Large-scale Industrial Internet Of Things Research Articles

A Novel Traffic Classifier With Attention Mechanism for Industrial Internet of Things

With the development of the Industrial Internet of Things (IIoT), the complex traffic generated by large-scale IIoT devices presents challenges for traffic analysis. Most of existing deep learning-based traffic analysis methods use a single flow for classification, resulting in being misled by the irrelevant flow. Thus, it is necessary to use flow sequences for traffic analysis. However, existing models fail to effectively distinguish unimportant flows in flow sequence, which affects the classification performance. To address the above challenges, we propose a novel traffic classifier called Flow Transformer to perform traffic analysis with flow sequences, which leverages multi-head attention mechanism to strengthen the information interaction between related flows. Besides, the RF-based feature selection method is designed to select the optimal feature combination, avoiding insignificant features from reducing the performance of the classifier. Experimental results on three real-world traffic datasets demonstrate that our method outperforms state-of-the-art methods with a large margin.

MapChain-D: A Distributed Blockchain for IIoT Data Storage and Communications

With the rapid growth of the Industrial Internet of Things (IIoT) devices, managing extensive volume of IIoT data becomes a significant challenge. While the conventional cloud storage approaches with centralised data centres suffer from high latency for large-scale IIoT data storage due to the increased communications and latency overheads, distributed storage frameworks such as blockchains have become promising solutions. In this paper, we design and analyse a dual-blockchain framework for secure and scalable distributed data management in large-scale IIoT networks. The proposed framework, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> , consists of a data chain that is mapped to an index chain to provide efficient data storage and lookup. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> is designed for practical IIoT applications with storage, latency, and communications constraints. Detailed data exchange protocols are presented for the data insertion and retrieval operations in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> . Based on these, theoretical analyses are provided on the space, time, and communications complexities of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> compared with conventional single-chain frameworks with local and distributed data storage. We implement our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> prototype using open-source LoRaWAN communications with multiple RPi and Arduino devices, Kademlia-based distributed hash table (DHT), and Ethereum-based blockchain with proof-of-authority (PoA) consensus. Experimental results from our prototype show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> is more suitable to be deployed on resource-constrained IIoT devices. We also highlight the scalability and flexibility of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> with different numbers of edge nodes in the system.

Main-Secondary Blockchain Framework: Cross-Domain Trust Management Mechanism Using Trust Ticket

Industrial Internet of Things (IIoT) can facilitate smart manufacturing and increase productivity through revolutionary techniques. As production processes are getting more sophisticated, an entire production process is usually completed collaboratively by multiple administrative domains (e.g., factories). Trust management is an effective solution to establish trust relationship between the devices from different administrative domains. Recently, many researchers integrate blockchain into trust management mechanisms. However, the existing methods have the shortcoming of low throughput, high delay and storage overhead, so cannot meet scalability requirements of large-scale IIoT multi-domains scenarios. Therefore, this paper proposes a master-slave chain framework to realize a scalable cross-domain trust management mechanism (MSB-CTrust), where trust tickets are used to resolve the heterogeneity issue of trust evaluation methods in different domains. Besides, MSB-CTrust provides embedded salary distribution rules, which are used to encourage all devices to behave honestly based on a revenue strategy game. Performance evaluations in the experiments show the feasibility and effectiveness of MSB-CTrust.

