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In recent years, the emerging Digital Twin (DT) paradigm under Industry 4.0 has been attracting more attention from both practitioners and academia due to its dynamic capabilities. Most DT studies are theoretical and deal with hypothetical analysis, whereas fewer studies are available on real-life empirical cases. Due to dynamic problem-solving capabilities, DT technologies are widely used in performance improvement analysis in food processing companies (FPC) despite limited implication's for business strategies. Our study incorporates the DT technologies with an implementation case study on FPC and accomplishes the real-life problem of the food processing company (FPC). Moreover, the proposed DT research framework demonstrates the various DT implementation stages, such as strategic mapping and physical-virtual space replica, with rigorous analysis. The results show that DT enhances the existing system's machine availability, allocation efficiency, technical efficiency, worker efficiency, utilization rate, effectiveness, step ratio, and throughput rate. The proposed physical-virtual interface model is executed using AnyLogic software with JAVA-enabled programming. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT) has undergone rapid growth in recent years, which has resulted in an increase in the number of threats targeting both IIoT devices and their connecting technologies. However, deploying tools to counter these threats involves tackling inherent limitations, such as limited processing power, memory, and network bandwidth. As a result, traditional solutions, such as the ones used for desktop computers or servers, cannot be applied directly in the IIoT, and the development of new technologies is essential to overcome this issue. One approach that has shown potential for this new paradigm is the implementation of intrusion detection system (IDS) that rely on machine learning (ML) techniques. These IDSs can be deployed in the industrial control system or even at the edge layer of the IIoT topology. However, one of their drawbacks is that, depending on the factory's specifications, it can be quite challenging to locate sufficient traffic data to train these models. In order to address this problem, this study introduces a novel IDS based on the TabPFN model, which can operate on small datasets of IIoT traffic and protocols, as not in general much traffic is generated in this environment. To assess its efficacy, it is compared against other ML algorithms, such as random forest, XGBoost, and LightGBM, by evaluating each method with different training set sizes and varying numbers of classes to classify. Overall, TabPFN produced the most promising outcomes, with a 10–20% differentiation in each metric. The best performance was observed when working with 1000 training set samples, obtaining an F1 score of 81% for 6-class classification and 72% for 10-class classification. [ABSTRACT FROM AUTHOR]

The calibration industry is renowned for its diverse and sophisticated equipment and complex processes, which necessitate innovative solutions to keep pace with rapidly advancing technology. This paper introduces an enhancement to an existing microservice-based cloud architecture, aimed at effectively managing the inherent complexity within this field. The enhanced architecture seamlessly integrates various equipment types and communication technologies, aligning diverse stakeholder expectations into a unified system that ensures efficient and accurate calibration processes. It highlights the integration of microservices to facilitate various methods of uncertainty calculation and the generation of digital calibration certificates (DCCs). A case study on RF power measurement illustrates the practical application and benefits of the enhanced architecture. Although initially focused on RF power measurement, the flexible architecture allows for future expansions to accommodate new standards and measurement techniques. The enhanced system offers a comprehensive approach to managing data flow from calibration equipment to the final generation of DCCs, utilizing cloud-based services for efficient data processing. As a future direction, this extension sets the groundwork for broader applicability across multiple measurement types, ensuring readiness for upcoming advancements in metrology. [ABSTRACT FROM AUTHOR]

The integration of the Industrial Internet of Things (IIoT) brings about a significant improvement in the efficiency and productivity of industrial processes. The speed and accuracy of various tasks have been greatly enhanced with the utilization of IIoT which allows smoother operations and cost-effective solutions, to protect uninterrupted intrusions and avoid potential threats. This article proposes a novel recurrent federated network-based enhanced local search grasshopper (RFN-ELG) algorithm. Datasets like UNSW-NB15 and MQTT-IoT-IDS2020 datasets are employed to determine the performances of IIoT via two diverse phases, namely the data preprocessing phase as well as attack detection phase. The recurrent neural networks (RNNs) are integrated with federated learning (FL) to overcome the gradient issues during the training process. In addition to this, the hyperparameters of RNN-FL are tuned via a grasshopper optimization algorithm with a local search strategy. Accuracy, precision, recall, F1-score, and false alarm rate (FAR) are the performance metrics taken for attack detection, where accuracy attains the value of 98.1%, precision attains the value of 97.4%, recall attains the value of 96.1%, F1-score attains the value of 97.2%, and FAR attains the value of 96.1%, respectively. Therefore, the proposed RFN-ELG algorithm attains high performance in detecting the attacks in IIoT. [ABSTRACT FROM AUTHOR]

