Remote training platform for industrial control automation

This paper discusses the new auspicious trends of internet of things (IoT) and its advanced developments in various sectors of today’s IT arena, including for industrial sectors. Each object acts as a communication node (or entity) that is able to communicate with other nodes, and corresponding information could be accessed and controlled, via several of today’s commonly used electronic devices such as laptops and cellular devices, etc., through an IoT-designed platform. Moreover, in industrial automation sectors, the employment of IoT is very advantageous, in terms of remote communication support; low-cost operations and maintenance; and autonomous collaborations among remote networked field devices, etc. The facility of autonomous collaborations provisioned by IoT, in the context of a supervisory control and data acquisition (SCADA) system as part of an industrial control system, somehow exists in a distributed network protocol (DNP3) during automation and controls. Therefore, the current study takes the step to model a new IoT framework for a SCADA system, which will be efficient at facilitating industrial automation through the collaborative DNP3-Modbus acquisitions and automation, called the SCADA-IoT system. An IoT gateway is employed and configured that supports for both SCADA protocols, such as DNP3 and Modbus and is efficient at communicating, from networked field devices, with inter-processing from both. In the overall SCADA-IoT design, the transmission is carried from an enormous amount of sensors and/or field devices, employing proprietary and nonproprietary protocols; further, sent information is analyzed via big data, stored in a cloud center, monitored and controlled over a SCADA-IoT supportive platform. At the same time, information security (IS) is a big associated challenge and one of the main contributions of this study. Thus, this study also analyzed the potential security mechanisms for securing SCADA-IoT and found cryptography to be a noteworthy security solution, based on the proposed system requirements and its communication demands.

This thesis dissertation mainly compares and investigates laboratory study of different implementation methodologies of applied control systems and how they can be adopted in industrial, as well as commercial, automation applications. Namely the research paper aims to assess or evaluate eventual feedback control loops' performance and robustness over multiple conventional or state-of-the-art technologies in the field of applied industrial automation and instrumentation by implementing a laboratory case study setup: the ball on beam system. Hence, the paper tries to close the gap between industry and academia by: first, conducting a historical study and background information of main evolutional and technological eras in the field of industrial process control automation and instrumentation. Then, some related basic theoretical as well as practical concepts are reviewed in Chapter 2 of the report before displaying the detailed design. After that, the next Chapter, analyses the ball on beam control system problem as the case studied in the context of this research through reviewing previous literature, modeling and simulation. The following Chapter details the proposed design and implementation of the ball on beam case study as if it is under the introduced distributed industrial automation architecture. Finally, Chapter 5 concludes this work by listing several points leaned, remarks, and observations, and stating possible development and the future vision of this research.

In an industrial automation environment, there are many sensors generating large volumes of data. Both time-critical data for real-time control as well as less time-critical data for central monitoring and log data need to co-exist in the system with often harsh wireless environments. Thus, available capacity may be low, causing congestion problems and long latencies for by traffic with less stringent timing requirements. In this paper, we consider the feasibility of using Information Centric Networking in industrial automation networks in order to improve efficiency in distribution of data and reduce latency.

Development of Advanced Fermentor Control Applications for Use in an Industrial Automation Environment

Current wireless technologies for industrial applications, such as WirelessHART and ISA100.11a, use a centralized management approach in which a central network manager handles the requirements of the static network. However, such a centralized approach has several drawbacks. For example, it cannot cope with dynamicity/disturbance in large-scale networks in a real-time manner while it also incurs a high communication overhead and latency for exchanging management traffic. In this thesis, we address the drawbacks of the centralized management approach utilized in WirelessHART and ISA100.11a for real-time industrial monitoring and control applications. More specifically, we propose new decentralized network management schemes to provide an end-to-end reliable and real-time communication for battery-powered and harvested-powered devices in a distributed manner. These schemes enable the network devices to join the network, schedule their communications, establish end-to-end connections by reserving communication resources to address real-time requirements, and cope with network dynamicity (e.g., node/edge failures) in a distributed manner. To evaluate wireless protocols in the domain of industrial monitoring and control, a reference point is needed. To that end, we developed a WirelessHART simulator in NS-2 as a reference point to evaluate other protocols. We validated the WirelessHART simulator with a WirelesHART deployment at an industrial plant. To the best of our knowledge, this is the first implementation that supports the WirelessHART network manager as well as the whole stack of the WirelessHART standard. To address the requirements of battery-powered I/O devices, we propose a distributed management scheme to address real-time and reliable communication requirements. This scheme considers the full mesh topology in which I/O devices are capable of participating in routing and distributed network management tasks, such as communication resources scheduling. We then propose a second distributed management scheme for hybrid networks to be used for real-time industrial wireless automation. This scheme addresses the requirements of energy constrained I/O devices. In this scheme, the I/O devices cannot participate in routing and distributed management tasks. The routers can dynamically reserve communication resources and manage the I/O devices in the local star sub-networks. We demonstrate that the proposed scheme achieves higher network management efficiency compared to the ISA100.11a standard, without compromising the latency and reliability requirements of industrial wireless networks. To better support and address the requirements of energy harvested I/O devices, we extend ISA100.11a. The proposed extension makes management more decentralized by delegating a part of the management responsibility to the routers in the network. It also allows the I/O devices to choose the best routers according to different metrics using local statistics and advertised routers’ ranks.