Intelligent and Low Overhead Network Synchronization for Large-Scale Industrial IoT Systems in the 6G Era

Holistic temporal coherence among distributed industrial infrastructures enabled by accurate network synchronization is essential for achieving tight orchestration of large-scale industrial Internet of Things (IIoT) systems. However, the low efficiency and situation agnosticism of conventional synchronization techniques using an inflexible "observing-andcalibrating" approach for clock offset correction are inevitably hindering the performance of many IIoT applications with increasing scale and heterogeneity. In this article, we provide an in-depth analysis of the challenges associated with conventional synchronization schemes over large-scale IIoT systems and then present three promising research directions for achieving intelligent and low overhead IIoT synchronization. Particularly, we first propose a new modelbased network synchronization scheme that can proactively enable low overhead clock calibration by leveraging the inherent characteristics of heterogeneous clocks to avoid frequent timestamp exchange. We then design an intelligent device clustering mechanism with a specifically designed synchronization strategy for each group of IIoT devices by jointly exploiting their distinctive synchronization requirements and multi-dimensional device attributes. To leverage the unique characteristics of each oscillator, we analyze historical timestamps to reject unreliable timestamps and identify unreliable devices. Finally, we envision an edge-cloud collaborative network synchronization paradigm to implement the proposed schemes and demonstrate their efficacy in large-scale IIoT systems.

Collaborative AI-Enabled Intelligent Partial Service Provisioning in Green Industrial Fog Networks

With the evolutionary development of the latency-sensitive industrial Internet-of-Things (IIoT) applications, delay restriction becomes a critical challenge, which can be resolved by distributing IIoT applications on nearby fog devices. Besides that, efficient service provisioning and energy optimization are confronting serious challenges with the ongoing expansion of large-scale IIoT applications. However, due to insufficient resource availability, a single fog device cannot execute large-scale applications completely. In such a scenario, a partial service provisioning strategy provides a promising outcome to enable the services on multiple fog devices or collaboration with cloud servers. By motivating this scenario, in this article, we introduce a new deep reinforcement learning (DRL)-enabled partial service provisioning strategy in the green industrial fog networks. With this strategy, multiple fog devices share the excessive workload of an application among themselves. To reflect this, a task partitioning policy is introduced to partition the requested applications into a set of independent or interdependent tasks. Furthermore, we develop an intelligent partial service provisioning strategy to utilize maximum fog resources in the network. The experimental results express the significance of the proposed strategy over the traditional baseline algorithms in terms of energy consumption and latency up to 25% and 16%, respectively.

Intelligent Blockchain-Enabled Adaptive Collaborative Resource Scheduling in Large-Scale Industrial Internet of Things

With the explosive growth of devices and tasks deployed in the industrial Internet of Things (IIoT), the lack of interconnection and collaboration between devices leads to poor timeliness and security in IIoT resource scheduling. This article focuses on the issue of adaptive scheduling of resources in large-scale IIoT. First, a collaborative terminal-edge IIoT architecture is designed, which introduces blockchain and AI technology to support dynamic resource scheduling in untrustworthy environments. Then, a smart contract-based multidimensional resource transaction model is developed to improve the efficiency and security of resource scheduling by establishing a credit-based consensus mechanism. Distributed transaction learning resource scheduling algorithm is further proposed to implement resource-adaptive scheduling between devices in IIoT. Extensive simulation experiments are conducted to evaluate the proposed method with respect to several performance aspects covering the scheduling decision delay, transaction generation ratio, and security. The obtained results demonstrate that the comprehensive scheduling performance of the proposed method outperforms other existing algorithms.

Full Graph Autoencoder for One-Class Group Anomaly Detection of IIoT System

With the increasing automation and integration of equipment, it is urgent to carry out anomaly detection (AD) for the large-scale system to ensure security, in virtue of Industrial Internet of Things (IIoT). Recently developed intelligent methods focus on component-level diagnosis or detection, resulting in difficulty in the health assessment of system with multisource data coupling. In addition, data-driven methods rarely emphasize the use of knowledge from the real physical system. In this article, we propose a full graph autoencoder to perform one-class group AD for the large-scale IIoT system. The proposed model takes as input data of normal status at training and only comprises several normalized graph convolutional layers, thus it is simple and fast. Different from Euclidean-based methods, the proposed model can handle various irregular structures together. For graph learning, multivariate time series are converted into graph data fused with prior knowledge. To achieve AD, we propose to reconstruct the full graph for the first time to obtain a reliable anomaly score. Besides, we extend a variational model to fully learn the graph representation. Moreover, a graph augmentation operation is employed to improve the accuracy and robustness. The proposed models are evaluated on two multisensor data sets from liquid rocket engine (LRE) systems, and the experimental results demonstrate the effectiveness and generalization of the IIoT system.

