IIoT Systems Research Articles

Open source container orchestration for Industry 4.0 – requirements and systematic feature analysis

Container-based virtualization is a popular technique, e.g., to realize microservice architectures. Recently, containers became popular in Industry 4.0 / IIoT systems, which typically consist of hundreds of (edge) devices and machines. In such setups, efficient management of containers is essential as offered by container orchestrators like Kubernetes. However, currently no specific overviews discussing orchestrator capabilities for Industry 4.0 are available. In this paper, we analyze nine open source container orchestrators for their application in Industry 4.0 or IIoT settings as a basis for future research and development. We contribute a systematic literature review to identify 23 basic orchestration requirements. We complement this by insights from an intensive requirements collection in a research project on intelligent industrial production, as well as selected features from a published generic orchestrator analysis. From these 66 requirements, we derive a requirements/feature taxonomy, which we use to analyze the nine open source orchestrators including Kubernetes. We show that there is, e.g., still a lack of support regarding edge devices, IIoT protocols, security mechanisms, and specialized resources for artificial intelligence.

Evaluating Dimensionality Reduction Methods for the Detection of Industrial IoT Attacks in Edge Computing

Edge computing is essential for 6G mobile networks for improving reliability, reducing data rates and latency, and enhancing mobile connectivity. Edge computing is also meant to meet the increasing demands of the Internet of Things (IoT)/ Internet of Everything (IoE). In these approaches, IIoT systems necessitate precision, reliability, and scalability, while vulnerabilities in IIoT systems may lead to financial losses and safety hazards. To tackle this, Edge AI/ML-based IDSs provide adaptability and robustness for IIoT security challenges. These solutions improve security levels, threat detection rates, and response times. However, main issues such as limited resources and the accuracy of attack detection rate are challenged nowadays. In this paper, we present contributions in proposing an intrusion detection system (IDS) for edge devices deploying a lightweight deep neural network to detect IIoT attacks. We present performance analysis of the typical dimensionality reduction methods and balancing data features of the Edge-IIoT dataset to get higher performance metrics than other state-of-the-art studies.

Novel AMUB Sequences for Massive Connection IIoT Systems

Novel AMUB Sequences for Massive Connection IIoT Systems

Cloud Storage for Object Detection using ESP32-CAM

Cloud storage services can create an object storage bucket to store our pictures, among them the Cloud Storage FUSE, Scaleway, S3 bucket, Firebase, etc. intelligent IoT systems generate vast amounts of multi-source industrial data, which necessitate a large amount of storage and processing power to enable real-time data processing and analysis. Cloud computing can be intricately linked into intelligent IIoT systems due to its strong computational and storage capabilities. Cloud Storage for Object Detection using ESP32-CAM. Create a workable solution that supports distributed storage bucket and implement it in a real-world setting. Implement the entire system as an addition to the well-known IoT cloud storage and run multiple experiments to evaluate its functionality in scenarios with varying setups and system. The target objects that are used as data sets are the ESP8266, Wemos D1, and Arduino Uno. Figuring out the ideal parameters for training the FOMO (First Object, More Object) model and then putting it into practice. It was necessary to find a balance between learning rate and accuracy, on the other hand, to maintain the highest possible accuracy in the identification of the microcontroller object to minimise the number of false positive reports. Find the value learning rate effective to this object is 0.01 with F1 score 98.7% and accuracy score 89.58%.

A Distributed Resource Sharing Mechanism in Edge-Enabled IIoT Systems

A Distributed Resource Sharing Mechanism in Edge-Enabled IIoT Systems

Detecting Cyber-attacks in the Industrial Internet of Things using a Hybrid Deep Random Neural Network

Critical infrastructure now faces greater vulnerabilities and a higher risk of cyberattacks as a result of the (IIoT) quick expansion. The security and dependability of industrial systems must be ensured by identifying and thwarting these threats. In this paper, we use a hybrid approach of deep learning and RNN called hybrid deep random neural network (HDRNN) to offer a novel method of identifying cyber-attacks in the IIoT.The proposed HDRNN model combines the benefits of random neural networks with deep learning to improve the detection of IIoT cyberattacks. The deep learning component makes use of deep neural networks' capacity to extract intricate features from unstructured data, while the random neural network component offers robustness and adaptability to manage changing attack patterns.Realistic threats and benchmark datasets such as UNSW-NB15and DS2OS are used in experimental evaluations. High accuracy, precision, and recall rates are attained by the model, which successfully detects a variety of assaults including infiltration, data manipulation, and denial of service.The suggested HDRNN model offers a promising approach for improving the security of IIoT systems by precisely identifying cyber-attacks in real-time. The model's hybrid nature enables enhanced detection capabilities, adaptability to changing attack patterns, and a reduction in false positives, enabling efficient threat mitigation and protecting crucial infrastructure in the IIoT context.

