Industrial Internet Of Things Environment Research Articles

Efficient Batch Authentication Scheme Based on Edge Computing in IIoT

In the industrial Internet of Things (IIoT) environment (e.g., a smart factory), smart devices with limited computing power can bring large amounts of privacy-sensitive data into insecure networks when they interact. If a network attacker intercepts and tampers with this data, it may cause chaos in production and even paralyze the entire IIoT system. Therefore, to ensure the regular operation of intelligent production, data receivers must authenticate the data before using them. However, existing message authentication schemes in the IIoT environment authenticate each message individually, which creates many redundant operations. Hence, to ensure data security among smart devices and reduce the computational overhead of data processing, we propose a batch authentication scheme based on edge computing in IIoT. Specifically, we design a lightweight batch authentication algorithm and use edge servers to assist smart devices in authenticating data, thus reducing the computational burden on smart devices and improving the efficiency of message authentication. The security analysis shows that the proposed scheme is secure in the random oracle model and meets the series of security requirements of the IIoT. In addition, we illustrate the efficiency of the scheme through experiments.

Privacy-Preserving Authentication Scheme With Revocability for Multi-WSN in Industrial IoT

Wireless sensor network (WSN), as one of the core technology in the Industrial Internet of Things (IIoT) system, plays a critical role in collecting data for the monitoring areas. Security and privacy are essential to ensure the trustworthy completeness of data access and transmission for the WSN-based IIoT system. Recently, a series of excellent privacy-preserving authentication schemes for the WSN-based IIoT system have been proposed. There is, however, strong demand for further improvements in multiple registrations, user revocation, and forward secrecy. Targeted by these security issues, we propose an efficient privacy-preserving authentication scheme with revocability for multi-WSNs in the IIoT environment. In the proposed scheme, the user can access multiple different WSNs with the same identity and password through a single registration. Our scheme is provable secure under the random oracle model, and security analysis via practical circumstances shows that it is able to provide a variety of security requirements, such as user revocation, forward secrecy, etc. In addition, the comparison with related schemes indicates that the proposed one has fewer sensor nodes-based computations and overall communication overheads. It is, therefore, more suitable for the resource-constrained IIoT environment than existing approaches.

A Fog-Based Security Framework for Large-Scale Industrial Internet of Things Environments

The Industrial Internet of Things (IIoT) is a developing research area with potential global Internet connectivity, turning everyday objects into intelligent devices with more autonomous activities. IIoT services and applications are not only being used in smart homes and smart cities, but they have also become an essential element of the Industry 4.0 concept. The emergence of the IIoT helps traditional industries simplify production processes, reduce production costs, and improve industrial efficiency. However, the involvement of many heterogeneous devices, the use of third-party software, and the resource-constrained nature of the IoT devices bring new security risks to the production chain and expose vulnerabilities to the systems. The Distributed Denial of Service (DDoS) attacks are significant, among others. This article analyzes the threats and attacks in the IIoT and discusses how DDoS attacks impact the production process and communication dysfunctions with IIoT services and applications. This article also proposes a reference security framework that enhances the advantages of fog computing to demonstrate countermeasures against DDoS attacks and possible strategies to mitigate such attacks at scale.

Synchronization of Shop-Floor Logistics and Manufacturing Under IIoT and Digital Twin-Enabled Graduation Intelligent Manufacturing System.

Logistics interfaces with manufacturing throughout the entire production process need synchronous operations. For achieving integrated organization and operations between manufacturing and logistics, this article introduces the concept of shop-floor logistics and manufacturing synchronization with four principles, including: 1) synchronization-oriented manufacturing system; 2) synchronized information sharing; 3) synchronized decision making; and 4) synchronized operations. The marriage of the principles rendered the development of an overall framework of the Industrial Internet of Things (IIoT) and digital twin-enabled graduation intelligent manufacturing system (GiMS). A mixed-integer programming-based synchronization mechanism is proposed under GiMS. To meet the requirement of fast decision making in real-life shop-floor logistics and manufacturing synchronization problems, an equivalent constraint programming model is developed and tested. The observation and analysis of the case company show the advantage of the proposed concept and approach with the best performance regarding key performance indicators. The concept of synchronization provides an insight for understanding the interaction of logistics and manufacturing at the operational level. This article potentially enables manufacturers to reevaluate and develop their manufacturing planning and control strategies in the IIoT and digital twin-based manufacturing environment.

