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

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.

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

Blockchain-based technologies have been created across a range of businesses for improving data security and integrity. A network chain is the most remarkable blockchain applications as part of the Industrial Internet of Things (IIoT). Sensor data in real time aids industrial equipment and infrastructures in making decisions and taking specified actions. Without sufficient security, an IIoT system can cause operational disruption and financial loss and lead to malicious activities and system manipulation. Detecting anomalies in IIoT networks is critical for network security. In this paper we discuss the Proof of Authority consensus algorithm. The Proof of Authority (PoA) depends on the reputation of trusted parties in blockchain network. The PoA consensus mechanism is based on the values of identities on a network, and validators stake their own identities and reputation. So, the validating nodes that are randomly picked as trustworthy safeguard the Proof of Authority Blockchain network. In Proof of Authority model, transactions are reviewed by already approved network users, and it operates with a fixed number of block validators. Since the identities of the nodes are trusted and known, the process can be used in applications like supply chains and trade networks.

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.

The industrial Internet of things (IIoT) is an important engine for manufacturing enterprises to provide intelligent products and services. The data analysis model represented by digital twins is the core of IIoT development in the manufacturing industry. With the development of IIoT, more and more attention has been paid to the application of ultra-reliable and low latency communications (URLLC) in the field of IIoT. This paper mainly introduces the development of 3GPP for URLLC in reducing delay and enhancing reliability, as well as the research on little jitter and high transmission efficiency, and further analyzes the enhanced key technologies required in the IIoT. Finally, the application of IIoT in digital twins is analyzed according to the actual situation.

The industry use cases for the Digital Twin idea

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.

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

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 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.

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.

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.

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.