Industrial Internet Of Things Scenarios Research Articles

Predictive maintenance of vehicle fleets through hybrid deep learning-based ensemble methods for industrial IoT datasets

Abstract Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant increase in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results.

AoI-Aware Resource Scheduling for Industrial IoT with Deep Reinforcement Learning

Effective resource scheduling methods in certain scenarios of Industrial Internet of Things are pivotal. In time-sensitive scenarios, Age of Information is a critical indicator for measuring the freshness of data. This paper considers a densely deployed time-sensitive Industrial Internet of Things scenario. The industrial wireless device transmits data packets to the base station with limited channel resources under the constraints of Age of Information. It is assumed that each device has the capacity to store the packets it generates. The device will discard the data to alleviate the data queue backlog when the Age of Information of the data packet exceeds the threshold. We developed a new system utility equation to represent the scheduling problem and the problem is expressed as a trade-off between minimizing the average Age of Information and maximizing network throughput. Inspired by the success of reinforcement learning in decision-processing problems, we attempt to obtain an optimal scheduling strategy via deep reinforcement learning. In addition, a reward function is constructed to enable the agent to achieve improved convergence results. Compared with the baseline, our proposed algorithm can achieve better system utility and lower Age of Information violation rate.

Anomaly Detection of Service Function Chain Based on Distributed Knowledge Distillation Framework in Cloud–Edge Industrial Internet of Things Scenarios

Anomaly Detection of Service Function Chain Based on Distributed Knowledge Distillation Framework in Cloud–Edge Industrial Internet of Things Scenarios

DANTD: A Deep Abnormal Network Traffic Detection Model for Security of Industrial Internet of Things Using High-Order Features

With the development of blockchain, artificial intelligence and data mining technology, abnormal network traffic data has become easy to obtain. The traffic detection model detects the traffic patterns in the network to find abnormal traffic that does not conform to the normal traffic law, which has great security significance for Industrial Internet of Things (IIoT) networks and devices in real scenarios. However, previous abnormal detection models rely on expert experience and cannot cope with real-time changes in IIoT scenarios. The manual features cannot be sufficiently representative and adaptive. Moreover, there are few abnormal traffic data in real scenarios, which makes the model unable to fully learn the potential distribution in abnormal data. Therefore, in this work, we propose a deep abnormal network traffic detection model (DANTD) for security of IIoT using high-order features and novel data augmentation strategies. The DANTD model first adopts a deep convolutional autoencoder to extract effective high-order features to make it more representative. Then the DANTD model uses generative adversarial networks as data augmentation strategies to enrich the abnormal data, so that the model can fully consider the information of the data distribution. Comprehensive experiments on real IIoT datasets validate the effectiveness of the DANTD model.

Partial message verification in fog-based industrial Internet of things

Due to the emergence of fog computing, the computational resources can be deployed close to the data source and conduct data-driven tasks. A typical scenario is integrity assurance in Industrial Internet of Things (IIoT). However, since the devices in IIoT scenario have low computation capability, they cannot verify the integrity of all incoming messages using traditional message authentication code (MAC), especially when the messages are in large volume and high frequency. In this paper, we propose a novel MAC scheme, namely, Partial Verification Message Authentication Code (PV-MAC), to partially offload the verification tasks to a device with more processing power, (e.g., a fog node). Moreover, the fog node can transparently choose parts of contents in the message for verification on behalf of the device that uses these data. Afterward, the user device can verify the integrity of the rest contents. The theoretical analysis and the experimental results show that our proposed schemes can achieve high efficiency and dynamic workload balancing while retaining a rigorous security guarantee.

RIS-aided dynamically adaptive wide field-of-view receiver for visible light communication in industrial internet of things.

In the current visible light communication (VLC) system, a condenser lens is generally used in the front of receiver to achieve a higher data rate, making an extremely narrow field-of-view for the receiver. With the spread of Industrial Internet of Things (IIoT), the communication between mobile terminals is urgently required. A wide-range detecting method for VLC system in IIoT scenario is asked. In this paper, a novel self-adaptive wide-FoV receiver involving reconfigurable intelligent surfaces (RIS) is proposed. The effective detecting range of the receiver can be expanded by dynamically adjusting the incident light directions with the assistance of RIS. Based on the maximum arrived flux criterion, the mathematical model is established and the optimized RIS parameter tuning algorithm is presented. The feasibility and validity of the method are verified by simulation. The results show that the tolerable transceiver offset can be increased to 2∼4 times as the conventional receiver.