An On-Demand Channel Bonding Algorithm Based on Outage Probability for Large-Scale Industrial Internet of Things

In Industrial Internet of Things (IIoT), a large number of wireless nodes communicate through limited channel resources. Using IEEE802.11ac/ah with multiple users multiple-input–multiple-output (MU-MIMO) and channel bonding technology in IIoT, bonding multiple channels for transmission links can improve data transmission quality. How to reasonably bond limited channel resources on demand for data transmissions in IIoT has become a key issue to improve network performance and ensure communication quality of the nodes. In this article, the definition of outage probability is extended from the amount of information to the signal noise ratio (SNR), which changes the outage probability from a statistical quantity to a quantity that can be directly calculated. This article further analyzes various factors affecting the outage probability, and derives the direct calculation formula of the outage probability in single-hop and multihop data transmission, which allows the outage probability to be directly calculated by some simple parameters. Based the outage probability, this article proposes a dynamic channel bonding algorithm based on outage probability (DCB-OP), which can bond multiple channels for the data transmissions with a high outage probability to improve the success rate of data transmissions. The experimental results show that in IEEE 802.11ac/ah networks, the DCB-OP algorithm can improve the utilization of channel resources and increase the throughput by about 40% compared with no channel bonding. Compared with the general channel bonding algorithm, DCB-OP can make the network throughput higher.

Microservices-based cloud-edge collaborative condition monitoring platform for smart manufacturing systems

In the context of the Industrial Internet of things (IIoT), large-scale IIoT data is generated, which can be effectively mined to provide valuable information for condition monitoring (CM). However, traditional CM methods cannot meet unprecedented challenges concerning large-scale IIoT data transmission, storage and analysis. Therefore, manufacturers have begun to shift from the traditional manufacturing paradigm to smart manufacturing, which integrates the encapsulated manufacturing services and the enabling cloud-edge computing technology to handle large-scale IIoT data. To enhance the agility, scalability and portability of traditional manufacturing services, a microservices-based cloud-edge collaborative CM platform for smart manufacturing systems is proposed. First, leveraging the microservices management system, the lightweight edge and cloud services are constructed from the microservices level, which enables flexible deployment and upgrade of services. Next, the proposed platform architecture effectively integrates the computing and storage capabilities of the cloud layer and the real-time nature of the edge layer, where the cloud-edge collaborative mechanism is introduced to achieve real-time diagnosis and enhance prognosis accuracy. Finally, based on the proposed system, the diagnosis and prognosis tasks are implemented on a manufacturing line, and the results show that the diagnostic accuracy is 90% and the prediction error is 50%.

Hyperledger Fabric Access Control for Industrial Internet of Things

The Industrial Internet of Things (IIoT) plays an important role in Industry 4.0, but the existing IIoT systems could be vulnerable to a single point of failure and malicious attacks, failing to provide reliable services. IIoT devices have some particularities, such as mobility, limited performance, and distributed deployment, which are challenging to traditional centralized access control methods in the large-scale IIoT environment. To resolve the challenges, we propose an access control system for the Industrial Internet of Things. The system contains three smart contracts: device contract (DC), policy contract (PC), and access contract (AC). The device contract provides a method of storing the URL of the resource data generated by the equipment and a query method. The policy contract provides the function of managing the attribute-based access control (ABAC) of the administrator user. The access contract is the core program that implements the access control method for ordinary users. Combining ABAC and blockchain technology provides decentralized, fine-grained, and dynamic access control management for IIoT.

