DSLs for Model Driven Development of Secure Interoperable Automation Systems with EdgeX Foundry

The rapid integration of industrial automation and control systems into large-scale Industrial Internet of Things (IIoT) infrastructures has substantially increased system complexity. The complexity arises from the deployment of heterogeneous devices that must coordinate in real-time to support adaptive decision-making. As a result, a robust situation awareness (SA) framework for IIoT has become a critical goal of IIoT. This review investigates the role of SA in the design of controllers in industrial applications, with the aim of advancing control strategies and facilitating integration with emerging IoT technologies. The survey focuses on SA, Model Predictive Control (MPC), and their integration, based on time series as a standard data structure to represent device performance. A key finding from the current literature is that SA is often treated separately from control theory, resulting in isolated applications and not integrated with large-scale IIoT systems. This survey establishes the features and paradigms of SA in IIoT. It provides a structured analysis of controller design methods in IIoT. State-space models (SSMs) can be used to model temporal dynamics in IIoT data. Finally, the integration of artificial intelligence (AI) with SSMs is explored to achieve SA based on multivariate time series data.

The Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) intersected in the realm of industry, transforming the industrial system to allow making real-time decisions at the edge. Latency issues, bandwidth consumption, and data security also pose a challenge to the traditional cloud-based system, and are of utmost priority in the industry where time is limited. Edge AI combines AI features with edge computing, moving the intelligence to the devices in the IIoT, allowing decisions to be made faster and more responsive to the context without depending on centralized infrastructures that might overwhelm the cloud. The paper summarizes the present-day developments in edge AI as a decision-making tool in IIoT and identifies the future directions. It discusses architectures, algorithms and applications that enable intelligent decision making at the network edge with particular focus on manufacturing, predictive maintenance, supply chain optimization and energy management. It is reviewed that facilitating technologies, such as lightweight deep learning models, federated learning, and hardware accelerators, and problems, such as scalability, interoperability, and cybersecurity are discussed. As depicted in the literature section, edge AI has been found to enhance efficiency of distributed industrial systems by reducing latencies, reliability, and independent decision-making. However, barriers such as the shortage of resources, failure to interface with current systems and standardized structures still persist. It is anticipated that future studies will be based on adaptive AI models and edge-cloud collaboration with application of 6G-enabled IIoT ecosystems. This paper summarizes the synthesis of state-of-the-art methods to inform the next generation of industrial automation and digital transformation powered by edge AI. It indicates the need of powerful, hardy, and scalable systems to open up the complete scope of opportunities that edge AI can introduce to decisions made by IIoT.

As a typical application of the Internet of Things (IoT), the Industrial IoT (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big-data-driven modeling and analysis, could be integrated in IIoT systems to aid the automation and intelligence of IIoT systems. As deep learning requires large computation power, it is commonly deployed in cloud servers. Thus, the data collected by IoT devices must be transmitted to the cloud for training process, contributing to network congestion and affecting the IoT network performance as well as the supported applications. To address this issue, in this article, we leverage the fog/edge computing paradigm and propose an edge computing-based deep learning model, which utilizes edge computing to migrate the deep learning process from cloud servers to edge nodes, reducing data transmission demands in the IIoT network and mitigating network congestion. Since edge nodes have limited computation ability compared to servers, we design a mechanism to optimize the deep learning model so that its requirements for computational power can be reduced. To evaluate our proposed solution, we design a testbed implemented in the Google cloud and deploy the proposed convolutional neural network (CNN) model, utilizing a real-world IIoT data set to evaluate our approach. 1 Our experimental results confirm the effectiveness of our approach, which cannot only reduce the network traffic overhead for IIoT but also maintain the classification accuracy in comparison with several baseline schemes. 1 Certain commercial equipment, instruments, or materials are identified in this article in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

