Industrial Internet Of Things Research Articles

REML: An Efficient Algorithm to Enhance Precision and Reliability of Bearing Fault Detection in Complex Industrial IoT Systems

REML: An Efficient Algorithm to Enhance Precision and Reliability of Bearing Fault Detection in Complex Industrial IoT Systems

Design and analysis of lightweight and robust authentication protocol for securing the resource constrained IIoT environment.

The Internet of Things (IoT) is a vast network of devices, sensors, wearables, or any other object capable of processing, storing, sending, and receiving data over an open network channel. This versatility gives IoT numerous applications, one of them being in the industry, also known as the Industrial Internet of Things (IIoT). As IIoT relies on an open network channel for data sharing, it is vulnerable to numerous threats, including side channels, impersonation attacks, and clock synchronization issues for which device authentication becomes crucial. Researchers occasionally proposed numerous authentication protocols using conventional cryptographic methods, identity-based cryptographic techniques, or certificateless methods; however, these protocols either suffer from modular exponentiation partial private key distribution problems or are completed in four to five round-trips during authentication. Therefore, this article presents an Elliptic Curve Cryptographic (ECC)-based efficient technique that emerges as a significant solution, addressing the certificate revocations, overheads problem, and the partial private key distribution problem of identity-based cryptography, respectively. The security of the proposed ECC-based protocol is of utmost importance in addressing all the known vulnerabilities in IIoT, freeing the industrial system from the urgency and the issue of data breaches. Its potential to instil a sense of security and confidence in IIoT deployment is crucial in improving user trust. Upon comparing the proposed protocol with state-of-the-art schemes, the result demonstrated that the proposed protocol enhanced 51.44% in terms of communication costs and 91.88% in terms of computation costs. So, it is recommended for practical implementation due to its fast and provable secure nature, making the industry feel confident and safe about its implementation.

Efficient Collaborative Learning in the Industrial IoT Using Federated Learning and Adaptive Weighting Based on Shapley Values

Efficient Collaborative Learning in the Industrial IoT Using Federated Learning and Adaptive Weighting Based on Shapley Values

IIoT Implementation to Investigate the Heat Transfer Efficiency of a Concentric Corrugated Pipe Heat Exchanger

ABSTRACTThe Industrial Internet of Things (IIoT) plays a key role in industrial applications and manufacturing processes. Numerous technologies are implementing IIoT to monitor and control manufacturing processes, maintain reliability, and increase productivity. In this study, IIoT was used to demonstrate the heat transfer performance of a double‐pipe heat exchanger at the laboratory scale to provide research guidelines. Two types of inner pipes, smooth and corrugated pipes, were used in the experiment. In the experiment, the hot‐ and cold‐water mass flow rates were 9 and 10 L/m, respectively. The cold‐ and hot‐water temperatures were set at 20°C, 22°C, and 24°C and 35°C, 40°C, and 45°C, respectively. Equipment operation control and reading and recording of the experimental data were performed using PLC and IIoT platforms. The experimental results showed that heat exchange using corrugated pipes was more efficient than smooth pipes when considering the overall heat transfer coefficient, with an effectiveness of approximately 19%. However, corrugated pipes exhibited a 30% higher pressure difference compared to smooth pipes. This reveals that IIoT has been established and can be effectively applied to laboratory thermal solutions, allowing operators to read real‐time data at any time, both inside and outside the work area.

Proactive Security for the Industrial IoT Networks in the AI Era: A Framework for Continuous Threat Exposure Management

This article investigates the convergence of Industrial Internet of Things (IIoT) technologies and Artificial Intelligence (AI), exploring the evolving landscape of AI-powered attacks and the critical need for proactive security measures. To mitigate these risks, this paper proposes a multi-faceted security approach that combines traditional security techniques with AI-driven solutions. Focusing on Continuous Threat Exposure Management (CTEM) and Attack Surface Management (ASM), and emphasizing real-time threat detection, automated response mechanisms, and a robust human-AI collaboration model, the study explores a paradigm shift from reactive to proactive security measures in response to the evolving cyber threats facing modern industrial environments. Through an in-depth analysis of successful security implementations across diverse industrial sectors, this research demonstrates the efficacy of integrating AI-driven security measures with existing infrastructure. By proactively addressing these challenges, organizations can harness the power of AI to safeguard their digital assets and ensure the integrity of their operations.