Energy consumption optimization is crucial for improving the quality of application services in the industrial Internet of Things (IIoT) environment. Traditional optimization methods often adopt blind search strategies, which leads to a significant waste of computing resources and low efficiency. This paper proposes a Hybrid Cat Swarm Optimization and Monarch Butterfly Optimization (HCSMBO), which combines the advantages of Cat Swarm Optimization (CSO) and Monarch Butterfly Optimization (MBO) to solve this problem. By utilizing the characteristics of swarm intelligence, including strong spatial search ability, high stability, and fast convergence, HCSMBO focuses on detailed optimization of the coverage, efficiency, and energy balance coefficient of Support Vector Machine (SVM) networks. By adjusting the weight factors of the fitness function, this algorithm can effectively find the global optimal solution. The experimental results confirm that HCSMBO significantly improves the intelligence, accuracy, and stability of task scheduling in resource management configuration, while achieving lower energy consumption. The research results of this paper are of great significance for improving the service quality of IIoT applications, meeting personalized business needs, and improving resource utilization and execution efficiency. In addition, they also provide new ideas and methods for energy consumption optimization research in the future IIoT. [ABSTRACT FROM AUTHOR]

Considering Industry 4.0 directions, followed by recent Industry 5.0 principles, interest in integrating legacy systems in industrial manufacturing has emerged. Due to the continuous evolution of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), as well as the rapid extension of the scope and adoption of broader technologies, such integration has become feasible. Even though newly developed equipment provides easier interoperability, the replacement of legacy systems highly impacts cost and sustainability, which usually extends to the entire production process, the operators and the maintenance team, and sometimes even the robustness of the production process. Ensuring the interoperability of legacy systems is a problematic task, being dependent on technologies and development techniques and specific industrial domain particularities. This paper considers strategies to ensure the interoperability of legacy systems in a building-management system scenario where local structures are approached using both industrial protocols and web-based contexts. The solution is built following the Industry 5.0 pillars (sustainability, human focus, resilience) and conceives the entire data acquisition and supervisory solution to be flexible, open-source, resilient, and under the control of company engineers. The chosen environment for interfacing and supervision is Node-RED, enabling IoT and IIoT tools, together with a complete orientation toward digital transformation. This way, it is possible to construct a final result that enhances security while bridging outdated protocols and technologies, eliminating compatibility risks in the context of the evolutionary IIoT, ensuring critical process functions are possible, and aiding operators in complying with regulations governing building-management system (BMS) operations, thus solving the challenges that arise in the complex task of adopting the IoT backbone of digital transformation in relation to the integration of legacy equipment. The obtained solution is tested in an automotive industry building-management system, and the results demonstrate its performance, reliability, and high customizability in a context of openness and low cost. [ABSTRACT FROM AUTHOR]

Anomaly detection (AD) in the industrial internet of things (IIoT) platform is said to be the major module of security the consistency, safety, and efficacy of industrial procedures. In IIoT, several sensors and devices always gather massive data counts from tools, infrastructure, and equipment. Ensuring the safety of IIoT AD systems is vital for protecting against cyber-attacks. AD and Intrusion detection are required to work in tandem to maintain industrial procedures. Machine learning (ML) and deep learning (DL) approaches generally employed for AD in IIoT. These methods allow automated AD and pattern detection. With this motivation, this study develops an enhanced pelican optimization model with an ensemble voting-based anomaly detection (EPOA-EVAD) approach. Primarily, the SMOTE approach is used for class imbalance data handling. The presented EPOA-EVAD technique integrates the EPOA-based feature selection and ensemble voting classifier to enhance the accuracy and efficiency of AD in IIoT environments. In addition, the group teaching optimization algorithm with SMOTE approach is used for the purpose of class imbalance data handling. EPOA optimally chooses the related features from the complex and high-dimensional IIoT data. For AD, the EPOA-EVAD technique involves an ensemble learning process comprising gated recurrent unit (GRU), bi-directional long short-term memory (BiLSTM), and stacked autoencoder (SAE). Lastly, the seagull optimization (SGO) algorithm fine-tunes the parameters compared with the DL approaches. The simulation values exhibited that the proposed system outperforms standard approaches, offering robust and adaptive AD capabilities in dynamic IIoT settings. The proposed EPOA-EVAD technique contributes to the advancement of AD techniques crucial for maintaining the integrity and efficiency of industrial processes in the era of IIoT. [ABSTRACT FROM AUTHOR]

Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last 30 years, the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine's utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating the opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company (New York City, NY, USA), and software tools have been developed by PTC Inc. (Boston, MA, USA) to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo (Esslingen, Germany) Cyber-Physical Lab (FCPL), an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring. [ABSTRACT FROM AUTHOR]

The digitalisation of production has a positive impact on manufacturing processes in terms of resource efficiency and environmental impact, particularly in the form of increased efficiency as well as cost and resource savings. However, the use of digitalisation technologies is also associated with efforts such as costs, CO2 emissions, and raw material consumption. When planning or deciding on the digitalisation of manufacturing systems, it is therefore necessary to assess whether these technologies pay off in terms of sustainability over their life cycle. This literature review (based on the PRISMA guidelines) analyses the relevance of sustainability assessment and its methods for the digitalisation of production in research. The review reveals that research focuses on the benefits of digitalisation technologies in manufacturing, while the assessment of efforts and their benefits is in its infancy. There is a need for further research on holistic assessment methods for digitalisation technologies. In particular, there is a lack of assessment methods that consistently link the economic and environmental dimensions of sustainability, and there is also a lack of guidance for the application of assessment methods in production. [ABSTRACT FROM AUTHOR]

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay. [ABSTRACT FROM AUTHOR]

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge. In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments carried out to verify the feasibility and effectiveness of the proposed method are reported. The model training time with the proposed method is generally 30% to 50% less than that with the random device selection method, and the training energy consumption under the proposed method is generally 35% to 55% less. [ABSTRACT FROM AUTHOR]

In the development of the Power Industry Internet of Things, the security of data interaction has always been an important challenge. In the power-based blockchain Industrial Internet of Things, node data interaction involves a large amount of sensitive data. In the current anti-leakage strategy for power business data interaction, regular expressions are used to identify sensitive data for matching. This approach is only suitable for simple structured data. For the processing of unstructured data, there is a lack of practical matching strategies. Therefore, this paper proposes a deep learning-based anti-leakage method for power business data interaction, aiming to ensure the security of power business data interaction between the State Grid business platform and third-party platforms. This method combines named entity recognition technologies and comprehensively uses regular expressions and the DeBERTa (Decoding-enhanced BERT with disentangled attention)-BiLSTM (Bidirectional Long Short-Term Memory)-CRF (Conditional Random Field) model. This method is based on the DeBERTa (Decoding-enhanced BERT with disentangled attention) model for pre-training feature extraction. It extracts sequence context semantic features through the BiLSTM, and finally obtains the global optimal through the CRF layer tag sequence. Sensitive data matching is performed on interactive structured and unstructured data to identify privacy-sensitive information in the power business. The experimental results show that the F1 score of the proposed method in this paper for identifying sensitive data entities using the CLUENER 2020 dataset reaches 81.26%, which can effectively prevent the risk of power business data leakage and provide innovative solutions for the power industry to ensure data security. [ABSTRACT FROM AUTHOR]