Blockchain-Based Resource Trading in Multi-UAV-Assisted Industrial IoT Networks: A Multi-Agent DRL Approach

With the Industrial Internet of Things (IIoT), mobile devices (MDs) and their demands for low-latency data communication are increasing. Due to the limited resources of MDs, such as energy, computation, storage, and bandwidth, IIoT systems cannot meet MDs’ quality of service (QoS) and security requirements. Recently, UAVs have been deployed as aerial base stations in the IIoT network to provide connectivity and share resources with MDs. We consider a resource trading environment where multiple resource providers compete to sell their resources to MDs and maximize their profit by continually adjusting their pricing strategies. Multiple MDs, on the other hand, interact with the environment to make purchasing decisions based on the prices set by resource providers to reduce costs and improve QoS. We propose a novel intelligent resource trading framework that integrates multi-agent deep reinforcement Learning (MADRL), blockchain, and game theory to manage dynamic resource trading environments. A consortium blockchain with a smart contract is deployed to ensure the security and privacy of the resource transactions. We formulated the optimization problem using a Stackelberg game. However, the formulated optimization problem in the multi-agent IIoT environment is complex and dynamic, making it difficult to solve directly. Thus, we transform it into a stochastic game to solve the dynamics of the optimization problem. We propose a dynamic pricing algorithm that combines the Stackelberg game with the MADRL algorithm to solve the formulated stochastic game. The simulation results show that our proposed scheme outperforms others to improve resource trading in UAV-assisted IIoT networks.

Novel Online Network Intrusion Detection System for Industrial IoT Based on OI-SVDD and AS-ELM

The Industrial Internet of Things (IIoT) should be equipped with computational resources to detect network intrusions, types of attacks, and update their models automatically in real time. The most challenging aspect of machine learning (ML)-based network intrusion detection system (NIDS) design to secure IIoT is the continuous need for up-to-date definitions of attack data records. Moreover, the approaches employed by cyber attackers are in a dynamic state with changing trends and techniques. Hence, conventional signature-based NIDS are not suitable since they cannot update obsolete detection models. Anomaly-based NIDS that contains an online learning technique is adopted in our proposed method. Since IIoTs face resource-constrained problems, such as low memory, low computing capacity, and limited energy supply, it is very challenging to implement the exiting general-purpose anomaly-based NIDS. This article proposes a lightweight NIDS based on an online incremental support vector data description (OI-SVDD) anomaly detection system on the IIoT devices and an adaptive sequential extreme learning machine (AS-ELM) on the multiaccess edge computing (MEC) server. Additionally, we utilize the MEC server, which provides computational resources to execute the AS-ELM model at the network’s edge. To avoid data saturation in the proposed model, we apply data filtering using the rate of convergence (ROC). We evaluated the proposed NIDS through experiments using two data sets, such as UNSW-NB15 (public data set) and our self-generated data set. Our results show that the proposed OI-SVDD and AS-ELM perform effectively and detect network intrusion in a realistic IIoT environment.

The Impact of Edge Computing on the Industrial Internet of Things

The emergence of the Industrial Internet of Things (IIoT) has improved the management of industrial operations and processes. IIoT involves collecting and processing data from a vast array of sensors deployed across industrial complexes. This enables the measurement of the efficiency of industrial processes, monitoring the health of machinery, optimisation of operations, and response to real-time events. In its application, IIoT underpins the operation of critical infrastructure in sectors including manufacturing and utilities. Maintaining the availability and resilience of critical infrastructure against internal and external threats is essential to minimise disruptions that could have a debilitating effect on a nation’s economy. Although internal threats can lead to a critical infrastructure’s downtime, external threats through cyberattacks also pose a significant threat. Historical events have demonstrated that the successful disruption of critical infrastructure can lead to the loss of human life, the interruption of necessary economic activities and national security concerns. Therefore, the availability of resilient critical infrastructures is vital to the well-being of a country. In this context, the paper compares the deployment of traditional IIoT to that of edge computing for the storage and processing of data. Traditional IIoT relies on a centralised server for data storage and processing, which is insufficient as IIoT environments cannot tolerate delays in responding to real-time events. Conversely, edge computing allows for data processing at the edge, closer to the data source, which plays a crucial role in enabling IIoT devices to respond to real-time events by reducing decision-making latency. Moreover, the decentralised nature of edge computing reduces the reliance on a centralised server by only sending required data to the cloud for further processing. Although edge computing enhances IIoT deployments, a notable concern is a resultant increase in the attack surface of IIoT environments, which consequently restricts its implementation. Exploratory research is conducted to explore the integration of edge computing into IIoT environments with a focus on improving the management and operation of critical infrastructures. A review of the current literature is performed to identify and discuss security concerns prevalent in edge computing-enabled IIoT environments and proposed mitigation strategies.