Analysis of Security Issues and Countermeasures for the Industrial Internet of Things

Industrial Internet of Things (IIoT) can be seen as an extension of the Internet of Things (IoT) services and applications to industry with the inclusion of Industry 4.0 that provides automation, reliability, and control in production and manufacturing. IIoT has tremendous potential to accelerate industry automation in many areas, including transportation, manufacturing, automobile, marketing, to name a few places. When the benefits of IIoT are visible, the development of large-scale IIoT systems faces various security challenges resulting in many large-scale cyber-attacks, including fraudulent transactions or damage to critical infrastructure. Moreover, a large number of connected devices over the Internet and resource limitations of the devices (e.g., battery, memory, and processing capability) further pose challenges to the system. The IIoT inherits the insecurities of the traditional communication and networking technologies; however, the IIoT requires further effort to customize the available security solutions with more focus on critical industrial control systems. Several proposals discuss the issue of security, privacy, and trust in IIoT systems, but comprehensive literature considering the several aspects (e.g., users, devices, applications, cascading services, or the emergence of resources) of an IIoT system is missing in the present state of the art IIoT research. In other words, the need for considering a vision for securing an IIoT system with broader security analysis and its potential countermeasures is missing in recent times. To address this issue, in this paper, we provide a comparative analysis of the available security issues present in an IIoT system. We identify a list of security issues comprising logical, technological, and architectural points of view and consider the different IIoT security requirements. We also discuss the available IIoT architectures to examine these security concerns in a systematic way. We show how the functioning of different layers of an IIoT architecture is affected by various security issues and report a list of potential countermeasures against them. This study also presents a list of future research directions towards the development of a large-scale, secure, and trustworthy IIoT system. The study helps understand the various security issues by indicating various threats and attacks present in an IIoT system.

Passive Network Synchronization Based on Concurrent Observations in Industrial IoT Systems

Accurate network synchronization is crucial to orchestrate distributed infrastructures in Industrial Internet of Things (IIoT) systems for accomplishing network-wide tight temporal collaboration. Traditional clock synchronization can be achieved with extensive exchanges of explicit timestamps for estimating clock offsets, which becomes impractical due to high overhead with the expansion of the network scale. The performance of conventional synchronization will also be dramatically deteriorated due to various uncertainties of IIoT networks. In this article, we propose a passive network synchronization scheme based on concurrent passive observations to calibrate the distributed clocks in IIoT systems while significantly reducing the explicit interactions and network resource consumption during synchronization. By processing the physical phenomena observed concurrently by a group of selected IIoT devices, the local clock offsets of the passive observing devices can be efficiently estimated according to the common time reference linked to the event observed. Multiple relay nodes are further coordinated by the cloud center to disseminate the reference time information throughout the IIoT system. Simulation results demonstrate that by utilizing a series of concurrent observations with efficient coordination, the proposed scheme can achieve accurate and reliable network synchronization for large-scale IIoT systems with significantly reduced network overhead.

TSCH-Sim: Scaling Up Simulations of TSCH and 6TiSCH Networks.

TSCH (Time-Slotted Channel Hopping) and 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) low-power wireless networks are becoming prominent in the industrial Internet of Things (IoT) and other areas where high reliability is needed in conjunction with energy efficiency. Due to the complexity of IoT deployments, network simulations are typically used for pre-deployment design and validation. However, it is currently difficult and time-consuming to simulate large-scale IoT networks with thousands of nodes. This paper proposes TSCH-Sim: a new discrete event simulator for IEEE 802.15.4-2015 TSCH and 6TiSCH networks. The evaluation shows that simulation results obtained with TSCH-Sim show a good match with results from other simulators that are commonly used to investigate TSCH networks. At the same time, TSCH-Sim is faster than these alternatives at least by an order of magnitude, making it more practical to carry out simulations of large networks.