Presently, the field of industrial process control and automation is facing significant challenges, with 40% of controllers operating in manual mode and poor tuning effects present in over 35% of them. Additionally, approximately 30% of controllers experience oscillating effects, while 35% of actuators are plagued by mechanical issues due to inadequate preventive maintenance. The full potential of industrial process control and automation can now be achieved by integrating the Internet of Things (IIoT) and advanced intelligent controllers. The advancements in computational power have opened the door for a radical transformation of how we manage industrial processes. Our research paper delves into the crucial role that IIoT technology plays in addressing the persistent challenges faced in industrial process control. These challenges include poor tuning, oscillating effects, and mechanical issues from a lack of maintenance. By integrating the Kepware OPC server and MATLAB toolboxes, our innovative IIoT platform combines the power of visual basic programming with sophisticated algorithms to model the process, identify optimal parameters, and monitor the system in real time. The outcome of this innovative approach is nothing short of extraordinary. Our platform delivers a simulated or experimental process that is connected, optimized, and completely controlled. The result is a more accurate and efficient process, with improved performance and reduced downtime. The use of advanced intelligent controllers and IIoT technology can revolutionize the field of industrial process control and automation, unlocking new possibilities and delivering unmatched performance.

Industrial automation and control systems (IACS) are tremendously employing supervisory control and data acquisition (SCADA) network. However, their integration into IACS is vulnerable to various cyber-attacks. In this article, we first present population extremal optimization (PEO)-based deep belief network detection method (PEO-DBN) to detect the cyber-attacks of SCADA-based IACS. In PEO-DBN method, PEO algorithm is employed to determine the DBN's parameters, including number of hidden units and the size of mini-batch and learning rate, as there is no clear knowledge to set these parameters. Then, to enhance the performance of single method for cyber-attacks detection, the ensemble learning scheme is introduced for aggregation of the proposed PEO-DBN method, called EnPEO-DBN. The proposed detection methods are evaluated on gas pipeline system dataset and water storage tank system dataset from SCADA network traffic by comparing with some existing methods. Through performance analysis, simulation results show the superiority of PEO-DBN and EnPEO-DBN.

This book presents cutting-edge emerging technologies and approaches in the areas of service-oriented architectures, intelligent devices and cloud-based cyber-physical systems. It provides a clear view on their applicability to the management and automation of manufacturing and process industries. It offers a holistic view of future industrial cyber-physical systems and their industrial usage and also depicts technologies and architectures as well as a migration approach and engineering tools based on these. By providing a careful balance between the theory and the practical aspects, this book has been authored by several experts from academia and industry, thereby offering a valuable understanding of the vision, the domain, the processes and the results of the research. It has several illustrations and tables to clearly exemplify the concepts and results examined in the text and these are supported by four real-life case-studies. We are witnessing rapid advances in the industrial automation, mainly driven by business needs towards agility and supported by new disruptive advances both on the software and hardware side, as well as the cross-fertilization of concepts and the amalgamation of information and communication technology-driven approaches in traditional industrial automation and control systems. This book is intended for technology managers, application designers, solution developers, engineers working in industry, as well as researchers, undergraduate and graduate students of industrial automation, industrial informatics and production engineering.