NEAT: A Resilient Deep Representational Learning for Fault Detection Using Acoustic Signals in IIoT Environment

Fault diagnostics involving the Internet-of-Things (IoT) sensors and edge devices is a challenging task due to their limited energy and computational capabilities. Another challenge concerning IoT sensors or devices is the incursion of noise when used in an industrial environment. The noisy samples affect the decision support system that could lead to financial and operational losses. This article proposes a noisy encoder using artificial intelligence of things (NEAT) architecture for fault diagnosis in IoT edge devices. NEAT combines autoencoders and Inception module to co-train the clean and noisy samples for solving the said problem. Experimental results on benchmark data sets reveal that the NEAT architecture is noise resilient in comparison to the existing works. Furthermore, we also show that the NEAT architecture has lightweight characteristics as it yields a lower number of parameters, weight storage, training, and testing times that support its real-life applicability in an Industrial IoT environment.

PBidm: Privacy-Preserving Blockchain-Based Identity Management System for Industrial Internet of Things

Industrial Internet of Things (IIoT) is revolutionizing plenty of industrial applications by utilizing large-scale smart devices in manufacturing and industrial processes. However, IIoT is facing the disclosure of identity privacy. The identity information is precious and critical, thereby inspiring a line of follow-up privacy-preserving studies, i.e., anonymous credential protocols, or privacy-preserving identity management schemes. However, they are either too anonymous to be used in the IIoT environment, or the system is highly centralized, which implies the risk of a single point of failure. In this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PBidm</small> , a privacy-preserving blockchain-based identity management scheme for IIoT. Specifically, by leveraging blockchain and diversified cryptographic tools, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PBidm</small> can fully support the desirable properties, i.e., unforgeability, blindness, unlikability, traceability, revocability, and public verifiability. Then, we provide security analysis to ensure reasonable security assurance. Finally, we present a performance evaluation of the proposed scheme to demonstrate the practicability in IIoT applications.

A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems

Industry 5.0 aims at maximizing the collaboration between humans and machines. Machines are capable of automating repetitive jobs, while humans handle creative tasks. As a critical component of Industrial Internet of Things (IIoT) systems for service delivery, network data stream analytics often encounter concept drift issues due to dynamic IIoT environments, causing performance degradation and automation difficulties. In this paper, we propose a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept drift adaptation in IIoT systems, consisting of dynamic data pre-processing, the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic model learning & selection, and the proposed Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model. It is a complete automated data stream analytics framework that enables automatic, effective, and efficient data analytics for IIoT systems in Industry 5.0. Experimental results on two public IoT datasets demonstrate that the proposed framework outperforms state-of-the-art methods for IIoT data stream analytics.

An Image Security Model Based on Chaos and DNA Cryptography for IIoT Images

The images generated by smart cameras and sensors in an Industrial Internet of Things (IIoT) ecosystem are at significant risk when transmitted over a public network due to the dynamicity and open nature of the IIoT environment. Encryption is a viable method for safeguarding IIoT digital images. This article addresses an image security framework using chaotic maps and DNA cryptography. The proposed algorithm uses a tent, circle, Chebyshev, and 3-D logistic map in a multilevel fashion to generate three keys. These keys are used for row-column rotation of subblocks, determine the rule to perform DNA encoding–decoding on the subblocks, and generate a key image on which DNA XOR operation is performed to get the encrypted image. The proposed scheme’s result analysis illustrates that the average NPCR (99.6566%), UACI (33.4588%), Entropy (7.9971), and larger key-space of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 10^{195}$</tex-math></inline-formula> are better than the existing schemes and are resistant to different attacks.

Multivariate Time-Series Prediction in Industrial Processes via a Deep Hybrid Network Under Data Uncertainty

With the rapid progress of the industrial Internet of Things (IIoT), reducing data uncertainty has become a critical issue in predicting the development trends of systems and formulating future maintenance strategies. This article proposes an end-to-end, deep hybrid network-based, short-term, multivariate time-series prediction framework for industrial processes. First, the maximal information coefficient is adopted to extract the nonlinear variate correlation features. Second, a convolutional neural network with a residual elimination module is designed to eliminate data uncertainty. Third, a bidirectional gated recurrent unit network is connected in a time-distributed form to achieve step-ahead prediction. Last, an optimized Bayesian optimization method is adopted to optimize the model's learning rate. A comparison with other state-of-the-art, deep learning-based, time-series prediction methods in the case study illustrates the superiority of the proposed framework in noisy IIoT environments.

