Distributed Knowledge Inference Framework for Intelligent Fault Diagnosis in IIoT Systems

The dependability of the Industrial Internet of Things (IIoT) system can directly affect the production efficiency of the whole system. In this paper, we design a novel production-centric IIoT system with a digital twin network. The operation process of the IIoT system is proposed to calculate a suitable control command and to make an optimal production scheme and AGV dispatch of the whole factory. Moreover, resilience is used to introduce the system dependability of the IIoT network. The simulation results show that the proposed production-centric IIoT system with a digital twin network can achieve improved dependability for the IIoT system compared with the traditional scheme.

Industrial Internet of Things (IIoT) systems play a pivotal role in enabling real-time monitoring and analysis of equipment to prevent breakdowns and optimize performance. Predictive maintenance stands as a critical component of IIoT systems, allowing industries to anticipate equipment failures, minimize downtime, and enhance overall equipment effectiveness. This paper investigates the potential of Long Range (LoRa) technology in predictive maintenance applications within IIoT systems. Specifically, the study delves into the utilization of LoRa technology to bolster predictive maintenance capabilities, with a focus on a live prediction analysis demonstration conducted on an industrial robotic arm. The research presents a detailed test bed setup for conducting predictive maintenance, which involves capturing data from the robot, transmitting it to a LoRaWAN gateway, enabling prediction of normal or abnormal behavior in the robotic arm. This study provides insights into the efficacy of LoRa technology in enhancing predictive maintenance capabilities within IIoT systems.

Global CO2 emissions reduction requires industries to manage and understand their CO2 emission levels in real-time. This paper examines the Industrial Internet of Things (IIoT) for real-time monitoring, measurement, and visualization of reducing CO2 emissions in industrial and environmental domains. Methodology: The methodology consists of a literature review based on peer-reviewed publications and use cases to explore the current state and practical implications. Furthermore, a technical analysis of IIoT systems, CO2 sensors, and data processing techniques is also identified. Results: IIoT systems can support CO2 emission monitoring and accuracy optimization in industrial domains by combining CO2 sensors, wireless communication, and data fusion techniques. In addition, machine learning and artificial intelligence can be used to reduce anomalies in CO2 sensor readings and predictive maintenance of systems. Challenges: Challenges include interoperability, data security and system scalability. To resolve these issues standardized communication protocols, data security methods and implementation barriers should be improved. Future Directions: To enhance data processing and security features, future work should focus on integrating edge computing, artificial intelligence, machine learning, and blockchain techniques. In addition, data visualizations and cost-effective solutions should also be in focus, to provide more adoptable IIoT systems in industrial domains. Conclusion: As IIoT systems and CO2 sensor technologies evolve, IIoT systems can contribute significantly to increasing global air quality and CO2 emission control in industry, agricultural, and urban areas.

The architecture of the Industrial Internet of Things (IIoT) system assumes the presence of a router as the main subsystem, which will allow connecting the subsystems into a single network and connecting them to the equipment of the Internet service provider. Failure of the router can lead to downtime of industrial equipment and the whole workshop at the industrial enterprise, and to the failure of the IIoT system as a whole. Control processing units of industrial routers (RCPU) are complex microprocessor systems (MS). Violation of their operability can be caused by both hardware (HW) and software (SW) failures. Therefore, to increase the reliability of RCPU it is reasonable to use such methods of reliability assurance as HW redundancy (duplication of processors with comparison of results), as well as the use of modern means of control and diagnostics. However, the number of HW and SW failures in RCPUs, is still quite high. In this connection, the solution of issues related to the development of methods for ensuring high reliability of RCPU is of great urgency. This paper proposes a method of ensuring the reliability of RCPU using SW diversity on the basis of priority series. The method let to choose the variant of redundancy of HW and the number of versions of SW as diversity in RCPU.

Through interconnecting machines, sensors, and devices, the Industrial Internet of Things (IIoT) has revolutionized industrial processes by allowing real-time data analysis and decision-making. The implementation of cloud, edge, and fog computing models has followed IIoT development, bringing unique security and privacy challenges in their wake. In this all-encompassing report, the potential risks, complications, and apprehensions of these models are analyzed with respect to IIoT integration. Examining cloud, edge, and fog computing models distinguishes and assesses their benefits when applied to IIoT. The paper analyzes the strengths offered by each model and addresses the risks associated with security and privacy, such as data infringement, malware, and denial-ofservice attacks. IIoT systems, in particular, pose significant challenges in providing protection. Cloud, edge, and fog computing models provide ample benefits but IIoT systems utilizing these models must have strong security and privacy policies in place. This paper highlights specific technical and organizational strategies to tackle potential challenges and risks. By effectively implementing these strategies, organizations can tap into the advantages of these models while ensuring the safety of their IIoT systems. Informed decisions can be made, and IIoT deployments can be safeguarded with the aid of the valuable resource given in this paper, which offers comprehensive analysis and recommendations. For those in the field of IIoT security, including practitioners and researchers, this paper is of great value.