Trust-AoI-Aware Codesign of Scheduling and Control for Edge-Enabled IIoT Systems

Trust-AoI-Aware Codesign of Scheduling and Control for Edge-Enabled IIoT Systems

Deploying IIoT Systems for Long-Term Planning in Underground Mining: A Focus on the Monitoring of Explosive Atmospheres

Deploying IIoT Systems for Long-Term Planning in Underground Mining: A Focus on the Monitoring of Explosive Atmospheres

Lifetime Reliability Aware Distributed Estimation and Communication Co-Design for IIoT Systems

Lifetime Reliability Aware Distributed Estimation and Communication Co-Design for IIoT Systems

A trustworthy security model for IIoT attacks on industrial robots

The security of industrial Internet of Things (IIOT) has recently attracted significant attention. As typical IIoT systems, industrial robots are suffering from lots of threats involving control, communication, and computing, which are difficult to detect IIoT attacks accurately in real-time due to resource constraints. How to efficiently and accurately identify IoT attacks on industrial robots is challenging. To address this, we propose a trustworthy security model (TSM) with a fusion design that integrates an improved deep Q-network (IDQN) and a control model, thus accelerating the model training process by reducing the network traversal space and improving detection accuracy by establishing a prejudgment mechanism. We initially provide a detailed overview of existing methods for robot security and derive a robot control model consisting of kinematics and kinetic. Then, a 17-labeled dataset named iRobot security dataset is established to train the TSM. Moreover, we established a robot physical platform to evaluate the performance of TSM, and five cyber security indicators are employed to quantify the performance. Experimental results show that TSM detects attacks at an average rate of 98.7 % and a 0.01 s latency, which demonstrates the excellent performance in detection accuracy and detection efficiency, and provides an idea for solving these issues.

Fingerprinting Industrial IoT devices based on multi-branch neural network

Industrial Internet-of-Things systems suffer from a vast and vulnerable attack surface, raising widespread concerns about shielding IIoT devices from malicious attacks and reducing cyber risks. Device identification is the prerequisite to safeguard IIoT systems. We leverage the observation that IIoT network protocol implementations vary due to different hardware architectures/configurations and design tasks of IIoT devices, which cause the difference in their network traffic payloads. Specifically, we develop a novel neural network to learn the semantic/syntax features among multiple IIoT packets. The neural network has multiple branches, each of which consists of convolution layers, attention modules, and highway units for learning the classification model of IIoT devices. To validate the precision and recall of our neural network in IIoT devices fingerprinting, we have implemented a prototype of the proposed IIoT device identification system. Our results show that our approach achieves 95.8% precision and 95.4% recall, significantly outperforming other classification models.

Fighting against COVID‐19: Innovations and applications

Fighting against <scp>COVID</scp>‐19: Innovations and applications

A Sparse Protocol Parsing Method for IIoT Based on BPSO-vote-HMM Hybrid Model

With the development of the Industrial Internet of Things, industrial control systems have become more open and intelligent. However, large numbers of unknown protocols exist in IIoT, threatening the security of IIoT devices and systems. Protocol reverse engineering extracts the grammar and semantics of the protocol by monitoring and analyzing the traffic trace or the execution process of instructions, without the need for protocol description. As the executable programs are mainly integrated into the IIoT devices and the communication traffic is relatively sparse, the traditional protocol analyzing method is not suitable for the IIoT environment. This paper proposes an improved sparse protocol parsing method of IIoT protocol based on the BPSO-vote-HMM hybrid model. The binary particle swarm optimization algorithm is introduced to expand the captured IIoT protocol message sequence, solving the problems of sparse samples in IIoT and the low efficiency of the GA-based data expansion model. Besides, we improve on the parameter training part to improve the efficiency and get better model parameters by dividing the training set into several sub-sets, conducting the parameter update parallel, and inputting the results into a voter to generate the final parameter of HMM, which is used in protocol field prediction. Finally, by combining the BPSO-based data expansion model and the protocol field parsing model based on vote-HMM, a hybrid analytical model is constructed to improve the analytical accuracy in a gradual evolutionary manner. Through a series of comparative experiments, the improved protocol field parsing model has better performance on IIoT protocol.

An efficient Key Agreement and Authentication Scheme (KAAS) with enhanced security control for IIoT systems

An efficient Key Agreement and Authentication Scheme (KAAS) with enhanced security control for IIoT systems

An Overview of Technologies for Improving Storage Efficiency in Blockchain-Based IIoT Applications

Since the inception of blockchain-based cryptocurrencies, researchers have been fascinated with the idea of integrating blockchain technology into other fields, such as health and manufacturing. Despite the benefits of blockchain, which include immutability, transparency, and traceability, certain issues that limit its integration with IIoT still linger. One of these prominent problems is the storage inefficiency of the blockchain. Due to the append-only nature of the blockchain, the growth of the blockchain ledger inevitably leads to high storage requirements for blockchain peers. This poses a challenge for its integration with the IIoT, where high volumes of data are generated at a relatively faster rate than in applications such as financial systems. Therefore, there is a need for blockchain architectures that deal effectively with the rapid growth of the blockchain ledger. This paper discusses the problem of storage inefficiency in existing blockchain systems, how this affects their scalability, and the challenges that this poses to their integration with IIoT. This paper explores existing solutions for improving the storage efficiency of blockchain–IIoT systems, classifying these proposed solutions according to their approaches and providing insight into their effectiveness through a detailed comparative analysis and examination of their long-term sustainability. Potential directions for future research on the enhancement of storage efficiency in blockchain–IIoT systems are also discussed.

