Industrial Internet of Things

The Internet of Things (IoT) is an essential component of the digital age, particularly in ensuring reliable and efficient operations as well as the timely and precise recognition of anomalies within IoT systems. However, anomaly detection in time-series data—especially data collected by edge devices—poses several challenges, including concerns over data privacy and communication overhead. To address these issues, this research proposes a novel deep learning (DL)-based anomaly detection model capable of being trained in real-time on edge devices within an IoT environment. The framework ensures user privacy by enabling distributed edge devices to cooperatively train an anomaly recognition system. This study introduces a new, Intelligent Shark Smell Tuned Deep Isolation Forest (ISS-DIF), which effectively detects anomalies and identifies outliers in industrial IoT sensor data with high accuracy. The hybrid model isolates anomalies rather than profiling normal data, making it particularly suitable for identifying rare anomalies within large datasets. Industrial data are collected from real-world manufacturing environments using IoT edge devices. Following data collection, median filtering is applied to reduce noise, and min-max scaling is employed for data normalization. The ISS component allows for fine-tuning the hyperparameters of the Deep Isolation Forest (DIF) to optimize detection performance. The DIF model is tailored to enhance anomaly detection capabilities in sensor data from industrial IoT applications. For validation, 80% of the dataset was used for training, while the remaining 20% served as the test set. The results demonstrate that the proposed ISS-DIF framework achieved superior training accuracy of 99.30%, with precision of 97.89%, recall of 98.76%, and an F1-score of 97.21%, outperforming the testing metrics. This approach integrates real-time anomaly detection with the processing capabilities of edge devices, thereby improving IoT data analytics and providing a scalable, efficient solution that preserves privacy in irregularity recognition within IoT environments.

Smart contract over the blockchain accelerates the deployment of decentralized Industrial Internet of Things (IIoT) applications. However, the usability of smart contracts is constrained to the data on the blockchain, and has no direct access to the off-chain environment. Consequently, smart contract cannot directly serve as a carrier for the IIoT applications that rely on intensive data exchange and task computation. To cope with this issue, we first propose an oracle-aided IIoT blockchain (OIB) system to facilitate the deployment of smart contract based IIoT applications. Specifically, a distributed oracle network is employed to not only bridge the off-chain environment and smart contracts, but also extend the computing capability of smart contracts. Then, we study important properties of the OIB system, i.e., liveness, safety, and fairness. In particular, we propose a new challenge of fairness to guarantee that each oracle can reap positive utility (i.e., individual rationality), and a new challenge of liveness to meet delay constraints of oracle services (i.e., delay-sensitivity). To address these two challenges, we develop an auction based incentive mechanism for the data-feeding oracles to achieve individual rationality as well as incentive compatibility, and a delay-sensitive matching based incentive mechanism for the computing oracles under specific delay constraints. Simulation results corroborate that the proposed auction based incentive mechanism guarantees not only truthfulness but also individual rationality, and show that the matching based incentive mechanism achieves smaller delay than the random selection scheme used in the Decentralized Oracle Service (DOS) network.

The vision of Industry 4.0, otherwise known as the fourth industrial revolution, is the integration of massively deployed smart computing and network technologies in industrial production and manufacturing settings for the purposes of automation, reliability, and control, implicating the development of an Industrial Internet of Things (I-IoT). Specifically, I-IoT is devoted to adopting the Internet of Things (IoT) to enable the interconnection of anything, anywhere, and at anytime in the manufacturing system context to improve the productivity, efficiency, safety and intelligence. As an emerging technology, I-IoT has distinct properties and requirements that distinguish it from consumer IoT, including the unique types of smart devices incorporated, network technologies and quality of service requirements, and strict needs of command and control. To more clearly understand the complexities of I-IoT and its distinct needs, and to present a unified assessment of the technology from a systems perspective, in this paper we comprehensively survey the body of existing research on I-IoT. Particularly, we first present the I-IoT architecture, I-IoT applications (i.e., factory automation (FA) and process automation (PA)) and their characteristics. We then consider existing research efforts from the three key systems aspects of control, networking and computing. Regarding control, we first categorize industrial control systems and then present recent and relevant research efforts. Next, considering networking, we propose a three-dimensional framework to explore the existing research space, and investigate the adoption of some representative networking technologies, including 5G, machine-to-machine (M2M) communication, and software defined networking (SDN). Similarly, concerning computing, we again propose a second three-dimensional framework that explores the problem space of computing in I-IoT, and investigate the cloud, edge, and hybrid cloud and edge computing platforms. Finally, we outline particular challenges and future research needs in control, networking, and computing systems, as well as for the adoption of machine learning, in an I-IoT context.