Industry 4.0, the fourth industrial revolution, focuses on the increasing digitization and interconnection of goods, products, value chains, and business models. It aims to transform traditional industries into smart industries by incorporating innovative technologies, connecting people, things, and machines globally. This revolution creates smart factories and manufacturing environments by integrating physical assets into interconnected digital and mechanical processes. A growing domain the Internet of Things (IoT), has changed the way people communicate and interact with things around them, turning common objects into connected objects, providing a universal connection to the Internet. The Internet of Things is a rapidly growing technology that greatly contributes to the realization of Industry 4.0. By connecting the physical world to the digital world, IoT seeks to penetrate our daily environment of people and things "anytime, anywhere, anything and anyone", ideally using any network and service. IoT healthcare, quality control, logistics, energy, agriculture and manufacturing Industrial Internet of Things (IIoT) provides efficient and sustainable production by breaking new ground for a better understanding of the production process. The article explains the concepts of IoT, IIoT, and Industry 4.0, while also delving into the opportunities, threats, and challenges associated with their implementation in a detailed manner. [ABSTRACT FROM AUTHOR]

Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning (ML). While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This study aims to bridge the gap between prototyping single TinyML models and developing reliable TinyML systems in production: (1) Embedded devices operate in dynamically changing conditions. Existing TinyML solutions primarily focus on inference, with models trained offline on powerful machines and deployed as static objects. However, static models may underperform in the real world due to evolving input data distributions. We propose online learning to enable training on constrained devices, adapting local models toward the latest field conditions. (2) Nevertheless, current on-device learning methods struggle with heterogeneous deployment conditions and the scarcity of labeled data when applied across numerous devices. We introduce federated meta-learning incorporating online learning to enhance model generalization, facilitating rapid learning. This approach ensures optimal performance among distributed devices by knowledge sharing. (3) Moreover, TinyML's pivotal advantage is widespread adoption. Embedded devices and TinyML models prioritize extreme efficiency, leading to diverse characteristics ranging from memory and sensors to model architectures. Given their diversity and non-standardized representations, managing these resources becomes challenging as TinyML systems scale up. We present semantic management for the joint management of models and devices at scale. We demonstrate our methods through a basic regression example and then assess them in three real-world TinyML applications: handwritten character image classification, keyword audio classification, and smart building presence detection. The results confirm the effectiveness of our approaches from various perspectives, such as accuracy improvement, resource savings, and engineering effort reduction. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) plays an important role as its implementation requires scalable and secure methods. IIoT integrates databases, machines, various sensors, and services to improve our lives in many ways, including e-healthcare and smarter cities. Accurately identifying attacks in IIoT is critical to mitigating security risks, and the volume of such attacks is rising to an unprecedented level. As IIoT solutions integrate into larger operational systems, additional planning and development of a new mechanism is required to ensure privacy and security. Many deep learning approaches have been developed to focus on intrusion detection. However, such approaches suffer from low detection accuracy, efficiency, privacy, and high time consumption. To mitigate these issues, this study proposes a novel approach called bi-long short-term memory (Bi-LSTM) in IIoT security for privacy and security in industrial systems. To improve the performance of Bi-LSTM, we use modified coot optimization. Our proposed Bi-LSTM approach effectively detects attacks in the IIoT environment. To assess its performance, we analyze various evaluation measures including precision, sensitivity, Matthews correlation coefficient (MCC), and accuracy. The results of our performance analysis reveal that the proposed optimized Bi-LSTM method achieves a higher performance rate of 98.91% accuracy, 95.86% sensitivity, 95.63% MCC, and 97.52% accuracy than other existing approaches. The existing approaches such as CDW_FedAvg, RNN, COSNN, and RCS-DOFH achieved lower accuracy of 98.52%, 97.83%, 95.17%, and 96.24%, respectively. The findings highlight the potential for improved cybersecurity in the IIoT, emphasizing the importance of effective planning and innovative critical systems. [ABSTRACT FROM AUTHOR]

In the digital economy era, efficiently leveraging the vast amount of sensor data generated by the Industrial Internet of Things (IIoT) is essential. This paper presents an innovative semantic annotation method for industrial sensor data, designed to optimize data processing and enhance system efficiency. Our method combines cluster analysis, ontology development, and rule‐based reasoning to automatically annotate IIoT sensory data. By utilizing data aggregation and filtering mechanisms, which incorporate the DBSCAN (Density‐Based Spatial Clustering of Applications with Noise) algorithm and a rule engine, we significantly reduce the data volume required for annotation. The Semantic Web Rule Language aids in naming new concepts and properties identified through clustering, contributing further to the automation of data processing. Experimental results, using public datasets, validate the effectiveness of our method, showing a reduction in data volume by about 20% and underscoring its potential in enhancing industrial systems' automation and overall efficiency. [ABSTRACT FROM AUTHOR]