Fostering insights and improvements from IIoT systems at the shop floor: a case of industry 4.0 and lean complementarity enabled by action learning

PurposeThis paper aims to investigate how manufacturers can foster insights and improvements from real-time data among shop-floor workers by developing organisational “learning-to-learn” capabilities based on both the lean- and action learning principle of learning through problem-solving. Second, the purpose is to extrapolate findings on how action learning can enable the complementarity between lean and industry 4.0.Design/methodology/approachAn insider action research approach is adopted to investigate how manufacturers can enable their shop-floor workers to foster insights and improvements from real-time data at VELUX.FindingsThe findings report that enabling shop-floor workers to use real-time data consist of developing three consecutive organisational building blocks of learning-to-learn, learning-to-learn using real-time data and learning-to-learn generating real-time data − and helping others to learn (to learn).Originality/valueFirst, the study contributes to theory and practice by demonstrating that a learning-to-learn capability is a core construct for manufacturers seeking to enable shop-floor workers to use real-time data-capturing systems to drive improvement. Second, the study outlines how lean and industry 4.0 complementarity can be enabled by action learning. Moreover, the study allows us to deduce six necessary conditions for enabling shop-floor workers to foster insights and improvements from real-time data.

Data protection and threat prediction in industrial IoT cloud environment

There are several cloud computing data protection systems depending on accessibility control, attribute-based encryption (ABE), security and reputations, but still, it is disjointed and lacks a cohesive rationale. I-IoT systems including data protection and threat security metrics are discussed in this paper. I-IoT layered method that is general and extended, with privacy, security features and layers detection is proposed. To protect the user's private data, security procedures and crucial management events are established amongst all the lower, moderate and higher levels. To permit data flow across the levels of the proposed cloud computed powered I-IoT paradigm, we built security certificates. The proposed systematic method eliminates potential security vulnerabilities and is also utilised in conjunction with the optimal security approaches to mitigate the security threats on cloud computed decentralised industrial IoT systems and devices. The assaults toward I-IoT systems are described in this paper, and a detailed examination of remedies to these attacks.

Implementing Industry 4.0 in Steel Manufacturing’s Evolving IIoT Systems

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Концепция программно-аппаратного комплекса защиты IIoT-устройств

The Internet of Things is a new trend in the development of information technologies, actively spreading as interconnected functional nodes that by default have the means to ensure interaction directly with each other, or with the external environment. The development of such networks is a phenomenon that can radically change the world economy, as well as social processes. As a result of the application of this layer of information technologies, the need for direct human control over actions and operations will be eliminated. One of the most common household examples is a “smart home” – a computer network of interconnected devices using various data transfer protocols with the ability to access this network from an external network. So, thanks to IoT, it is possible to configure devices in the house by remote access. The paper deals with the main security methods against attacks and threats of IIoT devices. The information infrastructure was analyzed, an idea of the architecture of IIoT systems was obtained, and the information transmission path was identified. An analysis of the IIoT regulatory framework governing security, architecture and data exchange in IIoT systems has been carried out, documents governing the Industrial Internet of Things have been identified. The best communication protocol has been identified. Subsequently, the threats of the protocol were selected, and possible means and methods of protection were identified. For IIoT gateways, the concept of a hardware-software complex was presented as a prototype, and a comparison was made with existing solutions.

Blockchain-Based Internet of Things and Industrial IoT: A Comprehensive Survey

Industry 4.0 connects the latest technologies such as cloud computing, Internet of things (IoT), machine learning and artificial intelligence (ML/AI), and blockchain to provide more automation in the industrial process and also bridges the gap between the physical and digital worlds through the cyber-physical system. The inherent feature of IoT devices creates the industry to smart industry (referred to as industrial IoT, i.e., IIoT) through its data-driven decision policies. However, several challenges such as decentralization, security and privacy vulnerability, single point of failure (SPOF), and trust issues exist in the IoT system. Blockchain is one of the promising technologies that can bring about opportunities for addressing the challenges of IoT systems. In this article, we have investigated the integration of IoT with blockchain technology and provided an in-depth study of the blockchain-enabled IoT and IIoT systems. The state-of-the-art research is categorized into data storage and management technique, big data and cloud computing technique (finance and data auditing), and industrial sectors (supply chain, energy, and healthcare sector). The insightful discussion based on the different categories is also presented in the paper. In particular, first, we introduce the IoT and IIoT and then discuss the need for smart contracts in IoT and IIoT systems. Next, we concentrate on the convergence of blockchain and IoT with state-of-the-art research. In addition, this article also provides the open and future research directions towards this era with the highlighted observations.