The deployment of fifth-generation (5G) and beyond 5G (B5G) networks is the ambitious objective of modern research on future mobile networks that are evolving to support computation-intensive and communication-sensitive applications. Such applications (e.g., autonomous vehicles, industrial automation, and remote surgery) impose diverse quality-of-service (QoS) requirements on the network in terms of processing, latency, reliability, and bandwidth, and will require ultra-reliable low-latency communication (URLLC), paving the way for multi-access edge computing (MEC). Our work considers a dynamic indoor B5G network in a robotic scenario where agents continuously need MEC services and migrate from one cell to another to perform their tasks in an ultra-dense cell environment. Assuming that every MEC service is a virtual machine (VM) to execute in one of the cells with the possibility of migrating the VM to another cell by paying some cost, we formalize the joint problem of (1) placing/migrating the VMs to respect their end-to-end communication latency requirements and (2) allocating their computation and communication bandwidth as a mixed-integer linear program (MILP). An MILP solver is then used to find the optimal VM placements/migrations and bandwidth allocations over a time horizon.Keywords5GMECMILPURLLCReal-time

Over the years, industrial automation has repeatedly been impacted by the continuous and tremendous evolution in the fields of computer systems, electronics and data transmission. Industry 4.0, the current wave of the industrial revolution– dubbed the data revolution -, integrates these advances in communication and computational systems with the Internet, resulting in a paradigm shift towards the (Industrial) Internet of Things. Automation systems and remote data acquisition systems evolved over the last years from classic solutions based on wired infrastructure to modern, flexible solutions bringing together wireless communications, battery powered devices and wireless sensors networks. In this paper we first define the main problems encountered in modern wireless automation systems - small distance limitations of wireless communication solutions, weak security or complete lack of security in data transmission, and high energy consumption of battery powered remote data acquisition systems. In order to address these limitations, we propose, implement, trial-run and benchmark a novel low power LoRa-based flexible hardware architecture for use in industrial remote monitoring and control. We obtain a LoS capability above 3km and a calculated autonomy of about 10 years, exceeding the capabilities of current State of the Art solutions.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in industrial automation and cybersecurity has become increasingly vital as industries transition towards smarter and more efficient systems. With the rapid advancement of technology, AI and ML are playing a key role in optimizing operations, enhancing safety, and improving overall system performance. This paper explores the applications of AI and ML in industrial automation, focusing on their transformative impact on manufacturing, process control, and predictive maintenance. Additionally, the paper highlights the critical role these technologies play in enhancing cybersecurity frameworks, particularly in the context of industrial control systems and critical infrastructure. The combination of AI and ML allows for real-time decision-making, anomaly detection, and the mitigation of security risks in an increasingly interconnected industrial environment. The findings suggest that AI and ML are not only optimizing operational efficiency but also providing advanced threat detection mechanisms to combat sophisticated cyber threats. This paper presents a detailed review of these technologies' capabilities and challenges, offering insights into their future potential in industrial settings.

This article explores the widespread application and importance of the Programmable Logic Controller (PLC) in industry. PLC, as an industrial controller designed specifically for industrial environments, occupies an important position in the field of industrial automation control due to its advantages of being less susceptible to interference, high reliability, easy programming, fast installation, and small size. With the rapid development of PLC technology, great progress has been made in the development of technology, hardware, and software, resulting in increasingly powerful functionalities and significantly enhanced system development and compatibility. This paper mainly analyzes the design ideas and applications of PLC in life and industry in detail, aiming to research PLC and find more scenes in industry fields in which PLC can participate. PLC has a wide range of applications and significant importance in industry. The continuous progress and innovation of its technology will bring more opportunities and challenges to the field of industrial automation control. In the future, with the deepening development of advanced manufacturing models such as Industry 4.0 and intelligent manufacturing, PLC technology is expected to play a greater role in more fields and promote industrial automation to a higher level.