The pace of technological development, including smart factories within Industry 4.0/5.0, means that the vagaries of supply chains observed previously cannot be repeated. The automation and computerization of supply chains, asset tracking, simulation, and the prediction of disruption through artificial intelligence (AI) are becoming a matter of course. In selected countries, this will be facilitated by sixth-generation mobile networks planned for full deployment in 2030. The 6G-based intelligent supply chain management within the Industry 4.0/5.0 paradigm will ensure not only greater fluidity of supply, but also faster response to changes in market availability or prices, allowing substitutes to be found and taken into account in the production process and its logistical provisioning. The article outlines key research and development trends in this area and identifies priority development directions, taking into account the advantages and opportunities offered by the Industrial Internet of Things (IIoT) and machine learning (ML). The emergence of 6G technology will transform the supply chain with unprecedented speed, connectivity, and efficiency. This technology will improve visibility, automation, and collaboration while supporting sustainable and safe operations. As a result, companies will be able to design, plan, and operate their supply chains with greater precision, flexibility, and responsiveness, ultimately leading to a more robust and agile supply chain ecosystem. [ABSTRACT FROM AUTHOR]

Energy consumption optimization is crucial for improving the quality of application services in the industrial Internet of Things (IIoT) environment. Traditional optimization methods often adopt blind search strategies, which leads to a significant waste of computing resources and low efficiency. This paper proposes a Hybrid Cat Swarm Optimization and Monarch Butterfly Optimization (HCSMBO), which combines the advantages of Cat Swarm Optimization (CSO) and Monarch Butterfly Optimization (MBO) to solve this problem. By utilizing the characteristics of swarm intelligence, including strong spatial search ability, high stability, and fast convergence, HCSMBO focuses on detailed optimization of the coverage, efficiency, and energy balance coefficient of Support Vector Machine (SVM) networks. By adjusting the weight factors of the fitness function, this algorithm can effectively find the global optimal solution. The experimental results confirm that HCSMBO significantly improves the intelligence, accuracy, and stability of task scheduling in resource management configuration, while achieving lower energy consumption. The research results of this paper are of great significance for improving the service quality of IIoT applications, meeting personalized business needs, and improving resource utilization and execution efficiency. In addition, they also provide new ideas and methods for energy consumption optimization research in the future IIoT.

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion, a series of discussions on network security issues are carried out based on game theory. From the perspective of the life cycle of network vulnerabilities, mining and repairing vulnerabilities are analyzed by applying evolutionary game theory. The evolution process of knowledge sharing among white hats under various conditions is simulated, and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed. On this basis, the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm (WCA) to produce better replacement individuals with greater probability from the perspective of both attack and defense. Through the simulation experiment, it is found that the convergence speed of the probability (X) of white Hat 1 choosing the knowledge sharing policy is related to the probability (x0) of white Hat 2 choosing the knowledge sharing policy initially, and the probability (y0) of white hat 2 choosing the knowledge sharing policy initially. When y0 ​= ​0.9, X converges rapidly in a relatively short time. When y0 is constant and x0 is small, the probability curve of the “cooperative development” strategy converges to 0. It is concluded that the higher the trust among the white hat members in the temporary team, the stronger their willingness to share knowledge, which is conducive to the mining of loopholes in the system. The greater the probability of a hacker attacking the vulnerability before it is fully disclosed, the lower the willingness of manufacturers to choose the ''cooperative development'' of vulnerability patches. Applying the improved wolf colony-co-evolution algorithm can obtain the equilibrium solution of the ''attack and defense game model'', and allocate the security protection resources according to the importance of nodes. This study can provide an effective solution to protect the network security for digital twins in the industry.

