor faults or failure behaviors between sub-networks may cause serious cascading failure effects of the entire system. In this paper, we will propose a security scheme based on intranal-adding links in the face of the integrated and converged industrial CPS system environment. Firstly, by calculating the size of the largest connected component in the entire system, we can compare and analyze industrial CPS systems’ security performance under random attacks. Secondly, we compare and analyze the risk of cascading failure between integrated industrial CPS systems under different intranal-adding link strategies. Finally, the simulation results verify the system security strategy’s effectiveness under different strategies and show a relatively better exchange strategy to enhance the system’s security. In addition, this paper’s research work can help us design how to further optimize the interdependent industrial CPS system’s topology to cope with the integrated and converged industrial CPS system environment.

Open Source Languages and Methods for Cyber-Physical System Development: Overview and Case Studies

Industrial cyber-physical system products interleave hardware, software, and communication components. System complexity is increasing simultaneously with increased demands on quality and shortened time-to-market. To effectively support the development of such systems, we present languages and tools for comprehensive integrated model-based development that cover major phases such as requirement analysis, design, implementation, and maintenance. The model-based approach raises the level of abstraction and allows to perform virtual prototyping by simulating and optimizing system models before building physical products. Moreover, open standards and open source implementations enable model portability, tool reuse and a broader deployment. In this paper we present a general overview of the available solutions with focus on Modelica/OpenModelica, Bloqqi, and Acumen. The paper presents contributions to these languages and environments, including symbolic-numeric modeling, requirement verification, code generation, model debugging, design optimization, graphical modeling, and variant handling with traceability, as well a general discussion and conclusions.

Dynamic Edge and Cloud Service Integration for Industrial IoT and Production Monitoring Applications of Industrial Cyber-Physical Systems

Industrial cyber-physical systems rely increasingly on data from Internet-of-Things (IoT) devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This article studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead framework model, is verified through two representative industrial use cases.

Industrial Cyber Physical System (ICPS) for the Control and Monitoring of Machines

The Industrial Cyber Physical System (ICPS) for industrial monitoring applying wireless technology i.e. Wireless Sensor Networks (WSNs). WSNs are a critical concern for engineering firms. There are various techniques incorporated in wireless networks for industrial application and machining in the modern age. In this research contribution, security protocols have been considered through the incorporation of novel design techniques for wireless industrial controlling and monitoring systems and eco-friendly machines. The wireless communication approaches are restricted to modest application due to their slow communication velocity, information security and communication distance. Moreover, they are influence by bad weather and noise conditions e.g. rain, fog and snow. This paper presents a proposal of a system for controls and monitoring of applications in engineering firms using Internet of Everything (IoE) and GPRS enhanced communication, high security measures and low costs without requiring infrastructural modification in the hardware of GSM operating segments.

Security preservation in industrial medical CPS using Chebyshev map: An AI approach

Cyber–Physical System (CPS) are widely used in various areas such as industrial manufacturing, telemedicine and energy, etc. Specifically, industrial medical CPS utilizes various medical devices connected to the network to realize real-time management and treatment of patient. However, the underlying wireless channel makes industrial medical CPS vulnerable to external attacks, which may leak patient privacy and even threaten patients’ lives. Besides, most of the authentication schemes are not suitable for real-time applications such as medical CPS due to the latency. Hence, the artificial intelligence (AI) techniques can be useful in this environment to cater to the aforementioned challenges. It necessitates constructing a secure and lightweight authentication protocol to prevent the unauthorized access and control of medical devices. Traditional authentication schemes adopt a password-based or smartcard to validate the authenticity of users, which may suffer from password guessing or smartcard lost attack. However, the AI-assisted biometrics technique can effectively resist the aforementioned attacks. Therefore, an AI-assisted lightweight authentication protocol for real-time access using a Chebyshev map in industrial medical CPS is designed in this paper, which is robust and resolve security and privacy issues. The proposed protocol is capable of resisting various known attacks through security analysis. The proposed protocol has less computational overhead with a slight increase in communication overhead. Also, the proposed protocol supports stringent security features and functionalities compared to other existing protocols.