Internet of Things and cloud services are one of main enablers in fourth industrial revolution. Real-time industrial systems have high availability requirements of 99.9% to 99.999% whereas architectures built on regional cloud services and IoT do not provide similar guarantees or Service Level Agreement. These differences of QoS and SLA availability between Operational Technology and Information Technology has become a main challenge in adoption of Industrial Internet of Things (IIoT) for real-time applications.This work presents an approach to find end-to-end QoS and availability for an IIoT architecture. Device-to-cloud, cloud-to-cloud and inside-cloud experiments have been performed over eight weeks where each experiment have more then four million QoS measurements. Our availability analysis shows that a remote IoT connected to a less busy cloud region gives higher availability as compared to an IoT device inside a busy cloud region. IIoT and regional cloud services provide good QoS with 99% to 99.9% availability for 1sec soft real-time requirements. In 100ms applications, more efforts are required to achieve higher then 95% availability and design industrial SLA. IIoT applications with 10sec latency like machine learning models can get 99.9% availability with cloud. Availability loss due to communication is almost 1% for 100ms applications. These results also provide requirements and future work of industrial edge computing for IIoT on real-time cloud.

The proliferation of the Internet of things (IoT) technologies has opened new avenues for technological advancements in various aspects of life. At the same time, the immense collection of competing IoT solutions has created new challenges in security and standards gaps that have attracted numerous research efforts. The majority of these research efforts are centered on the challenges of IoT in general. However, there is a growing separation between the two subcategories of IoT, the Consumer IoT (CIoT) and Industrial IoT (IIoT), in terms of application, growth, scale, requirements, and challenges. The ability to distinguish between the two is critical for developing appropriate solutions, infrastructure, and standardization. This would pave the way to creating reliable, scalable, and secure CIoT and IIoT applications. This study highlights the differences between IoT types in terms of purpose, hardware needs, communication protocol requirements, and security concerns. We also investigate existing IIoT-related solutions to provide readers with clear and concise information to help them make more informed decisions about IoT solutions.

The manufacturing industry is benefiting a lot since the Industrial Internet of Things (IIoT) deploys sensors and actuators to calibrate, audit, and analyze data to enhance efficiency and profitability, thereby automating systems in the industrial sector. The exponential growth of smart devices in the industrial sector requires real‐time connectivity, low latency, nearby storage, secure communication, and reliability. To meet this demand, fog computing is effectively used in IIoT applications. Fog computing solves most of the problems in the fourth industrial revolution, which is the interface between end‐user devices and cloud/web services. Fog also allows for local processing with minimal acceptable latency. This article focuses on how fog computing becomes part of IIoT and also explores the architecture of fog‐assisted IIoT applications. The taxonomy is built by classifying the existing literature to reveal the salient features of fog computing in IIoT. This article also discusses IIoT security solutions as well as research problems of fog‐assisted IIoT. This article also outlines the research challenges of fog‐assisted IIoT and also discusses the security solutions of IIoT.

The Internet of Things (IoT) is revolutionizing the industry. Powered by pervasive embedded devices, the Industrial IoT (IIoT) provides a unique solution for retrieving and analyzing data near the source in real-time. Many emerging techniques, such as Tiny Machine Learning (TinyML) and Complex Event Processing (CEP) , are actively being developed to support decision making at the edge, shifting the paradigm from centralized processing to distributed computing. However, distributed computing presents management challenges, as IoT devices are diverse and constrained, and their number is growing exponentially. The situation is even more challenging when various on-device applications (so-called artifacts) are deployed across decentralized IoT networks. Questions to be addressed include how to discover an appropriate function, whether that function can be executed on a certain device, and how to orchestrate a cross-platform service. To tackle these challenges, we propose an approach for the scalable management of on-device applications among distributed IoT devices. By leveraging the W3C Web of Things (WoT) , the capabilities of each IoT device, or more precisely, its interaction patterns, can be semantically expressed in a Thing Description (TD) . In addition, we introduce semantic modeling of on-device applications to supplement an TD with additional information regarding applications on the device. Specifically, we demonstrate two examples of semantic modeling: neural networks (NN) and CEP rules. The ontologies are evaluated by answering a set of competency questions. By hosting the enriched semantic knowledge of the entire IoT system in a Knowledge Graph (KG) , we can discover and interoperate edge devices and artifacts across the decentralized network. This can reduce fragmentation and increase the reusability of IoT components. We demonstrate the feasibility of our concept on an industrial workstation consisting of a conveyor belt and several IoT devices. Finally, the requirements for constructing an IoT semantic management system are discussed.