On-Demand Sensing and Wireless Power Transfer for Self-Sustainable Industrial Internet of Things Networks

On-demand data sensing and wireless power transfer (WPT) can provide sustainability and robust operations in large-scale industrial Internet of Things (IoT) networks. The efficiency of on-demand data collection and WPT can be increased by efficient scheduling of IoT nodes and dedicated energy transmitters respectively. In this article, we propose an energy-aware mode switching strategy to enable IoT nodes to perform either on-demand sensing or dedicated WPT. For on-demand sensing, we propose an IoT node scheduling scheme to maximize the utility of the IoT nodes comprising residual energy and energy required for sensing operation while considering the reliability of sensing tasks. For WPT, we propose an energy transmitter scheduling scheme for IoT nodes to minimize the cost of charging while keeping IoT nodes sufficiently charged. The simulation results for IoT node scheduling demonstrate that less than 50% IoT nodes need to be activated in all scenarios to complete the tasks. The proposed energy transmitter scheduling scheme shows that less than 60% energy transmitters should be scheduled in all the scenarios which results in significant energy reduction in the overall system.

FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications

Along with the proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool Deep Neural Networks (DNNs) used by Industrial IoT (IIoT) applications. Due to biased training data or vulnerable underlying models, imperceptible modifications on inputs made by adversarial attacks may result in devastating consequences. Although existing methods are promising in defending such malicious attacks, most of them can only deal with limited existing attack types, which makes the deployment of large-scale IIoT devices a great challenge. To address this problem, we present an effective federated defense approach named FDA3 that can aggregate defense knowledge against adversarial examples from different sources. Inspired by federated learning, our proposed cloud-based architecture enables the sharing of defense capabilities against different attacks among IIoT devices. Comprehensive experimental results show that the generated DNNs by our approach can not only resist more malicious attacks than existing attack-specific adversarial training methods, but also can prevent IIoT applications from new attacks.

ADTT: A Highly Efficient Distributed Tensor-Train Decomposition Method for IIoT Big Data

The industrial Internet of Things (IIoT) is growing quickly due to increasing deployment and integration of smart sensors, instruments, and devices, and software using wired or wireless networks. Through this integrated hardware–software approach, industrial practices will improve significantly, resulting in industrial intelligence for more efficient manufacturing. To realize such industrial intelligence, significant developments in IIoT big data processing and analysis are required to uncover and use hidden essential and valuable information of the production process. But large-scale, streaming, multiattribute IIoT data from production processes are noisy and have redundancies. Therefore, a suitable data processing technique such as tensor-train that can handle these IIoT data is needed. However, existing tensor-train decomposition methods are inefficient and cannot meet the processing demands of the large-scale IIoT big data. In this article, we propose an advanced (improved and highly efficient) distributed tensor-train (ADTT) decomposition method with its incremental computational method for processing IIoT big data. Finally, experiments are carried out on a typical and publicly available IIoT dataset—the bearing test data to verify and measure the performances of the proposed ADTT method.

BoSMoS: A Blockchain-Based Status Monitoring System for Defending Against Unauthorized Software Updating in Industrial Internet of Things

The role of the Industrial Internet of Things (IIoT) in critical infrastructure sectors, such as power, chemistry, and manufacturing, will be increasingly important as we move toward Industry 5.0. For example, IIoT devices are deployed in factories to help the manufacturing companies (e.g., automotive) gain in-depth insight into the various states of production, and thus improving production efficiency and achieving cost reductions. However, malicious code may compromise IIoT devices if either the devices are exposed to outside or unexposed inner devices are updated unauthentically. Due to their limited resources and features, it is challenging to implement strong security solutions for such embedded devices. In this article, we propose a blockchain-based software status monitoring system, called BoSMoS. The system is designed to monitor the software status of IIoT devices to detect and respond to identified malicious behaviors (e.g., intrusions). BoSMoS takes a snapshot of the statue of monitored software and monitors its file system calls. In order to ensure the software integrity information, we use blockchain as the distributed ledger to store a snapshot of software status. The blockchain network of BoSMoS can employ different consensus algorithms. We also evaluate the performance of BoSMoS, in terms of exception response delay, resistance performance to various intrusions, and scalability. The experimental results justify that BoSMoS is practical and sound. In addition, the evaluation of scalability and security demonstrates that the system can carry deployment of large-scale IIoT devices and can guarantee authenticated software updating, as well as detect unauthorized software status.