WITHDRAWN: Enhancing the QOS of far field networking and communication using the optical properties of graphene

The Optical fibers with data links are the backbone of optical communications in the internet world. Generally, Indium tin oxide (ITO) contains high optical conducting material which has good electrical conductivity property. This property plays a vibrant role in the photo-detecting material to increase the speed in optical communication. The absorption of ITO is not much considerable under certain wavelengths. The unique properties of graphene such as gapless, a direct, and tunable band-gap make the various light detection. Nowadays, surveillance in industrial cyber-physical systems needs to enhance optical communication between networking. In this work, we coated graphene with ITO using spin and dip coating techniques that increase the light absorption result in increasing optical communication at high data rate. The main goal of the proposed work is to investigate the optical properties of coated graphene with ITO to improve communication in the defense field. The fabricated graphene with ITO particle composite properties are analyzed using an X-Ray Diffractometer (XRD), scanning electron microscopy (SEM), and Ultraviolet–visible spectroscopy in terms of light absorption, electrical conductivity, and transmittance. The experimental results significantly improve the optical properties in order to increase the high data rate in optical and network devices. Light absorption and transmittance are carried out to evaluate the optical properties of the graphene with ITO. The experimental result clearly shows that the Graphene with ITO based optical communication enhances the improvement in Quality of Service (QoS) parameters.

A systematic mapping of semi-formal and formal methods in requirements engineering of industrial Cyber-Physical systems

The requirements engineering of Industrial Cyber-Physical Systems is extremely challenging due to large system sizes, component heterogeneity, involvement of multi-discipline stakeholders and machines, and continuous evolution. Formal and semi-formal languages, techniques, tools and frameworks can assist by providing repeatable and rigorous structures for eliciting, specifying, analysing, verifying and maintaining requirements. Various approaches have been proposed, but a contemporary and comprehensive study providing a landscape of the state-of-the-art is currently missing. This article reports a systematic mapping study covering 93 primary studies published between 2009 and October 2020. We categorise surveyed studies by current research directions in the use of semi-formal and formal methods for Requirements Engineering phases for Industrial Cyber-Physical Systems. We also identify gaps in current research and develop a novel conceptual model capturing the relationship between available formalisms and Requirements Engineering activities. We find that extensive work has been carried out on the formal analysis and verification of safety and timings requirements. However, the use of semi-formal notations, works on key phases like requirements elicitation and management, and the adoption of industrial standards are largely missing. Moreover, we find no literature providing methods to handle privacy and trust requirements, which have become critical concerns in this area.

RESEARCH OF DIKW AND 5C ARCHITECTURAL MODELS FOR CREATION OF CYBER-PHYSICAL PRODUCTION SYSTEMS WITHIN THE CONCEPT OF INDUSTRY 4.0

The development of cyber-physical production systems is a complex scientific and technical task, therefore the developer needs to determine the requirements, tasks for the system being developed and choose an architectural model for its implementation. In turn, the choice of an architectural model assumes a balance for the set of requirements of persons interested in its development. In a typical case, the development of a specific cyber-physical industrial systems needs to be adapted to the means of implementation, to the realities of its future use, maintenance and evolution. Subject matter of this study are architectural models for building complex cyber-physical production systems. Goal of this article is a study of architectural models DIKW and 5C, according to the results of the decomposition of which, in the future, it will be possible to carry out a mathematical description of elementary problems of each level and their physical or simulation modeling. To achieve this goal, it is necessary to solve the following tasks: analyze the DIKW model; analyze the architectural model 5C; compare the DIKW model and the 5C architectural model, using its structural decomposition into levels, information and command channels with feedback within each structure. The research carried out is based on the methods of decomposition and formalized representation of systems. Conclusions: Based on the results of the decomposition at each structural level of the DIKW and 5C models, a decomposition structure was developed, which shows the main differences and general similarities of the models. It was revealed that the 5C model, as a common software shell that combines integrated sensors and actuators, is more suitable for solving problems of developing a cyber-physical production system, and the DIKW interpretation model is more suitable for solving problems of modifying existing systems at enterprises, and the choice of the model itself the development of a cyber-physical production system depends on the requirements of the customer, existing equipment, the level of its automation and the level of project financing.

