Reliable industrial IoT-based distributed automation

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.

Today's industrial automation systems are undergoing a digital transformation that implies a shift towards the Internet of Things (IoT), leading to the Industrial Internet of Things (IIoT) paradigm. Existing Industrial Automated Control Systems (IACS), enriched with a potentially large number of IoT devices are expected to make systems more efficient, flexible, provide intelligence, and ultimately enable autonomous control. In general, the majority of such systems come with high level of criticality that calls for well-established methods and approaches when achieving cybersecurity, preferably prescribed by a standard. IEC 62443 is an industrial standard that provides procedures to manage risks related to cybersecurity threats in IACS. Given the new IIoT paradigm, it is likely that existing standards are not sufficiently aligned with the challenges related to developing and maintaining cybersecurity in such systems. In this paper we review the applicability of the IEC 62443 standard in IIoT contexts and discuss potential challenges the process owners might encounter. Our analysis underlines that some areas within the standard could prove difficult to reach compliance with. In particular, handling of cross zone communication and software updates require additional guidance.

The rapid integration of industrial automation and control systems into large-scale Industrial Internet of Things (IIoT) infrastructures has substantially increased system complexity. The complexity arises from the deployment of heterogeneous devices that must coordinate in real-time to support adaptive decision-making. As a result, a robust situation awareness (SA) framework for IIoT has become a critical goal of IIoT. This review investigates the role of SA in the design of controllers in industrial applications, with the aim of advancing control strategies and facilitating integration with emerging IoT technologies. The survey focuses on SA, Model Predictive Control (MPC), and their integration, based on time series as a standard data structure to represent device performance. A key finding from the current literature is that SA is often treated separately from control theory, resulting in isolated applications and not integrated with large-scale IIoT systems. This survey establishes the features and paradigms of SA in IIoT. It provides a structured analysis of controller design methods in IIoT. State-space models (SSMs) can be used to model temporal dynamics in IIoT data. Finally, the integration of artificial intelligence (AI) with SSMs is explored to achieve SA based on multivariate time series data.

The Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) intersected in the realm of industry, transforming the industrial system to allow making real-time decisions at the edge. Latency issues, bandwidth consumption, and data security also pose a challenge to the traditional cloud-based system, and are of utmost priority in the industry where time is limited. Edge AI combines AI features with edge computing, moving the intelligence to the devices in the IIoT, allowing decisions to be made faster and more responsive to the context without depending on centralized infrastructures that might overwhelm the cloud. The paper summarizes the present-day developments in edge AI as a decision-making tool in IIoT and identifies the future directions. It discusses architectures, algorithms and applications that enable intelligent decision making at the network edge with particular focus on manufacturing, predictive maintenance, supply chain optimization and energy management. It is reviewed that facilitating technologies, such as lightweight deep learning models, federated learning, and hardware accelerators, and problems, such as scalability, interoperability, and cybersecurity are discussed. As depicted in the literature section, edge AI has been found to enhance efficiency of distributed industrial systems by reducing latencies, reliability, and independent decision-making. However, barriers such as the shortage of resources, failure to interface with current systems and standardized structures still persist. It is anticipated that future studies will be based on adaptive AI models and edge-cloud collaboration with application of 6G-enabled IIoT ecosystems. This paper summarizes the synthesis of state-of-the-art methods to inform the next generation of industrial automation and digital transformation powered by edge AI. It indicates the need of powerful, hardy, and scalable systems to open up the complete scope of opportunities that edge AI can introduce to decisions made by IIoT.

This article presents an idea of achieving reliability through Industrial Internet of Things (IIoT) for industrial power systems. It proposes hybrid approach for predictive and corrective maintenance. It discusses the self-corrective maintenance (SCM) paradigm as hybrid approach for industrial power systems along with condition-based maintenance approach utilizing IIoT to achieve it. As it is well known that industries pay huge penalty for the down time, and suffer to meet reliability demands for years. Study witnesses its cost in millions of dollars yearly for production disruptions. It can be prevented by proactively following the aggressive maintenance schedule. However, it often becomes expensive as part or service may not be utilized for its full life and failure may occur even in middle of maintenance cycle. On the other hand, condition-based maintenance (CBM) helps utilize the full life and prevents the downtime by predicting the failures ahead. This article reviews current maintenance practices followed by industry leaders and a proposal on self-corrective maintenance based on condition of restorable resources. It is about learning the condition of subsystems by itself and taking corrective action when subsystem is not active. This concept helps reduce manual intervention to correct the problem as well as the maintenance cost. This research also covers the self-uncorrectable issues to be handled by proactively following CBM process through IIoT. This hybrid proposal could be a significant gear shift in maintenance direction for general industry as well as power systems. It can be termed as industry's 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> revolution or Industry 5.0.

Industrial Internet of Things (IIoT) applications connect machines, sensors and actuators in high-stake manufacturing industries. Industrial systems are using the potential of IoT to reduce the unnecessary operational cost and increase the usability and reliability of the industrial assets to achieve more profits. However, such smart Industries need connectivity and interoperability to enhance performance which makes them susceptible to various attacks. Recent attacks on Cyber-physical systems raise a strong security concern as such attacks causes a huge property loss and may also lead to life threatening situations. In this paper we discuss the potential security threats to the Industries adapting to IIoT and study the various attacks that are possible on the components in the layered IIoT architecture and some of the preventive measures. Finally, we propose IIoT attack taxonomy which would help in mitigating the risks of the attacks.