Preserving Privacy in Industrial IoT: A Machine Learning Framework Enhanced by Poly1305 Encryption

Abstract: The convergence of Industrial Internet of Things (IoT) systems and machine learning has ushered in new paradigms for predictive maintenance, anomaly detection, and quality control in industrial sectors. Though, the sensitive nature of data generated by IoT devices raises significant privacy and security concerns. To address these challenges, this review paper presents a thorough examination of safeguarding privacy machine learning frameworks in the context of Industrial IoT environments, leveraging the robust Poly1305 encryption algorithm. This paper commences by surveying the landscape of Industrial IoT applications, highlighting the urgency of safeguarding sensitive data without compromising the potential insights gleaned from machine learning models. It then delves into the intricacies of Poly1305 encryption, elucidating its strengths in ensuring data integrity, authenticity, and confidentiality. The integration of Poly1305 encryption within machine learning pipelines is explored, considering encryption at data collection, transmission, storage, and analysis stages. The review systematically analyses a spectrum of data learning models deployed in Industrial IoT scenarios, spanning predictive maintenance to quality enhancement. Each model's compatibility with Poly1305 encryption is assessed, along with performance implications and security guarantees. The paper navigates through key obstacles including key management, computational overhead, and interoperability concerns. In summation, this review paper not only serves as a comprehensive guide for researchers and practitioners in the realm of Industrial IoT and machine learning but also contributes to the ongoing discourse on safeguarding data privacy while harnessing the transformative power of data-driven insights

Channel measurement and characterisation for 5G multi‐scenarios at 26 and 38 GHz

AbstractMillimetre‐wave (mmWave) frequencies play a vital role in fifth‐generation (5G) wireless systems and beyond due to the vast available bandwidth of several GHz. This paper presents channel characteristics and their channel models for mmWave based on extensive channel measurements at 26 and 38 GHz conducted in 5G scenarios, such as the rooftop, the high‐speed railway, and the Industrial Internet of Things scenarios. The channel sounder receiver (Rx) uses an omnidirectional or phased array antenna to meet the requirements of channel measurement, such as recording absolute delay and obtaining 3‐dimensional angular information. We use the classical close‐in model and floating‐intercept model to model path loss. Meanwhile, channel statistics in the delay domain are derived from the measured power delay profiles. Note that the different scenarios are measured with the same channel sounder, making the measurement results of different scenarios comparable. It is shown that the shadow fading parameter σ is 0.7 dB as fewer large scatterers exist in the rooftop scenario. Moreover, due to the significant dependence of delay spread on the geometric relationship between the transmitter (Tx), Rx, and surrounding environment, the statistical data in the delay domain varies significantly in different scenarios. The channel characteristics and models will guide future air‐interface, beamforming, and transceiver designs for 5G and beyond.

Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT

Integrated sensing and communication (ISAC) in the Industrial Internet of Things (IIoT) presents unique challenges in terms of localization techniques. While three-dimensional (3D) environments offer extra challenges to enhanced accuracy and realism, research in this area remains limited. To bridge this gap, we propose a novel localization technique assisted by a single base station (BS) in 3D IIoT scenarios. Our approach employs the MUltiple SIgnal Classification (MUSIC) algorithm to jointly estimate the angle of arrival (AoA) in azimuth and elevation, as well as the time of arrival (ToA). Compared to conventional multi-BS-assisted or MUSIC-based algorithms, our technique offers flexibility, easy implementation, and low computational cost. To improve performance, we integrate the Taylor-series into the iterative process after a MUSIC-based joint azimuth, elevation angle and delay estimation (JAEDE), resulting in a significant 99% reduction in computational complexity compared to a two-step MUSIC-based approach utilizing coarse-fine grid searching. Through numerical simulations, we compare our algorithm with three other MUSIC-based joint or separate estimation approaches, demonstrating its superior performance in azimuth angle of arrival (AoAz), elevation angle of arrival (AoAe), TOA, and overall location estimation across varying signal-to-noise ratio (SNR) conditions.

