A Computer Science Perspective on Digital Transformation in Production

Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

The increasing integration of traditional industrial systems with smart networking and communications technology (such as fifth-generation networks, software-defined networking, and digital twin), has drastically widened the security vulnerabilities of the industrial internet of things (IIoT). Nevertheless, owing to the lack of sufficient instances of high-quality attacks, it has been incredibly difficult to resist the cyberattacks that directed at such a substantial, complicated, and dynamic IIoT. This work introduces an intelligent federated deep learning framework, termed FED-SEC, for automatic and early identification of cyber-attacks against IIoT infrastructure. In particular, a new convolutional recurrent network designed to detect cyberattacks within IIoT data. Then, a secure federated learning scheme presented to promote making use of mobile edge computing to enable the distributed IIoT entities to cooperate together to train a unified model for cyberattack detection in a privacy-preserved manner. More, a safe communication channel constructed via an improved Homomorphic Encryption scheme aiming to keep the model parameters secure against any leakage of inferential attacks, especially throughout the training procedure. Massive experimentations on multiple public datasets of IIoT cyberattacks proved the high-level efficacy of the FED-SEC in discovering different categories of cyber-attacks against IIoT and the superiorities over cutting-edge approaches.

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.

This review article provides an overview of the potential of the Industrial Internet of Things (IIoT) to revolutionize industrial automation. The IIoT is the Internet of Things (IoT) but in an industrial context, i.e., IIoT is used more to connect machines and devices in industrial environments. The IIoT has the potential to benefit from advances in artificial intelligence, particularly machine learning and deep learning, to increase efficiency and productivity and reduce overhead costs. We provide an overview of the supervisory control and data acquisition system, a definition of IIoT, and how IIoT can offer industry greater potential for system integration to improve automation and optimization. In addition, five of the major IIoT protocols are discussed, namely, message queue telemetry transport, advanced messaging queuing protocol, constrained application protocol, data distribution service, and open platform communication unified architecture. We then identified key IIoT improvements for industrial automation. These are; efficient and low-cost systems, digital twin, machine failure prediction, real-time remote monitoring, and security. We then discussed the key research in the literature for each category. We presented some public IIoT datasets so that researchers can use them to develop new learning models to improve the security of IIoT systems. Finally, we discussed some of the limitations, recommendations, and future perspectives for developing IIoT-enabled systems.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

The industry use cases for the Digital Twin idea

Blockchain and PUF-based secure key establishment protocol for cross-domain digital twins in industrial Internet of Things architecture

File- and API-based interoperability of digital twins by model transformation: An IIoT case study using asset administration shell

The rapid evolution of digital technology and designed intelligence, such as the Internet of Things (IoT), Big data analytics, Artificial Intelligence (AI), Cyber Physical Systems (CPS), has been a catalyst for the 4th industrial revolution (known as industry 4.0). Among other, the two key state-of-the-art concepts in Industry 4.0, are Industrial IoT (IIoT) and digital twins. IIoT facilitates real-time data acquisition, processing and analytics over large amount of sensor data streams produced by sensors installed within a smart factory, while the ‘digital twin’ concept aims to enable smart factories via the digital replication or representation of physical machines, processes, people in cyber-space. This paper explores the capability of present-state open-source platforms to collectively achieve digital twin capabilities, including IoT real-time data acquisition, virtual representation, analytics, and visualisation. The aim of this work is to ‘close the gap’ between research and implementation, through a collective open source IoT and Digital Twin architecture. The performance of the open-source architecture in this work, is demonstrated in a use-case utilising industry ‘open data’, and is bench-marked with universal testing tools.