• Using SMOTE-ENN to balance data and enhance transformer DGA fault diagnosis. • Applying RobustScaler to preprocess data, improving accuracy against outliers. • Proposing a DNN with RobustScaler, SMOTE-ENN, and an optimal optimizer. • Integrating the deep learning model with IoT for fault visualization. • IoT-based monitoring analyzes gas data for faster transformer fault detection. One of the most vital components of power systems is power transformers, which provide an essential link in the chain of other devices used to supply electricity to consumers. According to the literature, the Duval pentagon method (DPM) is one of the most accurate and reliable dissolved gas analysis (DGA) interpretation methodologies. However, implementing large amounts of data in DPM is still challenging and has several limitations. To overcome these limitations, this paper introduces a robust deep neural network (DNN) method for precise DGA monitoring. Another merit is the proposal of synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) preprocessing to eliminate noise from the imbalanced dataset, resulting in cleaner merged DGA samples. Furthermore, a unique RobustScaler technique is employed to maintain high performance against uncertain data noise. To visualize transformer faults remotely and enhance the acceleration of decision-making regarding the transformer status, this paper utilizes an industrial Internet of Things (IoT) platform. Specifically, the designed deep learning model is hybridized with an IoT platform to analyze the transferred DPM dataset of the gases concentration and send the classification results using the IoT gateway to the cloud for visualizing the detected fault on the IoT dashboard. The empirical results display that the proposed method outperforms several state-of-the-art approaches. The proposed method achieves satisfaction in diagnosing faults for the assessment dataset, with an accuracy of 98.19 %. Besides, the obtained results illustrate the effectiveness of the proposed model against uncertainty noise up to 20 % with a superior prediction diagnosis of the transformer faults.

As industrial Internet of things (IIoT) for Manufacturing Service Collaboration (MSC) is becoming the current trend to accelerate the upgrade iteration of manufacturing capability, developing the robust IIoT platform operation mechanism for MSC is crucial to promote the continuous and stable service collaboration in the presence of supply and demand uncertainties. This paper studies the operational robustness of the IIoT platform for MSC. Firstly, the operation performances, requirements, and challenges of the IIoT platform towards manufacturing collaboration are analysed in classified platform practices, which can provide a comprehensive cognition about platform operation for manufacturing collaboration. Then, to evaluate the tolerance and persistence capabilities of MSC under supply and demand uncertainties, a graph-based operational robustness analysis method of the IIoT platform for MSC is proposed. The IIoT platform operation network for MSC is modelled as an interdependent network-of-network structure based on graph theory, which helps to characterise MSC performance properties under complexities. By combining manufacturing properties with network statistics, the evaluation metrics of operational robustness are established, which is done to quantise the MSC effectiveness under uncertainty effects. A case about customised manufacturing of automobiles illustrates the application of the proposed methods. Finally, future studies about robust MSC regulation are discussed.

Driving aspects in the developments of the Industrial Internet of Things (IIoT) are based on markets demanding a highly flexible production on the one hand and, on the other hand, by the production industry looking for new business models. This is enabled by interconnecting intelligent devices and aggregating and analyzing huge amounts of data. In the section of field devices, targeted new developments are dealing with intelligent systems which support users in each stage of the product life cycle. During installation and commissioning of a new machine, the concept of Plug-and-Produce has been created in the production industry. It is relating to the analogy of Plug-and-Play in the field of information technology which makes it possible to recognize and use devices without additional effort across several platforms. Industrial production systems differ in some aspects from Plug-and-Play computer devices that these methods are not applicable without adjustments and more advanced considerations. Solutions to support the commissioning process are scope of this contribution and analyzed theoretically by the example of the integration of an electro-hydraulic actuator. Two results are highlighted in this contribution. Different IIoT related concepts and technologies (i.e. OPC-UA, Cyber-physical systems, semantics) are presented and merged to realize Plug-and-Produce as an holistic business process. Furthermore, the draft combines the domain of fluid power with the abstract and generalized concepts and models and gives an understanding of future requirements for fluid power field devices in the context of IIoT.

The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.

The applications of Internet of Things in the automotive industry: A review of the batteries, fuel cells, and engines

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value (μ) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations.