Fortifying industrial cybersecurity: a novel industrial internet of things architecture enhanced by honeypot integration

The industrial internet of things (IIoT) has significantly transformed the industrial sectors by connecting devices, machines, and systems to enhance automation, efficiency, and decision-making. However, the increased interconnectivity also poses significant security challenges because IIoT devices control critical infrastructures and processes. Our work presents an implementation of a robust industrial cybersecurity strategy with a segmented network architecture, collaborative efforts between information technology (IT) and operational technology (OT) teams for enhanced resilience and effectiveness, and vertical honeypots across all Industry 4.0 levels integrated with Wazuh for log transmission and proactive threat response, alongside Snort intrusion detection system (IDS) monitoring network traffic. Additionally, we reinforce our architecture by Wazuh with Elasticsearch and Kibana as a security information and event management solution, facilitating data analysis and compliance enforcement through custom rulesets and cybersecurity threat intelligence (CTI) integration, with automatic updates for continuous adaptation against emerging threats.

Federated learning at the edge in Industrial Internet of Things: A review

Federated learning at the edge in Industrial Internet of Things: A review

Deep reinforcement learning with dual-Q and Kolmogorov–Arnold Networks for computation offloading in Industrial IoT

Deep reinforcement learning with dual-Q and Kolmogorov–Arnold Networks for computation offloading in Industrial IoT

An NB-IoT Monitoring System for Digital Mobile Radio With Industrial IoT Performance and Reliability Evaluation

An NB-IoT Monitoring System for Digital Mobile Radio With Industrial IoT Performance and Reliability Evaluation

On the performance of Zenoh in Industrial IoT Scenarios

On the performance of Zenoh in Industrial IoT Scenarios

Ambient Backscatter Communication-Assisted Intelligent Resource Management for Green Industrial IoT

Ambient Backscatter Communication-Assisted Intelligent Resource Management for Green Industrial IoT

Advancing Industrial IoT and Industry 4.0 through Digital Twin Technologies: A comprehensive framework for intelligent manufacturing, real-time analytics and predictive maintenance

Advancing Industrial IoT and Industry 4.0 through Digital Twin Technologies: A comprehensive framework for intelligent manufacturing, real-time analytics and predictive maintenance

Mapping of Industrial IoT to IEC 62443 Standards

The increasing adoption of the Industrial Internet of Things (IIoT) has led to significant improvements in operational efficiency but has also brought new challenges for cybersecurity. To address these challenges, a number of standards have been introduced over the years. One of the best-known series of standards for this purpose is ISA/IEC 62443. This paper examines the applicability of the ISA/IEC 62443 series of standards, traditionally used for securing industrial automation and control systems, to the IIoT environment. For each requirement described in the ISA/IEC 62443 standards, relevant research on that subject is reviewed and presented in a table-like manner. Based on this table, areas for future research are identified, including system hardening, asset inventory, safety instrumented system isolation, risk assessment methodologies, change management systems, data storage security, and incident response procedures. The focus on future improvement is performed for the area of system hardening, for which research and guidelines already exist but not for the specific area of IIoT environments.

Secured Network Architectures Based on Blockchain Technologies: A Systematic Review

Blockchain applications have emerged in recent decades, among which blockchain secured-networks serve as a prevalent application. This paper provides the potential of networks secured by blockchain technology to enhance various domains and provides a structured view of the current landscape of blockchain applications, capturing the practical applications and potential of blockchain technology. Followed by a background overview, a survey analysis of the latest advancements in blockchain-secured networks is presented, categorized by six application fields: Vehicular Ad-Hoc, Health Care, Smart Home, Unmanned Aerial Vehicle (UAV), Internet of Things (IoT), and Industrial IoT Networks. An in-depth discussion on these key research topics within blockchain-secured networks is provided, enhancing understanding of their application and influence across multiple disciplines. The study overall conveys the versatility of blockchain-secured networks, highlighting their immense potential for application in various practical fields. A comprehensive structured survey analysis of recent advancements in blockchain-secured networks is conducted, and categorized by application fields. By providing a comprehensive understanding of the current trends in blockchain application, this paper enables readers to navigate information effectively and identify key areas of interest, facilitating further exploration and research opportunities in the field of blockchain technology.