Digital Twins are starting to revolutionize many industries in the last decade providing a plethora of benefits to optimize the performance of industrial systems. They aim to create a continuously synchronized model of the physical system which enables rapid adaptation to dynamics, mainly unpredicted and undesirable changes. A wide range of industrial fields have already benefited from digital twins technology such as aerospace, manufacturing, healthcare, city management and maritime and shipping. Furthermore, recent research works are starting to study the integration of digital twins for computer networks to allow more innovation and intelligent management. One of the basic building blocks of digital twins technology is the Internet of Things where wireless sensors and actuators are deployed to ensure the interaction between the physical and digital worlds. This type of network is complex to manage due to its severe constraints especially when it controls critical industrial applications, resulting in the Industrial Internet of Things (IIoT). We believe that the optimization of the IIoT will lead to efficient integration of Digital Twins in Industry 4.0. In this paper, we design a Network Digital Twin for the IIoT where sensors, actuators and communication infrastructure are replicated in the digital twin to enable intelligent real-time management of such networks. This way, new networking services such as predictive maintenance, network diagnosis, resource allocation, energy optimization with other intelligent services can be efficiently integrated and exploited in the network life-cycle. We validate the proposed architecture by providing a promising prototype implementation that should unleash the full potential of the network digital twin.

Blockchain-based trust mechanism for digital twin empowered Industrial Internet of Things

Emerging technologies such as digital twins and 6th Generation mobile networks (6G) have accelerated the realization of edge intelligence in Industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users, hinder the effective application of federated learning in IIoT. In this paper, we introduce the Digital Twin Wireless Networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system, and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multi-agent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning method.

The current massive use of machine learning and the 5G networks have supported the high demand for the digital twin and made it more popular and common in the industrial sector and scientific research related to smart manufacturing. As part of this research study, the currently available opportunities for smart manufacturing using digital twins have been reviewed and discussed in the industry 4.0 field. While digital twins have garnered much attention in the industrial internet of things, their use in smart manufacturing has been much less common. This discussion here focuses on the open challenges in smart manufacturing and industry 4.0 and suggests that in some cases. Digital twins should be treated differently to enhance industrial processes and smart manufacturing applications. On the other hand, this research examines the impact of digital twins on smart manufacturing and sustainable production rates, aiming to promote the industry’s digital transformation to meet the required production rate. The research discussed the digital twin concept and its origins and perspectives from academia and industrial sectors. It reveals its potential for the digitalization of manufacturing. Also, the review discussed how the digital twins could support the integrated, flexible, and collaborative manufacturing environments associated with the fourth industrial revolution. Different industrial operational technologies and communication technologies have profoundly changed smart manufacturing. Intelligent and automated information exchange, automated machine control, and interoperable production systems have all been enabled by Industry 4.0. System-level CPS and digital twins can collaborate through Smart Service Platforms for Digital Twins. In addition to optimizing production configurations, the digital twin is used to determine the impact of decisions made during modifications or upgrades. By analyzing the predictive maintenance of manufacturing lines, the time between production delays will be reduced. A specific alarm or notification will be sent to the user to enable them to take quick action. A digital twin application analyzes and simulates data by controlling, monitoring, and optimizing variables based on various factors in both online and offline modes. This study seeks to evaluate the evolution of digital twin concepts and their relevance to smart manufacturing. It summarized and explained the current state of digital twins in manufacturing literature, highlighting future directions for studies and the highest potential for future applications.

As health-care budgets are continuously under increasing demands, Artificial Intelligence resources such as digital heart twins could save millions of dollars by predicting results and preventing unnecessary surgery. Can we start to make digital human body twins to plant and predict health outcomes for a patient? By using a way to design competent simulation models from real objects, digital twins were created through IoT. But the digital twin is a complicated system and a very long-drawn step away from its possibilities. Researchers must design all components of entities or structures. There is a need to collect and merge various types of data. Many engineering researchers and participants aren’t sure about which technologies and resources to use. The 3D digital twin model offers a reference guide for digital twin comprehension and implementation. This paper aims to investigate and outline the recent technologies and tools used for digital twin applications from a 3-D digital model perspective, such as references to technologies and tools for future digital twin applications.

In this paper we propose an approach for an integrated and iterative performance tuning of an Industrial Internet of Things (IIoT) digital twin. Using the systematic series of steps in the proposed approach, the digital twin- linked to its real-world counterpart, can now be used in different ways to predict, refine, fine tune and optimize the operation of the asset (e.g. Cyber Physical System (CPS) in the IIoT plant) during various stages of its software development life cycle.