Industrial internet of things (IIoT) enables edge computing technology to provide communication between the machines that produce a large amount of data and locate at the edge network. A task scheduling is implemented in the edge node. Furthermore, the real-time data can achieve with the lowest latency that allowed by the edge node near the edge network. However, a mobile machine such as an autonomous guided vehicle can interfere in this situation. Because the vehicle also needs service by the edge node. Over that, quality of service (QoS) performance can decrease. Therefore, this paper deploys an unmanned aerial vehicle (UAV) as an edge node to provide service to the edge network through optimizing the trajectory of UAV, where the edge network request task using a Deep Q-Network (DQN) Learning. The result shows that using machine learning, notably the DQN algorithm, can increase the number of the machine that can be provided service. Subsequently, the real-time data can achieve either the interrupt occurs at the edge node.

New generation Industrial Automation and Control Systems (IACS) are providing advanced connectivity features, enabling new automation applications, services and business models in the Industrial Internet of Things (IIoT) era. Nevertheless, due to the extended attack surface and increasing number of cyber-attacks against industrial equipment, security concerns arise. Hence, these systems should provide enough protection and resiliency against cyber-attacks throughout their entire lifespan, which, in the case of industrial systems, may last several decades. A sound and complete management of security issues and software updates is fundamental to achieve such goal, since leading-edge security countermeasures implemented in the development phase may eventually become out-of-date. In this article, a review of the IEC 62443 industrial security standard concerning the security maintenance of IIoT systems and components is given, along with guidelines for the implementation of such processes. As concluded, the security issues and software updates management shall jointly be addressed by the asset owner, service providers and product suppliers. These security processes should also be compatible with the safety procedures established by safety standards.

A Survey on blockchain for industrial Internet of Things

The application of workflow in office OA has gradually matured, but it needs to be more real-time and random in the field of industrial Internet of things. The traditional modeling methods and process design can not meet the industrial process scenarios in industrial Internet of things. Therefore, this paper takes the application of workflow in the industrial Internet of things as the background, and aims to realize the workflow engine platform of probabilistic timed automata based on the industrial Internet of things. In this paper, through the requirements analysis, based on probabilistic timed automata to establish the process model based on industrial Internet of things, establish workflow engine, design the overall idea of workflow system platform, we adopt multi module structure and Spring Boot + MyBatis + Apache Shiro + Redis + Flowable + Beetl and other mainstream technologies to form a complete set of workflow engine platform. It can meet the requirements of industrial process and make the industrial process more efficient.

Industry 4.0 is a new concept representing the evolution of the current production systems through the convergence of new technologies of industrial automation and information systems. The Industrial Internet of Things (IIoT) stands out for the communication among equipment and systems, offering to users a variety of useful information for the management of improvement of production systems. This paper presents the development of a wireless gateway for IIoT applications. This IIoT gateway is based on open source solutions and enables the programming, network communication and supervision of the industrial equipment. The communication protocols supported by the gateway are: Modbus TCP/IP, MQTT and CoAP for connection between the remote modules and the gateway and HTTP using the REST standard with JSON format for external communication with the gateway. An innovation and differential of the gateway was adding the OpenPLC project in order to enable the programming and automation of industrial processes according to the IEC 61131-3. In addition, the gateway stores process information and makes it available online and accessible in real-time to any point connected to the network and/or the Internet. Several operation tests performed validated the development and demonstrated the efficacy of the gateway.

Automation and control systems are becoming more networked thanks to the Industrial Internet of Things (IIoT). The SCADA network has also developed into a more open and interconnected system. In addition, by connecting industrial electronic devices to the SCADA network, the interconnectivity and efficiency of methods have been significantly increased owing to the control and monitoring benefits of SCADA. The SCADA system is inextricably linked to the public network, and security breaches and cyber-attacks are a legitimate worry for the SCADA network system. We propose to deter attackers from launching distributed denial-of-service attacks in this work with a token authentication module. According to the experimental findings, the security defence architecture provided by this study may successfully increase security while being compatible with realworld energy management systems in the field.