Artificial Intelligence, Quantum Computing, Autonomous Operation, Emotional Intelligence: Key Drivers of Industry 6.0 and Sustainabile Development Goals (SDG-8,9,12,17) for Business Sustainability in the Oil and Gas Industry

Objective: The objective of the study investigates the extent to which specified programs and machines are activated and achieve business sustainability through sustainable development goals (SDG-8,9,12,17) in Industry 6.0. This report analyses the current situation from 2013 to 2024 and assesses the influence of Industry 6.0 on the oil and gas sector within the Gulf Cooperation Council (GCC). Theoretical Framework: Digital transformation progresses from Industry 5.0 to Industry 6.0 globally within the oil and gas sector, with only certain regions achieving totally autonomous operation, while the majority are endeavouring to attain such autonomy. The industrial revolution is realized through Industry 6.0, which is accomplished by implementing autonomous operations utilizing robotics, Artificial Intelligence (AI), quantum computing, machine learning (ML), the Industrial Internet of Things (IIoT), blockchain technology, and cloud computing within the oil and gas sector. Based on the study, proposed model proposes with autonomous operation through Industry 6.0 techniques and business sustainability. Method: Researchers have gathered numerous empirical articles, case studies and book chapters to thoroughly investigate the topic and its domains. This study employs a literature review methodology, concentrating mostly on the oil and gas service industries within GCC nations. Results and Discussion: Researchers discovered that the oil and gas service industry encounters difficulties in achieving fully automated procedures devoid of human interaction Industry 6.0 will attain corporate sustainability via total autonomy. The second scenario involves artificial intelligence, quantum computing, and emotional intelligence (EI) enabling Industry 6.0 to attain favourable results. Research Implications: The research study suggests that the implementation of Industry 6.0 in the oil and gas service sector remains mostly at a conceptual stage in most areas. Originality/Value: This study contributes to engineering services in oil and gas industry, entrepreneur’s emotional intelligence and autonomous operation during the defined period were not evaluated and it added value in the engineering service sector to mitigate their issues to have better business sustainability.

Transforming Industrial Maintenance with Thermoelectric Energy Harvesting and NB-IoT: A Case Study in Oil Refinery Applications

Heat-intensive industries (e.g., iron and steel, aluminum, cement) and explosive sectors (e.g., oil and gas, chemical, petrochemical) face challenges in achieving Industry 4.0 goals due to the widespread adoption of industrial Internet of Things (IIoT) technologies. Wireless solutions are favored in large facilities to reduce the costs and complexities of extensive wiring. However, conventional wireless devices powered by lithium batteries have limitations, including reduced lifespan in high-temperature environments and incompatibility with explosive atmospheres, leading to high maintenance costs. This paper presents a novel approach for energy-intensive and explosive industries, which represent over 40% of the gross production revenue (GPR) in several countries. The proposed solution uses residual heat to power ATEX-certified IIoT devices, eliminating the need for batteries and maintenance. These devices are designed for condition monitoring and predictive maintenance of rotating machinery, which is common in industrial settings. The study demonstrates the successful application of this technology, highlighting its potential to reduce costs and improve safety and efficiency in challenging industrial environments.