Industrial Internet of Things (IIoT) is a fusion of industrial automation systems and IoT systems. It features comprehensive sensing, interconnected transmission, intelligent processing, self-organization and self-maintenance. Its applications span intelligent transportation, smart factories, and intelligence. Many areas such as power grid and intelligent environment detection. With the widespread application of IIoT technology, the cyber security threats to industrial IoT systems are increasing day by day, and information security issues have become a major challenge in the development process. In order to protect the industrial IoT system from network attacks, this paper aims to study the industrial IoT information security protection technology, and the typical architecture of industrial Internet of things system, and analyzes the network security threats faced by industrial Internet of things system according to the different levels of the architecture, and designs the security protection strategies applied to different levels of structures based on the specific means of network attack.

This article addresses cyber security risk assessment in industrial internet of things (IIoT) networks, and particularly the continuous risk assessment (CRA) process, which assumes real-time, dynamic risk evaluation based on the run-time data. IIoT cyber security risks, threats, and attacks are briefly presented. Requirements for cyber security risk assessment of industrial control systems as well as applicability of machine learning for that purpose are considered. The architectural view of the CRA process in the IIoT environment is presented and discussed. Possibilities of deep learning approaches to achieve CRA in IIoT systems are explored. Deep learning can be integrated into edge-computing-based systems and used for feature extraction and risk classification from massive raw data. Several research works are presented and briefly discussed. The article ends with emphasizing the future research directions and concluding remarks.

The industry has undergone a digital transformation facilitated by the Industrial Internet of Things (IIoT) technology, ushering in the era of Industry 4.0. However, the widespread use of IIoT devices, such as sensors, robots, and other IIoT technologies, has made IIoT systems and associated services vulnerable to a range of network-based attacks, which can impede their performance and disrupt operations. Moreover, the automation process is essential for IoT-based industries to meet future demands. The concept of a zero-touch network has recently evolved to coordinate and manage network resources automatically. Machine learning (ML) plays a critical role in its architecture due to its ability to facilitate a close-loop automation process. With ML, analytical tasks and real-time predictions can be achieved effectively to build smart applications for the early detection of cyberattacks in IIoT systems. The ML components, such as preprocessing, training, and testing, can be subdivided into microservices to improve service while allowing interaction with edge and cloud services. In this article, we propose an ensemble learning-based intrusion detection system (IDS) for a zero-touch network automation process by leveraging ML and microservice technology to improve the trustworthiness of IIoT systems. More specifically, we use the feature selection technique to select significant features and pass them to different models, then blend their predictions based on a stacked ensemble learning approach. Experiments are conducted to evaluate the performance of the proposed framework compared to existing studies.

Industrial Internet of Things (IIoT) envisions the tight coupling of numerous critical industrial manufacturing subsystems, such as control, networking, and computing through the ubiquitous Internet of Things technologies. Nonetheless, such interconnectivity poses significant challenges to the successful management and operation of massively distributed industrial manufacturing systems. Without carefully integrated system design, the nonoptimal management and operation of highly intertwined subsystems can lead to the loss of productivity and ultimately the value of factories and plants. To address this issue, in this article, we conduct the integrated design that is capable of simultaneously configuring both control and networking subsystems in IIoT with consideration for their inherent interdependencies. We first analyze the performance of the dynamic backoff exponential (BE) in IEEE 802.15.4 carrier-sense multiple access (CSMA) and show the performance improvement of dynamic BE. We then design a model-free reinforcement learning algorithm to configure the control and networking subsystems automatically via systematic trial and error, as it is impractical to build a model for a highly intertwined complex IIoT system. Considering the time-sensitive characteristics of IIoT systems, we design priority-aware policies based on importance among networking traffic (i.e., sensing traffic and actuation traffic) to improve the convergence speed. The experimental results demonstrate that our priority-aware reinforcement-learning-based integrated design can successfully reconfigure the complex and highly intertwined IIoT system at runtime with a minimal convergence time. Besides, our approach reduces convergence time by 37.5%, and energy consumption by 9.2%, compared to the standard reinforcement learning approach.