Application of internet of things-based efficient security solution for industrial

Recent years have seen a rise in demonstrations of the Industrial Internet of Things (IIoT). Creating secure Internet of Things (IIoT) applications has become a major challenge. Because of the constant demand for additional functionality, such as foresight, the number of connected devices in the enterprise facility is continually growing. Certifying a smaller number of signatories provides stronger authentication methods and prohibits trustworthy third parties from being publicly certified among the available encryption instruments. Certain network criteria must be met by the virtual network function (VNFs) used in this arrangement. As a result of this output, the final service may be delivered at the lowest possible cost, including processing time and network delay. According to the results of this study, our solution is better suited to simplifying trust management in IIoT devices. These findings reveal that our proposed confidence model may withstand behavioural shifts in hostile IIoT environments. The experimental results show our proposed method BCEPP has a high computational cost of 95.3%, a low latency ratio of 28.5%, and increased data transmitting rate of 94.1%, enhanced security rate of 98.6%, packet reception ratio of 96.1%, user satisfaction index of 94.5%, probability ratio of 33.8% when compared to other methods.

A Digital Twin-Based Heuristic Multi-Cooperation Scheduling Framework for Smart Manufacturing in IIoT Environment

Intertwining smart manufacturing and the Internet of Things (IoT) is known as the Industrial Internet of Things (IIoT). IIoT improves product quality and reliability and requires intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. Recently, it has been increasingly deployed; however, multi-party collaborative information processing is often required in heterogeneous IIoT. The security and efficiency requirements of each party interacting with other partners have become a significant challenge in information security. This paper proposes an automated smart manufacturing framework based on Digital Twin (DT) and Blockchain. The data used in the DT are all from the cluster generated after blockchain authentication. The processed data in the DT will only be accessed and visualized in the cloud when necessary. Therefore, all the data transmitted in the process are result reports, avoiding the frequent transmission of sensitive data. Simulation results show that the proposed authentication mode takes less time than the standard protocol. In addition, our DT framework for a smart factory deploys the PDQN DRL model, proving to have higher accuracy, stability, and reliability.

Energy efficient resource allocation method for 5G access network based on reinforcement learning algorithm

Edge computing and IIoT (Industrial Internet of Things) are two representative application scenarios in 5G (5th Generation) mobile communication technology network. Therefore, this paper studies the resource allocation methods for these two typical scenarios, and proposes an energy-efficient resource allocation method by using DRL algorithm, which enhances the network performance and reduces the network operation cost. Firstly, according to the characteristics of edge computing, taking the connection relationship between base stations, users and the transmission power allocated by base stations to users as decision variables, minimizing the overall energy efficiency as the goal, and taking the needs of mobile users as constraints, a resource allocation model for user quality of service (QoS) guarantee is designed. Secondly, simulation experiments are used to verify that the proposed method has faster training speed and convergence speed, ensures the quality-of-service requirements of mobile users, and realizes intelligent and energy-saving resource allocation. The approximate amounts of steps to convergence under four cases are 500, 350, 250, and 220, respectively, and the values of awards are 21.76, 21.09, 20.38, and 20.25, respectively. Thirdly, aiming at the IIoT environment scenario, this paper proposes a resource allocation method for 5G wide connection low delay services based on asynchronous dominant action evaluation algorithm (A3C). Firstly, according to the characteristics of IIoT environment, taking the connection relationship between base stations and users and the transmission power allocated by base stations to users as decision variables and maximizing energy efficiency while satisfying needs of each user as optimization goal, a resource allocation model for 5G wide connection low delay services is designed. On this basis, an energy-efficient resource allocation method according to A3C is proposed. The hierarchical aggregation clustering algorithm (HAC) is adopted to determine the connection relationship between the base station and users, and the A3C algorithm is adopted to allocate transmission power to users, so as to maximize the overall energy efficiency and ensure the needs of each user.

TRUTH: Trust and Authentication Scheme in 5G-IIoT

Due to the extremely important role of data in the industrial Internet of Things (IIoT) network, trust and security of data are among the major concerns. In this article, we develop a cloud-integrated 5G-IIoT network architecture enabled by a three-party authenticated key exchange (AKE) protocol with privacy-preserving to secure data exchanged via wireless communication, cope with unauthorized entities, and ensure data integrity. Moreover, we develop a trust model based on the Dempster–Shafer theory to check the trustworthiness of data collected by smart devices/sensor nodes. Security analysis performed on our scheme demonstrates that it can withstand different well known attacks in the IIoT environment. We also analyzed the validity of our scheme by using the automated validation of internet security protocols and applications tool. Additionally, the performance evaluation and experimental results prove the effectiveness of the proposed scheme compared to the existing works in terms of accuracy, delay, trust, and throughput.