Nowadays, manufacturing companies integrate several advanced technologies such as Internet of things, cyber-physical systems, smart factories, etc. to adapt themselves, as soon as possible, to the volatile market demands as well as to increase their throughput and profit. At the design level, the use of classical formal approaches such as Petri nets (either low or high-level) becomes neither convenient nor useful in the modeling and verification of modern manufacturing systems due to their rigid structure that cannot handle the reconfigurability of such systems. In this paper, we introduce configuration-dependent stochastic reward nets formalism that allows designers to study the reconfiguration in smart factories. The proposed formalism transforms configuration-dependent stochastic reward nets into stochastic reward nets in order to use the existing tools proposed for stochastic reward nets.

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.

Abstract— IIoT is being used more and more to automate and track industrial environments through machines, sensors and controllers that are connected. However, its extensive connectivity with numerous systems introduces significant security challenges for industrial control systems. IIoT environments are prone to more sophisticated cyber threats, because conventional cybersecurity approaches are constrained by old protocols. In this work, we propose a deep learning based method for detection of the cyberattacks in IIoT systems in particular SCADA systems. The proposed method integrates a PRU model with decision trees (DT) through an ensemble learning strategy, achieving enhanced detection accuracy. This integration enables nonlinear feature extraction and strengthens the system by mitigating the impact of inconsistent features. Evaluation on fifteen SCADA datasets demonstrates that our approach outperforms classical and learning-based detection methods. The results demonstrate that the framework can be utilized for enhancing protection, trust and robustness in industrial IIoT systems. Keywords— Industrial Internet of Things(IIoT), Cyberattack Detection, Deep Learning, Pyramidal Recurrent Units (PRU), Decision Trees (DT).

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.

New generation Industrial Automation and Control Systems (IACS) are providing advanced connectivity features, enabling new automation applications, services and business models in the Industrial Internet of Things (IIoT) era. Nevertheless, due to the extended attack surface and increasing number of cyber-attacks against industrial equipment, security concerns arise. Hence, these systems should provide enough protection and resiliency against cyber-attacks throughout their entire lifespan, which, in the case of industrial systems, may last several decades. A sound and complete management of security issues and software updates is fundamental to achieve such goal, since leading-edge security countermeasures implemented in the development phase may eventually become out-of-date. In this article, a review of the IEC 62443 industrial security standard concerning the security maintenance of IIoT systems and components is given, along with guidelines for the implementation of such processes. As concluded, the security issues and software updates management shall jointly be addressed by the asset owner, service providers and product suppliers. These security processes should also be compatible with the safety procedures established by safety standards.

Deep Neural Networks (DNNs) have been extensively applied in the Industrial Internet of Things (IIoT) to support intelligent manufacturing and industrial automation. However, the limited computing and energy resources of IIoT devices hinder the efficiency of on-device DNN inference due to increased energy consumption and latency. To mitigate these limitations, collaborative inference and adaptive sampling techniques have emerged as effective approaches to dynamically allocate computational workloads between IIoT devices and edge servers, thereby reducing on-device burdens and improving overall system performance. In this paper, we propose an edge-assisted IIoT system that enables collaborative inference through a novel Diffusion Model-based Deep Reinforcement Learning (Diffusion-DRL) algorithm. The algorithm jointly optimizes sampling and task offloading strategy to minimize terminal energy consumption while ensuring inference accuracy and meeting latency requirements under complex constraints. By combining the generative power of the diffusion models with the decision-making capabilities of deep reinforcement learning, Diffusion-DRL effectively handles multi-dimensional discrete action spaces. Experimental results demonstrate that the proposed approach significantly reduces latency and energy consumption, while enhancing inference accuracy, offering an effective solution for intelligent collaborative inference in IIoT systems.

ABSTRACTThe transformation of industrial systems through real‐time analytics, autonomous control, and intelligent data acquisition has underscored the pivotal role of Industrial Edge Computing (IEC) in next‐generation Industrial Internet of Things (IIoT) environments. By enabling decentralized processing close to data sources, IEC enhances responsiveness, supports latency‐sensitive applications, and reduces the strain on centralized infrastructure. However, as IIoT ecosystems grow in scale and complexity, traditional edge solutions face increasing challenges related to device heterogeneity, dynamic network conditions, bandwidth constraints, and energy efficiency. This special issue explores emerging advancements in edge intelligence that address these pressing challenges. It brings together innovative research that leverages artificial intelligence, advanced communication technologies, and architectural innovations to improve the adaptability, resilience, and performance of edge‐enabled industrial systems. The featured studies contribute novel techniques for real‐time data processing, secure and efficient communication, and intelligent decision‐making at the edge, all of which are essential for supporting industrial automation, predictive maintenance, and cyber‐physical operations. Collectively, these contributions highlight the immense potential of edge intelligence to redefine the operational landscape of industrial systems. This issue is intended to support ongoing research and practical innovation in the evolving domain of edge‐enabled IIoT technologies.

Incorporating internet of things (IoT) in industrial systems prompted the development of industrial internet of things (IIoT) systems, which in turn enable the automation of intelligent devices to gather, analyze, and transmit data from industrial systems in real-time. This paper develops a low-cost and smart industrial remote monitoring and control system based on NodeMCU microcontrollers and Blynk server platform. It is deployed to remotely monitor manufacturer's environment and industrial equipment and control them autonomously. Also, it protects the manufacturer's employees from fire catastrophe by warning them using a buzzer and notification. The system comprises two main parts, sensing and actuation. The sensing part consists of three subsystems that measure temperature and humidity, water flow, and flame. The actuation part consists of a water pump, light, and fan. A powerful user interface is developed based on the Blynk platform. The proposed system controls the water pump by sensing water flow autonomously. In addition, based on a fire detected, a protection system is implemented to shut down the electricity from load in case of fire event occurs. Several testing scenarios were carried on to check the response of the system, and the result shows successful implementation of the proposal to handle different situations.

The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.