Industrial IoT (IIoT) is critical for industrial infrastructure modernization and digitalization. Therefore, it is of utmost importance to provide adequate protection of the IIoT system. A modern IIoT system usually consists of a large number of devices that are deployed in multiple locations and owned/managed by different entities who do not fully trust each other. These features make it harder to manage the system in a coherent manner and utilize existing security mechanisms to offer adequate protection. The emerging blockchain technology provides a powerful tool for IIoT system management and protection because the IIoT nature of distributed deployment and involvement of multiple stakeholders fits the design philosophy of blockchain well. Most existing blockchain construction mechanisms are not scalable enough and too heavy for an IIoT system. One promising way to overcome these limitations is utilizing hardware based trusted execution environment (TEE) in blockchain construction. However, most of the existing works on this direction do not consider the characteristics of IIoT devices (e.g., fixed functionality and limited supply) and face several limitations when they are applied for IIoT system management and protection, such as high energy consumption, single root-of-trust, and low decentralization level. To mitigate these challenges, we propose a novel field programmable gate array (FPGA) based blockchain system. It leverages the FPGA to build a simple but efficient TEE for IIoT devices, and removes the single root-of-trust by allowing all stakeholders to participate in the management of the devices. The FPGA based blockchain system shifts the computation/storage intensive part of blockchain management to more powerful computers but still involves the IIoT devices in the block construction to achieve a high level of decentralization. We implement the major FPGA components of the design and evaluate the performance of the whole system with a simulation tool to demonstrate its feasibility for IIoT applications.

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

The fast development of the applications of the Industrial Internet of Things (IIoT) requires the real-time object detection which has the capability to work effectively in energy-restrained edgefog conditions. Unlike the former YOLO-based and lightweight detectors and RL-based edgefog offloading mechanisms, the proposed RAEED framework combines lightweight detection and training-free adaptive inference offloading to optimize the accuracy, latency and energy consumption jointly in IIoT settings. The deep learning-based detectors are very accurate and capable of handling resource-intensive IIoT deployments, however, their systems are frequently computation- and powerintensive, which is not feasible in resource-constrained IIoT applications. In this effort, the present work suggests that a resource-conscious energy-efficient detector (RAEED) system is proposed which allocates inference resources between the edge and fog nodes dynamically projected by resource availability and network characteristics. The framework integrates a convolutional backbone which is lightweight together with adaptive offloading strategy to trade-off between detection accuracy, latency, and energy consumption. The applications to the COCO 2017 dataset under constrained deployment conditions indicate that RAEED can have 82.4% mAP, 83.1% precision and 81.7% recall, with a latency of 47.6 ms and with a consumption of 0.91 mJ/frame. These findings indicate that IIoT systems can greatly manage to find and identify objects with a much better energy and resource savings.

The rapid adoption of the Industrial Internet of Things (IIoT) model has exposed methods to inadequate security measures. False data injection attacks (FDIAs) pose a significant security threat in IIoT, as they aim to mislead industrial platforms by manipulating sensor readings. In IIoT, owing to the data attacker’s changeability, the FDIA is the most critical intrusion network. As aggressive devices permitted in the network perform their typical data-collecting tasks, identifying an FDIA becomes a nontrivial effort due to potential attacks. Conventional attack detection models have proven insufficient in addressing FDIAs, and most current countermeasures focus on the need to legalise data, primarily in the context of data clustering services. Presently, deep learning (DL) systems are employed for detecting FDIA in real-time and supply personalised protective measures to the threat. This paper presents an Intelligent Management of False Data Injection Attacks Using Feature Selection and Voting Classifier (IMFDIA-FSVC) technique in IIoT systems. The primary purpose of the IMFDIA-FSVC technique is to develop a model for detecting and mitigating FDIAs to ensure safe and trustworthy operations of IIoT systems. Initially, the data pre-processing stage involves two stages: missing value analysis and normalisation to standardise the input data for an effective study. For an effective feature selection, the IMFDIA-FSVC model utilises a statistical and information-theoretic selection (SITS) technique to select optimal features from the input data. Finally, the classification process is mainly deployed by three models: the temporal convolutional network (TCN), the deep belief network (DBN), and the autoencoder (AE). An ensemble classifier is then performed using the voting classifier. The comparison study of the IMFDIA-FSVC method showed a superior accuracy value of 99.15% compared to existing models on the IIoT and FDIA datasets.

The rapid development of industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this article, we first propose a new paradigm digital twin network to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present asynchronous actor-critic algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.

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.