Industrial Vision: Rectifying Millimeter-Level Edge Deviation in Industrial Internet of Things With Camera-Based Edge Device

Nowadays, to realize the intelligent manufacturing in Industrial Internet of Things (IIoT) scenarios, novel approaches in computer vision are in great demand to tackle the new challenges in IIoT environment. These approaches, which we call <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Industrial Vision</i> , are expected to offer customized solutions for intelligent manufacturing in an accurate, time efficient and robust manner. In this paper, we propose a novel approach to industrial vision, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Edge-Eye</i> , to rectify the edge deviation automatically for Irradiated Cross-linked Polyethylene Foam (IXPE) production with millimeter-level accuracy. IXPE has been one of the most commonly used materials in industry. During the production process of IXPE sheets, their edges need keep aligned strictly, otherwise, they could quickly get out of the border of the rolling plate and cause the huge economic loss. We deploy a commercial camera with mobile edge node in front of the IXPE sheet to continuously detect and rectify the edge deviation. Particularly, to handle the complex production environment when extracting the edge of IXPE sheet, we deploy a pair of reference bars with high-contrast colors to efficiently differentiate the sheet edge from the background. Then, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bi-direction Edge Tracking method</i> to perform the edge detection from both vertical and horizontal aspects. To realize the rectification using mobile edge nodes with limited computing resources, we reduce the cost of computation by extracting the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Minimized Region of Interest</i> , i.e., the edge area overlapped with the higher contrast reference bar on both sides. We further design a negative feedback control system with multi-stage feedback regulation mechanism, keeping the edge deviation within <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">millimeter-level</i> . We implemented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Edge-Eye</i> on the ARM64 platform and performed evaluation in the practical IXPE production process. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Edge-Eye</i> achieves the average accuracy of 5 mm for the edge deviation rectification, with the average latency of 200 ms for edge deviation detection. During the process of 20-month real deployment for 36 production lines, 66 manpower per day (90% of the overall manpower) has been saved, and the utilization rate of IXPE material increases from 87% to 94%.

Voting-based Ensemble Learning approach for Cyber Attacks Detection in Industrial Internet of Things

The Industrial Internet of Things (IIoT) is a fast developing interconnection of smart technologies that have revolutionised the way the industries work by combining sensors and devices by automating their regular operations through Internet of Things (IoT). In various application domains, IoT devices provide seamless connectivity and diversity. Because of their round-the-clock connectivity these systems and their communication channel are subjected to targeted cyber-attacks. As a result, a multi-level security solution is required to protect the industrial system. An Intrusion Detection System (IDS) can be used to counteract these cyber-attacks by analyzing the data packets for any targeted attacks in the IIoT environment. This paper proposes a cyber-attacks detection framework for IIoT by using the Voting-based Ensemble Learning approach. In the proposed framework, an ensemble of the latest and classical Machine Learning (ML) techniques like Histogram Gradient Boosting (HGB), CatBoost, and Random Forest (RF), and a hard voting classifier are employed for efficient detection of cyber-attacks. In our proposed model, CatBoost exceeds the other two with an accuracy of 99.85 %, while HGB and RF have an accuracy of 97.90 % and 98.83 % respectively.

Mitigating Malicious Adversaries Evasion Attacks in Industrial Internet of Things

With advanced 5G/6G networks, data-driven interconnected devices will increase exponentially. As a result, the Industrial Internet of Things (IIoT) requires data secure information extraction to apply digital services, medical diagnoses, and financial forecasting. This introduction of high-speed network mobile applications will also adapt. As a consequence, the scale and complexity of Android malware are rising. Detection of malware classification is vulnerable to attacks. A fabricated feature can force misclassification to produce the desired output. This article proposes a subset feature selection method to evade fabricated attacks in the IIoT environment. The method extracts application-aware features from a single android application to train an independent classification model. Ensemble-based learning is then used to train the distinct classification models. Finally, the collaborative ML classifier makes independent decisions to fight against adversarial evasion attacks. We compare and evaluate the benchmark Android malware dataset. The proposed method achieved 91% accuracy with 14 fabricated input features.

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.