In make-to-order manufacturing workshops, accurate prediction value of production progress (PP) is a significant reference index for dynamic optimisation of production process and on-time delivery of production orders. The implementation of big data and Industrial Internet of Things (IIoT) in manufacturing workshops makes it possible to obtain large amounts of production data which can affect PP. However, the particularities of massive historical order data are not fully excavated and the amount of target order data is insufficient to support the training of high-precision prediction model, which will result in bad training approximation and generalisation. To overcome these shortcomings, a PP prediction approach consisting of two models with transfer learning (TL) is proposed. TL can avoid the training of PP prediction model from scratch every time. Consequently, computational efficiency can be greatly improved. A convolutional neural network (CNN) model with TL is devised to excavate the comprehensive features from historical and current orders. Additionally, a long short-term memory network (LSTM) model with TL is constructed to fit the nonlinear relation of the features provided by CNN-TL model for PP prediction. In order to validate the performance of the proposed PP prediction approach, comparative experiments of eight algorithms are conducted in an IIoT-enabled manufacturing workshop. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) provides a foundation for the development of emerging digital servitization paradigm in smart manufacturing. The deep integration of massive heterogeneous IIOT data plays a critical role in realising manufacturing digital servitization. However, there is a knowledge gap between different manufacturing fields, which brings a challenge for efficient integration and leverage of industrial big data. For this purpose, a Framework of Manufacturing Knowledge Graph (FMKG) is proposed, which is used to extracts industry knowledge triples from multi-source heterogeneous data to integrate domain knowledge. Also, an attention-based graph embedding model (ABGE) is proposed to discover and complement the implicit missing relationships in the knowledge graph to obtain a complete industrial knowledge graph. The effectiveness of the ABGE model has been verified on several knowledge graph data sets. And an aerospace enterprise production process was taken as an example to establish a product quality knowledge graph, which proved the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Can automatically authored videos of industrial operators help other operators to learn procedural tasks? This question is relevant to the advent of the industrial internet of things (IIoT) and Industry 4.0, where smart machines can help human operators rather than replacing them in order to benefit from the best of humans and machines. This study considers an industrial ecosystem where procedural knowledge (PK) is quickly and effectively transferred from one operator to another. Assembly tasks are procedural in nature and present a certain complexity that still does not allow machines and their sensors to capture all the details of the operations. Especially if the assembly operation is adaptive and not fixed in terms of assembly sequence plan. In order to help the operators, videos of other operators executing the complex procedural tasks can be automatically recorded and authored from machines. This study shows by means of statistical design and analysis of experiments that expert aid can reduce the assembly time of an untrained operator, whereas automatically authored video aids can transfer PK but producing an opposite effect on the assembly time. Therefore, hybrid training methods are still necessary and trade-offs have to be considered. Managerial insights from the results suggest an unneglectable impact of the choice to digitise industrial operations too early. The experimental studies presented can act as guidelines for the correct statistical testing of innovative solutions in industry. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects objects that sense, communicate, and interact to achieve shared goals, and its integration with manufacturing has led to Industry 4.0. Security is crucial for user acceptance, involving data confidentiality, integrity, authentication, identity management, privacy, and trust among devices. Trust management is vital in IoT and IIoT for reliable services, accurate data collection, device authentication, and secure decision-making. This paper surveys blockchain-based trust mechanisms in IoT and IIoT, categorizing them into four types: basic trust frameworks, enhanced trust interaction, trust data management, and trust-based transaction validation. We analyze these mechanisms to identify their strengths, weaknesses, and current challenges. Blockchain shows promise in improving trust management by decentralizing trust, ensuring transparency, and enhancing security. However, challenges like scalability, energy consumption, and integration with existing IoT infrastructure remain. This survey aims to guide future research by highlighting these issues and proposing potential solutions, contributing to the development of blockchain-based trust management systems in IoT and IIoT. [ABSTRACT FROM AUTHOR]

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models. [ABSTRACT FROM AUTHOR]