The proliferation of Internet of Things (IoT) devices has significantly expanded the attack surface for ransomware, with the healthcare sector-particularly Internet of Medical Things (IoMT) devices-emerging as a critical target. Traditional cybersecurity solutions often fail to address the unique vulnerabilities and operational constraints of eHealth IoT environments, where ransomware attacks can result in severe consequences, including patient safety risks and operational disruptions. This paper introduces a novel hybrid detection framework that combines Long Short-Term Memory (LSTM) networks with Autoencoders to identify ransomware activity in IoMT devices. By leveraging the sequential data analysis capabilities of LSTMs and the anomaly detection strengths of Autoencoders, the proposed model effectively detects subtle and sophisticated ransomware behaviors while maintaining computational efficiency suitable for real-world deployment. The approach incorporates adaptive thresholding to accommodate dynamic IoMT environments and employs continuous learning to adapt to evolving ransomware tactics. Experimental results demonstrate that the hybrid model outperforms conventional detection methods in both accuracy and sensitivity, achieving robust real-time detection with minimal false positives. These findings lay the groundwork for future research into resilient, AI-driven security solutions for industrial and medical IoT applications

Internet of Things (IoT), together with fifth-generation (5G) mobile systems and related enabling communication technologies, constantly contributing for the enhancement in various industrial applications and driving towards the vision of complete industrial automation. In this context, both Blockchain and Edge computing are considered as promising technologies to fulfill the vision and requirements for the future Industry 4.0. The integration of these technologies together with massive scale Industrial IoT (IIoT) applications would be vital in addressing several key challenges related to e.g. latency, scalability and security of data processing and sharing, among others. However, such complex IIoT systems are constantly exposed to security threats from various sources. This paper focuses on identifying the major security challenges in the IIoT blockchain-edge related networks. In addition, some potential security solutions are presented in order to overcome these challenges.

Federated learning for green and sustainable 6G IIoT applications

Internet of things (IoT) is creating new opportunities for developing innovative applications by leveraging on existing and new technologies. In recent years, a variety of consumer and industrial IoT applications have been developed and deployed. Despite much progress, developing IoT applications is still a complex, time‐consuming and challenging activity. This is because IoT systems involve a wide range of hardware and software components, depending on a variety of communication and distributed system technologies. Many IoT application frameworks of varying approaches have been developed to manage the complexities of developing IoT applications. However, there remains a paucity of surveys on these IoT application development frameworks. This study presents a comprehensive review and a comparative analysis of existing IoT application development frameworks and toolkits, illustrating their strengths and weaknesses. This study will assist in finding the most appropriate IoT application development paradigm for the desired IoT application. Finally, future research directions are highlighted to improve existing and future frameworks and toolkits for IoT applications.

SummaryThe evolution of Internet of Things (IoT) has led to the development of Industrial Internet of Things (IIoT). IIoT is one the widely applied areas to facilitate people in the manufacturing world. The adoption of IIoT automates sensing, capturing, communicating, and processing in real time. To understand how rapidly IoT and IIoT are growing, this article examines the emergence of 5G‐enabled IIoT, current research trends in IIoT, key milestones achieved in IIoT, and IoT applications specific to 5G‐enabled IIoT. The paper presents the state‐of‐the‐art in networking layered framework of IIoT and comparing relationships of technologies of cloud computing as well as edge computing paradigms. We also explored the type of security attacks and their preventive measures in an IIoT‐driven 5G technology. We have also highlighted the revolution of IIoT‐driven 5G framework which satisfies the demands of IIoT applications.