Service Popularity-Based Smart Resources Partitioning for Fog Computing-Enabled Industrial Internet of Things

Recently, fog computing has gained increasing attention in processing the computing tasks of the industrial Internet of things (IIoT) with different service popularity. In task-diversified fog computing-enabled IIoT (F-IIoT), the mismatch between expected computing efficiency and partitioned resources on fog nodes (FNs) may pose serious traffic congestion even large-scale industrial service interruptions. The existing works mainly studied offloading which type of computing tasks into FNs, but few studies enabled smart resource partitioning of FNs. In this paper, a service popularity-based smart resources partitioning (SPSRP) scheme is proposed for fog computing-enabled IIoT. We first exploit Zipf's law to model the relationship between popularity ranks and computing costs of IIoT services. Moreover, we propose an implementation architecture of the SPSRP scheme for F-IIoT, which decouples the computing control layer from data processing layer of IIoT through a specified SPSRP controller. Besides, a mobility and heterogeneity-aware partitioning algorithm is presented for extending SPSRP scheme to seamlessly support cross-domain resources partitioning. The simulations demonstrate that the SPSRP scheme can bring notable performance improvements on delay time, successful response rate and fault tolerance for fog computing to deal with the large-scale IIoT services.

Energy-Aware Real-Time Routing for Large-Scale Industrial Internet of Things

This paper proposes a routing scheme that enhances energy consumption and end-to-end delay for large-scale Industrial Internet of Things (IIoT) systems based on IEEE 802.15.4a MAC. In the current IIoT, a larger-scale and complex deployment has been a noticeable obstacle for minimizing power consumption and routing on real-time. Thus, the proposed algorithm is targeted at large-scale systems where data are aggregated through different clusters on their way to the sink. Moreover, a hierarchical system framework is employed to promote scalability of IIoT elements. By estimating the residual energy and hop counts for each path, the data can be forwarded to the destination through the optimal path. Simulation results show that the scheme can reduce the energy consumption and end-to-end delay effectively.

Parallel computing and network analytics for fast Industrial Internet-of-Things (IIoT) machine information processing and condition monitoring

Rapid advancement in sensing, communication, and mobile technologies brings a new wave of Industrial Internet of Things (IIoT). IIoT integrates a large number of sensors for smart and connected monitoring of machine conditions. Sensor observations contain rich information on operational signatures of machines, thereby providing a great opportunity for machine condition monitoring and control. However, realizing the full potential of IIoT depends to a great extent on the development of new methodologies using big data analytics. This paper presents a new methodology for large-scale IIoT machine information processing, network modeling, condition monitoring, and fault diagnosis. First, we introduce a dynamic warping algorithm to characterize the dissimilarity of machine signatures (e.g., power profiles during operations). Second, we develop a stochastic network embedding algorithm to construct a large-scale network of IIoT machines, in which the dissimilarity between machine signatures is preserved in the network node-to-node distance. When the machine condition varies, the location of the corresponding network node changes accordingly. As such, node locations will reveal diagnostic information about machine conditions. However, the network embedding algorithm is computationally expensive in the presence of large amounts of IIoT-enabled machines. Therefore, we further develop a parallel computing scheme that harnesses the power of multiple processors for efficient network modeling of large-scale IIoT-enabled machines. Experimental results show that the developed algorithm efficiently and effectively characterizes the variations of signatures in both cycle-to-cycle and machine-to-machine scales. This new approach shows strong potentials for optimal machine scheduling and maintenance in the context of large-scale IIoT.