As an important cornerstone of the fourth industrial revolution, the industrial Internet transforms isolated industrial systems into connected networks and is an important expansion direction for digital industrialization. Anomaly detection in the industrial Internet of things environment is of great significance for automated decision-making. To address the problem that most existing methods fail to effectively consider the complex unknown topological relationships between sensors and the multi-scale patterns inherent in the industrial IoT temporal data, an unsupervised anomaly detection method MSTSAD with multi-scale spatiotemporal feature fusion for industrial IoT temporal data is proposed, which firstly constructs a novel bi-directional spatiotemporal feature extraction module to sequentially captures the correlation and bidirec- tional dependency between multiple time series. Secondly, a multi- scale gated temporal convolutional neural network is designed to adaptively extract multi-scale temporal features of time series, and a dual affine projection is introduced to realize the cross- fusion of multi-scale temporal features and spatiotemporal features to enhance the feature extraction of the model on the original data. Finally, a variational self-encoder combined with adversarial training is proposed to amplify the reconstruction error of anomalies and enhance the anti-interference ability of the model to train data noise, which enhances the ability of model to discriminate anomalous data. Experiments are conducted on four publicly available datasets, GPW, ECG5000, Occupancy and SWaT, in comparison with five state-of-the-art methods, and the experimental results show that F1 scores of MSTSAD are enhanced by 0.015-0.047. [ABSTRACT FROM AUTHOR]

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment, but has also posed significant challenges to network security, especially physical layer security, which is crucial for the reliable transmission of messages among interconnected devices and sensors. Focusing on enhancing the physical layer security in IIoT communication, this paper proposes a mechanism for the Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate devices is known, while the eavesdropper devices have a random distribution of locations, forming the SSOP model related to the device locations from the perspective of insecure regions (ISRs) and secure regions (SRs), and the closed-form expression for its upper bound is derived. Subsequently, under the constraint of the SSOP conditions, we establish an optimization model with the objective of system throughput maximization based on the Spatial Secrecy Outage Probability (SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of the system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in MATLAB, we verified the accuracy of the definition and derived the upper bound of the SSOP. The experimental results also show that the SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that, when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. This performance is crucial for understanding and optimizing the security performance of IIoT communication systems. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings. [ABSTRACT FROM AUTHOR]

The emerging tribovoltaic nanogenerators (TVNGs) has garnered considerable attention in recent years owing to its higher power density, but it is still limited by unstable output, mismatched impedance, and lower charging speed. Herein, a power‐managed GaN‐based TVNG is first proposed and a self‐powered wireless temperature monitoring system (SWTMS) is developed. The energy backflow from transition capacitor to TVNG is effectively suppressed by applying a high‐voltage‐resistant diode. Benefiting from the combination of the maximum power point tracking (MPPT and the buck converter, an autonomous power management unit (PMU) is designed. The internal impedance of TVNG with PMU decreases by ten times and the charging speed of capacitor increases by 7.18 times compared to those without PMU. Moreover, 100 LEDs and six parallel thermohygrometers can be constantly powered. The wireless temperature monitoring in industrial environments without external power supply is demonstrated through the developed SWTMS. This work proposes an effective power management strategy for TVNG with high output voltage, which exhibits great potential to promote the development of self‐powered sensors and Industrial Internet of Things (IIoT). [ABSTRACT FROM AUTHOR]

This paper delves into the realm of cryptographic analysis by employing mixed-integer linear programming (MILP), a powerful tool for automated cryptanalysis. Building on this foundation, we apply the division property method alongside MILP to conduct a comprehensive cryptanalysis of the IIoTBC (industrial Internet of Things block cipher) algorithm, a critical cipher in the security landscape of industrial IoT systems. Our investigation into IIoTBC System A has led to identifying a 14-round integral distinguisher, further extended to a 22-round key recovery. This significant finding underscores the cipher's susceptibility to sophisticated cryptanalytic attacks and demonstrates the profound impact of combining the division property method with MILP in revealing hidden cipher weaknesses. In the case of IIoTBC System B, our innovative approach has uncovered a full-round distinguisher. We provide theoretical validation for this distinguisher and uncover a pivotal structural issue in the System B algorithm, specifically the non-diffusion of its third branch. This discovery sheds light on inherent security challenges within System B and points to areas for potential enhancement in its design. Our research, through its methodical examination and analysis of the IIoTBC algorithm, contributes substantially to the field of cryptographic security, especially concerning industrial IoT applications. By uncovering and analyzing the vulnerabilities within IIoTBC, we enhance the understanding of cipher robustness and pave the way for advancements in securing industrial IoT communications. [ABSTRACT FROM AUTHOR]