Integration of Industrial Internet of Things (IIoT) and Digital Twin Technology for Intelligent Multi-Loop Oil-and-Gas Process Control

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

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.

Digital twin (DT) can bridge the physical status with the virtual space in real-time for the Industrial Internet of Things (IIoT), where the integration of federated learning (FL) with DT can enable many edge intelligence services for timely intelligent production in the era of Industry 4.0. However, the issues of heterogeneity of devices and the resource-constrained IIoT make it challenging to achieve efficient FL via DT technology. To handle this problem, we propose a DT-enabled IIoT (DTENI) framework in wireless networks, in which DTs capture the characteristics of industrial devices to enable real-time processing and intelligent decision-making. Specifically, we first analyze the necessity of adaptive wireless parameters (i.e., CPU frequency, bandwidth, and transmission power) on FL training performance and provide theoretical analysis in the DTENI IIoT. Based on the above analysis, we then formulate the minimization problem of FL model loss under a given resource budget, which is a stochastic optimization problem with strongly coupled wireless parameters variables. Benefiting from the model-free learning superiority of deep reinforcement learning (DRL) in dealing with stochastic optimization problems, we develop DTENI-assisted DRL to adaptively adjust the wireless parameters for solving this optimization problem. Lastly, simulation results demonstrate that our proposed scheme can mostly save communication costs up to 74.23%, 69.51%, and 60.94% compared to the three benchmarks.

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

The Industrial Internet of Things(IIoT) deepens the collaborative computing between different devices and control layers. IIoT, as a dynamic, time-varying and complex environment, digital twin(DT) drives the real-time dynamic mapping between physical devices and twins to achieve global control of industrial production lines. This paper reviews the relevant research on the application and deployment of DT in the IIoT, which provides technical support for cross-production line data sharing and accurate decision-making. In particular, the related work of the multi-layer collaborative computing architecture built by federated learning(FL) in DT-IIoT is reviewed. We systematically explore the key technical breakthroughs of DT and FL in the IIoT, such as data security collaboration, dynamic resource scheduling, computing efficiency improvement, and cross-domain collaborative computing, from four technologies: edge computing, blockchain, deep reinforcement learning, and personalized learning. On this basis, we have explored and experimented in detail with fully decentralized SL in IIoT. SL-knowledge distillation(KD), it can solve the challenge of reducing the reliability of global model decisions in SL due to multi-source heterogeneous data in complex industrial scenarios. Experimental results show that SLKD outperforms other baselines by an average of 0.024 and 0.060 in recall and accuracy. In addition, we discuss the current challenges and future research directions.

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.

The Industrial Internet of Things (IIoT) has revolutionized several industries by improving the communication of sensor data among interconnected machines and systems. IIoT frameworks, on the other hand, can be challenging to set up and keep functioning several times. This paper introducing Task Offloading (TO) into the Internet of Things (IoT) brings about plenty of challenges, which will be addressed throughout this study. Tasks that use an enormous number of resources are allocated to remote server locations in the cloud in order to be performed. As a result of the objective of optimizing decisions involving Optimize Task Offloading (OTO), it should be considered that Digital Twins (DT) be developed. DTs are automated backups of physical objects or systems that can be used to perform data collection in real-time, management’s decisions, and optimization. DTs are also identified as digital copies of things. Using DT, constantly tracked in real-time, and Metaheuristic Optimization (MO) computational approaches, this research recommends a Task Offloading Model (TOM) for the IIoT. In the model, the Task Execution Time (TET) is reduced to a minimum by limited factors such as the server’s computing power, the constraints on bandwidth, and the Energy Consumption (EC) of the device. In order to efficiently increase OTO results, the Offloading with Digital Twins and Raindrop Algorithm (ODTRA) method that has been industrialized makes use of the Water Cycle Metaphor (WCM) and the Probabilistic Recursive Local Search (PRLS) technique. It is feasible to implement the algorithm in real-time IIoT environments through the presence of DT in order to progress decision-making and provide real-time monitoring. This work reviews the assumptions of a research study that analyzes the performance of OTO in IIoT environments, with a detailed emphasis on the potential for real-time deployment.

To Date, the application of digital twin (DT) in the industrial internet of things (IIoT) has been continuously promoted and deepened, and has become the focus of the industry. IIoT serves as the foundational infrastructure that enables pervasive connectivity, real-time data acquisition, and intelligent control within industrial environments. DTs provide enterprises with an empathetic, virtual environment that enables them to manage and operate their production facilities in a more efficient and intelligent manner. However, there is not a special summary and analysis of the combinability and combination mode of the two. Therefore, this paper firstly sorted out the professional definitions, characteristics and frameworks of IIoT and DT, and deeply analyzed the semantic context of data flow. Secondly, this paper discusses the combinability and combination mode of IIoT and DT, and summarizes the enabling technologies and tools at each layers. Finally, the applications status of DT empowered IIoT in different fields was summarized, and the challenges of the combined application of the two were analyzed.

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.

Ensuring robustness against adversarial attacks is imperative for Machine Learning (ML) systems within the critical infrastructures of the Industrial Internet of Things (IIoT). This paper addresses vulnerabilities in IIoT systems, particularly in distributed environments like Federated Learning (FL) by presenting a resilient framework - Secure Federated Learning (SFL) specifically designed to mitigate data and model poisoning, as well as Sybil attacks within these networks. Sybil attacks, involving the creation of multiple fake identities, and poisoning attacks significantly compromise the integrity and reliability of ML models in FL environments. Our SFL framework leverages a Digital Twin (DT) as a critical aggregation checkpoint to counteract data and model poisoning attacks in IIoT’s distributed settings. The DT serves as a protective mechanism during the model update aggregation phase, substantially enhancing the system’s resilience. To further secure IIoT infrastructures, SFL employs blockchain-based Non-Fungible Tokens (NFTs) to authenticate participant identities, effectively preventing Sybil attacks by ensuring traceability and accountability among distributed nodes. Experimental evaluation within IIoT scenarios demonstrates that SFL substantially enhances defensive capabilities, maintaining the integrity and robustness of model learning. Comparative results reveal that the SFL framework, when applied to IIoT federated environments, achieves a commendable 97% accuracy, outperforming conventional FL approaches. SFL also demonstrates a remarkable reduction in loss rate, recording just 0.07 compared to the 0.14 loss rate experienced by standard FL systems. These findings highlight the efficiency and applicability of the SFL framework in enhancing data security and traceability within the IIoT ecosystem.

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

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.

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.