임베디드 기기는 시퀀스 제어 기능과 수치연산 기능을 활용하여 제어 프로그램에 따라 산업현장의 기기 등 다양한 자동화 시스템에 활용된다. 현재 임베디드 기기는 기업의 산업현장, 원전, 대중교통 같은 국가기반시설에서 제어 시스템으로 활용되고 있다. 따라서 임베디드 기기를 대상으로 하는 공격은 큰 경제적 손실과 사회적 손실을 야기할 수 있다. 임베디드 기기를 대상으로 하는 공격은 대부분 데이터, 코드 변조로서 제어 프로그램을 대상으로 이루어진다. 산업 자동화 임베디드 기기의 제어 프로 그램은 일반적인 프로그래밍 언어와 달리 회로 구조를 표현하기 위하여 설계되었고, 대부분의 산업 자동화 제어 프로그램은 그래픽 기반 언어인 LAD로 설계되어있어 정적분석이 용이하지 않다. 이러한 특징으로 인하여 산업 자동화 제어 프로그램에 대한 취약점 분석 및 보안 관련 연구는 정형 검증, 실시간 모니터링 수준에 그친다. 또한 사전에 취약점을 탐지하고 공격에 대한 대비가 가능한 산업 자동화 제어 프로그램 정적분석 연구는 매우 저조한 실정이다. 따라서 본 연구에서는 산업 자동화 임베디드 프로그램에 대한 정적분석 효율성 증대를 위하여 회로 구조를 표현하기 위해 설계된 산업 자동화 제어 프로그램을 논리식으로 표현하기 위한 방법을 제시한다. 또한 다양한 제조사의 산업 자동화 제어 프로그램을 통합적으로 분석하기 위하여 LLVM IR을 활용한 중간어 변환 기술을 제안한다. LLVM IR을 활용함으로서 동적 분석에 대한 통합분석이 가능하다. 본연구에서는 해당 방법에 대한 검증을 위하여 S 社의 제어 프로그램을 대상으로 하여 논리식 형태의 중간어로 변환하는 프로그 램의 시제품을 개발하였다.Utilizing sequence control and numerical computing, embedded devices are used in a variety of automated systems, including those at industrial sites, in accordance with their control program. Since embedded devices are used as a control system in corporate industrial complexes, nuclear power plants and public transport infrastructure nowadays, deliberate attacks on them can cause significant economic and social damages. Most attacks aimed at embedded devices are data-coded, code-modulated, and control-programmed. The control programs for industry-automated embedded devices are designed to represent circuit structures, unlike common programming languages, and most industrial automation control programs are designed with a graphical language, LAD, which is difficult to process static analysis. Because of these characteristics, the vulnerability analysis and security related studies for industry automation control programs have only progressed up to the formal verification, real-time monitoring levels. Furthermore, the static analysis of industrial automation control programs, which can detect vulnerabilities in advance and prepare for attacks, stays poorly researched. Therefore, this study suggests a method to present a discussion on an industry automation control program designed to represent the circuit structure to increase the efficiency of static analysis of embedded industrial automation programs. It also proposes a medium term translation technology exploiting LLVM IR to comprehensively analyze the industrial automation control programs of various manufacturers. By using LLVM IR, it is possible to perform integrated analysis on dynamic analysis. In this study, a prototype program that converts to a logical expression type of medium language was developed with regards to the S company s control program in order to verify our method.

Power system substations have intelligent electronic devices (IEDs) that collect data and control other devices. As the bridge between the physical and cyber parts of the power system, IEDs capture some key system behaviors. Since adversaries can modify the system’s behavior, physical and cyber data can be used to infer characteristics about the adversary. In this article, we present alert and control logic for hardware-based power system defense using the physical data and communication status in substation IEDs for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyber threat detection</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyber-physical contingency detection and response</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physical contingency identification and response</i> . The proposed alert and control logic routines are implemented in an industrial real-time automation controller using IEC 61131-3 in the resilient energy systems lab testbed. The goal is to help operators identify adversaries and protect the power grid in a cyber-physical environment. The effectiveness and accuracy of logic schemes are validated under different adversarial scenarios. Comparing the proposed schemes with an intrusion detection system, Snort, our results also suggest the benefits of using cyber and physical data to identify threats. The results also suggest the use of such hardware-based schemes with software algorithms in a next-generation cyber-physical energy management system, which can implement automatic control actions to protect power grids and its physical equipment against cyber threats.

Fog computing extends cloud capabilities to the net-work edge, providing low-latency services for mission-critical applications such as healthcare and industrial automation. However, validating security-aware scheduling policies in this distributed paradigm remains a challenge due to the lack of native support for trust modeling in standard simulation tools like CloudSim and iFogSim. Existing simulators focus primarily on resource provisioning, cost, and energy metrics, often neglecting “Trust” and “Data Confidentiality” as first-class simulation parameters. This study presents the design and implementation of a trust-aware extension for WorkflowSim. We detail the software architecture modifications required to support attribute-based trust verification, secure task fragmentation, and encryption overhead modeling. Unlike standard simulators, our extended framework treats Trust as a dynamic entity in the Virtual Machine (VM) and Task class hierarchy. We validate the framework’s correctness through unit tests and scenario-based trace analysis, demonstrating its ability to accurately model security-performance trade-offs in heterogeneous fog environments. Experimental results indicate that enabling trust-aware scheduling increases makespan by approximately 19% and cost by 16%, while achieving a security score of 0.88 compared to 0.65 for the baseline. The simulation engine overhead remains below 7.1% even for 1000-task workflows. The source code and design patterns presented provide researchers with a robust, extensible tool for evaluating secure scheduling algorithms without the cost of physical testbeds.