Обнаружение и объяснение аномалий в индустриальных си-стемах Интернета вещей на основе автокодировщика

In industrial Internet of Things (IoT) systems, explaining anomalies plays a crucial role in identifying bottlenecks and optimizing processes. This paper proposes an approach to anomaly detection using an autoencoder and its explana-tion based on the SHAP method. The purpose of the anomaly explanation is to provide a set of data features in indus-trial IoT systems that most significantly influence anomaly detection. The novelty of this approach lies in its ability to quantify the contribution of individual features for specific data samples and to calculate an average contribution across the dataset, providing a feature importance ranking. The proposed approach is tested on Industrial IoT datasets with varying feature counts and data volumes. The anomaly detection achieves an F-measure of 88-93%, outperform-ing the comparable methods discussed. The study demonstrates how explainable artificial intelligence can identify the causes of anomalies in both individual samples and datasets as a whole. The theoretical importance of the proposed approach lies in its ability to shed light on the workings of intelligent detection models, enabling the identification of factors influencing their outcomes and uncovering previously unnoticed patterns. In practice, this method enhances security system operators' understanding of ongoing processes, aiding in threat identification and error detection within data.

Robust Access Control for Secure IoT Outsourcing with Leakage Resilience

The Internet of Things (IoT) has revolutionized various industries by enabling seamless connectivity and data exchange among devices. However, the security and privacy of outsourced IoT data remain critical challenges, especially given the resource constraints of IoT devices. This paper proposes a robust and leakage-resilient access control scheme based on Attribute-Based Encryption (ABE) with partial decryption outsourcing. The proposed scheme minimizes computational overhead on IoT devices by offloading intensive decryption tasks to the cloud, while ensuring resilience against master secret key leakage, side-channel attacks, and other common security threats. Comprehensive security analysis demonstrates the scheme’s robustness under standard cryptographic assumptions, and performance evaluations show significant improvements in decryption efficiency, scalability, and computational performance compared to existing solutions. The proposed scheme offers a scalable, efficient, and secure access control framework, making it highly suitable for real-world IoT deployments across domains such as smart healthcare, industrial IoT, and smart cities.

A Comprehensive Survey on Emerging Trends in Networking and Communication : From IoT to Edge Computing

The advancement of networking and communication technologies has escalated the IoT and edge computing integration by leaps and bounds. This paper provides a review of the current trends, technologies and issues concerning IoT and edge computing. Looking at the key application areas of smart cities, healthcare, industrial IoT and smart grids this paper demonstrates how edge computing solves the problems of cloud computing such as latency, bandwidth and privacy. The survey also reveals present day constraints such as the lack of standards for scalability, integration problems, and lack of strong data protection measures. This work can be considered as a reference source for researchers and practitioners since it reveals the relationship between IoT and edge computing and gives an idea of further advancements.

Securing Cyber Physical Systems: Lightweight Industrial Internet of Things Authentication (LI2A) for Critical Infrastructure and Manufacturing

The increasing incorporation of Industrial Internet of Things (IIoT) devices into critical industrial operations and critical infrastructures necessitates robust security measures to safeguard confidential information and ensure dependable connectivity. Particularly in Cyber Physical Systems (CPSs), IIoT system security becomes critical as systems become more interconnected and digital. This paper introduces a novel Lightweight Industrial IoT Authentication (LI2A) method as a solution to address security concerns in the industrial sector and smart city infrastructure. Mutual authentication, authenticated message integrity, key agreement, soundness, forward secrecy, resistance to a variety of assaults, and minimal resource consumption are all features offered by LI2A. Critical to CPS operations, the approach prevents impersonation, man-in-the-middle, replay, eavesdropping, and modification assaults, according to a security study. The method proposed herein ensures the integrity of CPS networks by verifying communication reliability, identifying unauthorized message modifications, establishing a shared session key between users and IIoT devices, and periodically updating keys to ensure sustained security. A comprehensive assessment of performance takes into account each aspect of storage, communication, and computation. The communication and computing capabilities of LI2A, which are critical for the operation of CPS infrastructure, are demonstrated through comparisons with state-of-the-art systems from the literature. LI2A can be implemented in resource-constrained IIoT devices found in CPS and industrial environments, according to the results. By integrating IIoT devices into critical processes in CPS, it is possible to enhance security while also promoting urban digitalization and sustainability.