Temperature-Constrained Reliability Optimization of Industrial Cyber-Physical Systems Using Machine Learning and Feedback Control

As the backbone of Industry 4.0, industrial cyber-physical systems (ICPSs) that are geographically dispersed, federated, cooperative, and security-critical systems become the center of interest from both industry and academia. In ICPS, there are huge amounts of devices, such as sensors and actuators, which are embedded and networked together to improve the performance of real-time monitoring and control. Reliability and temperature are two important concerns of these embedded and networked devices in ICPS due to their stringent requirement of reliable execution and long lifespan. In this article, we study the problem of maximizing soft-error reliability of CPU- and GPU-integrated embedded platforms deployed in ICPS under the temperature constraint. To speed up the estimation of soft-error rate (SER) and temperature, we train an artificial neural network (ANN) that is able to quickly and accurately derive the system’s SER and temperature. To solve the temperature-constrained reliability optimization problem, we propose a feedback control-based task scheduling scheme that adaptively determines the number of tasks admitted in the system and the number of replicas for the admitted tasks. We perform a series of simulation experiments to verify the efficacy of our scheme. The experimental results demonstrate that: 1) the estimated SER and temperature derived by our ANN-based method are very close to the ground-truth data and 2) our proposed feedback control-based task scheduling method can improve system reliability by up to 184.2% with a lower peak temperature when compared with one baseline and two state-of-the-art methods. Note to Practitioners—This article is motivated by the safety-critical industrial cyber-physical system (ICPS) applications necessitating reliable execution and long lifespan, which could be realized by increasing reliability and controlling operating temperature. Our goal is to improve the system reliability of CPU- and GPU-integrated multiprocessor systems-on-chip (MPSoCs) deployed in ICPS under the temperature constraint. Most of the existing papers target either reliability or temperature. A few recent papers have focused on reliability and temperature optimization simultaneously. However, they are not designed for ICPS and do not consider the widely accepted CPU- and GPU-integrated MPSoC platforms. This article proposes a machine learning-based approach that trains an artificial neural network (ANN) to facilitate the online estimation of system SER and temperature. Compared to the offline estimation using simulation tools, the online approach is more applicable to real-time ICPS applications. This article also designs a feedback control-based approach for improving system reliability and reducing peak temperature of the CPU- and GPU-integrated MPSoCs by determining the number of tasks to be admitted and the number of replicas for tasks.

Developing Industrial CPS: A Multi-Disciplinary Challenge.

Industrial Cyber–Physical System (CPS) is an emerging approach towards value creation in modern industrial production. The development and implementation of industrial CPS in real-life production are rewarding yet challenging. This paper aims to present a concept to develop, commercialize, operate, and maintain industrial CPS which can motivate the advance of the research and the industrial practice of industrial CPS in the future. We start with defining our understanding of an industrial CPS, specifying the components and key technological aspects of the industrial CPS, as well as explaining the alignment with existing work such as Industrie 4.0 concepts, followed by several use cases of industrial CPS in practice. The roles of each component and key technological aspect are described and the differences between traditional industrial systems and industrial CPS are elaborated. The multidisciplinary nature of industrial CPS leads to challenges when developing such systems, and we present a detailed description of several major sub-challenges that are key to the long-term sustainability of industrial CPS design. Since the research of industrial CPS is still emerging, we also discuss existing approaches and novel solutions to overcome these sub-challenges. These insights will help researchers and industrial practitioners to develop and commercialize industrial CPS.