Recently, the Internet of Things (IoT) has been used in various applications such as manufacturing, transportation, agriculture, and healthcare that can enhance efficiency and productivity via an intelligent management console remotely. With the increased use of Industrial IoT (IIoT) applications, the risk of brutal cyber-attacks also increased. This leads researchers worldwide to work on developing effective Intrusion Detection Systems (IDS) for IoT infrastructure against any malicious activities. Therefore, this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure. A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is introduced. The data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT applications. Correspondingly, this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT network. This is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient fashion. Therefore, several machine learning algorithms such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (NB), Classification and Regression Tree (CART), Random Forest (RF), and AdaBoost (AB) are used for the detection intent on thirteen different intrusion datasets. Several performance metrics like accuracy, precision, recall, and F1-score are used to estimate the proposed framework. The experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97, 1.00, 0.99, 0.99, 1.00, 1.00, and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed datasets. In addition, the proposed work accomplishes high performance compared to other recent related works in terms of different security and detection evaluation parameters.

Intelligent manufacturing is the future development direction of manufacturing industry. There are still some problems lurk in the traditional mode of production, such as “information island” and “network security”, which result in low productivity but high-cost production, and information leakage. With application of the new generation information technology in manufacturing field is expanded, the deep integration of information technology and manufacturing industry promotes the development of production towards intellectualization, networking, and service. Single mode of production is gradually replaced by the way of large-scale collaborative production, so the traditional industrial internet of things (IoT) and manufacturing architecture are not suitable for the needs of actual production any more. For instance, the great challenges of compatibility and coordination of interface protocols, huge data, cost, and safety. In order to solve these problems, a new model of industrial IoT with security mechanism used in intelligent workshop is proposed. Firstly, this article analyzes the current situation of manufacturing industry and information technology, a secure transmission model of industrial IoT was designed based on the new generation information technology. Secondly, combined with the structure of the IoT and the real production demands, a six-tier industrial IoT architecture based on the new generation information technology is proposed. Finally, the three dimensions of the intelligent manufacturing model are expanded according to the architecture of the IoT. Not only a six-tier intelligent manufacturing system framework based on the new generation of information technology is obtained, but also the network security mechanism is introduced. On one hand, it realizes the coordinated operation of industrial chain, supply chain, and value chain of production process, on the other hand, it also improves the networking, intellectualization, and service level of industrial production. What is more the system can also provide the reference model for the various application of IoT.

The modern developments in the Internet of Things (IoT) have been producing an increasing number of interconnected devices, allowing a variety of elegant applications. These IoT devices generate huge information that require smart data study and processing methods, for instance, deep learning (DL). Remarkably, the DL algorithms, when amalgamated with the industrial IoT (IIoT), can facilitate a mixture of applications, for example, elegant assembling, effective manufacturing, efficient networking, accident identifying, and prevention of errors. Also, IIoT can change the way in which industries use to produce autonomous self-healing machines and improve stocks using machine learning (ML). Alternatively, ML carries hidden imminence in IoT data for quick, automated answers, and enhanced assessment making. ML for IoT can be used to venture future trends, become aware of anomalies, and augment intelligence by consuming images, videos, and audios. Various industries currently use ML in IoT to keep up acceptable standards as well as maintain predictable things in terms of benefits and profits at different times. Banks and other businesses in the financial industry make use of ML technology for two main reasons: to recognise significant imminence in data and to avoid scams. Data mining can also be used to make out clients with high-risk outlines or use cyber-surveillance to locate notifying indications of fraud. IIoT has been a means to digital transformation in manufacturing. It takes up a network of sensors to implement a serious building of data and brings cloud software into action to use this data in expensive approaches about the effectiveness of manufacturing procedures. Machines can gain knowledge of the data and algorithms that are accountable for origin faults in the system to identify the errors before those can do damage. Manufacturers can make use of ML to get better safeguarding processes and facilitate them to construct real-time, smart judgments derived from data. So the main aim of this chapter is to focus on data acquisition, integration, and predictive control in the industry using ML for IIoTs for real-time collection and analysis of thousands of measurement points, an early detection of equipment failures and process disturbances, detection of new types of anomalies as they occur for the first time, and Web-based UI for handling alerts.