PDPoSt+sPBFT: A High Performance Blockchain-Assisted Parallel Reinforcement Learning in Industrial Edge-Cloud Collaborative Network

With the increasing demand for resource scheduling efficiency in Industrial Internet of Things (IIoT), parallel reinforcement learning (PRL) based distributed edge-cloud collaborative resource scheduling scheme has attracted enormous attention. However, the computing and communication capacities, the security degree of massive distributed edge computing servers are different. It is difficult to make a large number of edge servers carry out security and efficiency PRL based edge-cloud collaboration resource scheduling scheme. Thus, in this paper, a large-scale distributed edge-cloud collaborative resource scheduling method based on picture delegated proof of state and suspicious practical byzantine fault tolerance (pDPoSt+sPBFT) consensus algorithm is proposed. To be specific, we first propose a collaborative edge-cloud industrial network architecture to support massive industrial intelligence tasks, then a distributed PRL based resource allocation scheme is utilized. Secondly, in order to improve the efficiency and security of distributed PRL training, we propose a server filtering strategy based on pDPoSt algorithm. Finally, a sPBFT algorithm is proposed to further realize security parameter aggregation of distributed PRL. Experimental results show that the proposed method has good efficiency and security performance compared with the traditional distributed edge-cloud collaborative resource scheduling algorithm. The proposed approach has great potential in complex IIoT scenarios.

Intelligent Computation Offloading and Resource Allocation in IIoT With End-Edge-Cloud Computing Using NSGA-III

The Industrial Internet of things (IIoT), which consists of massive IoT devices and wireless access points in industrial infrastructures to acquire intelligent services, has been considered as a vital physical information platform for implementing Industry 4.0. Further, mobile edge computing (MEC) has brought an opportunity to accelerate the development of IIoT. However, the existing MEC methods may not be directly used for IIoT scenarios due to the large size of IIoT devices and the characteristics of the applications, as well as the limited and heterogeneous resources of edge servers. In view of this, the computation offloading and resource allocation are formulated as a multi-objective optimization problem, and an end-edge-cloud collaborative intelligent optimization method is devised in this paper. Comprehensive experiments and evaluations are carried out to prove the effectiveness and efficiency of our proposed method with regard to the energy consumption and time consumption of IIoT devices, as well as resource utilization and load balancing of edge servers.

Spatiotemporal Self-Attention-Based Network Traffic Prediction in IIoT

The sixth-generation (6G) mobile communications are considered as a future network and very closed to the Industrial Internet of Things (IIoT) due to its low latency and high throughput. Massive nodes supported by 6G make up the complexity of the network. Moreover, the heterogeneous traffic brings difficulties to the network management. Long-term network traffic matrix (TM) prediction is a crucial technology for realizing network edge intelligence and dealing with the above issues. However, predicting long-term network traffic in heterogeneous IIoT is challenging. Due to the powerful feature extraction capability over long sequences, self-attention is widely applied in language inference tasks. Motivated by these observations, we propose a self-attention traffic matrix prediction (SATMP) model for long-term network TM prediction in IIoT scenarios. SATMP consists of three components: (a) a spatial–temporal encoding for obtaining the spatial–temporal features of network TM; (b) a learnable positional encoding for providing positional correlation to the traffic sequence; and (c) a self-attention module for capturing long-term dependence. These components work together to enhance long-term prediction performance in complex networks effectively. Extensive experiments on three publicly available datasets demonstrate that SATMP is feasible and accurate in IIoT long-term network TM prediction.

Designing a high-fidelity testbed for 5G-based Industrial IoT

With the rise of the Industrial IoT (Internet of Things) and Industry 4.0 paradigms, many control and sensor systems used for IACS (Industrial Automation and Control Systems) have become more complex, due to the increasing number of interconnected field devices, sensors and actuators often being geographically spread across large areas. Supporting these increasingly sophisticated networked scenarios calls for the involvement of telecommunications and utility providers to better support Machine-to-Machine (M2M) communications and infrastructure orchestration, for which 5G technology is considered a perfect match. Nowadays, such 5G networks empower solutions both for consumer and for industrial IoT scenarios, providing the capacity and the means to seamlessly connect a massive number of gadgets and sensors, with diverse data rate requirements, low latency, and low power consumption. Part of this flexibility is also due to the nature of the 5G Service Architecture (SA), which is based on a microservice concept, dividing its core through multiple functions, allowing it to horizontally scale in a flexible way. Furthermore, the 3GPP specifications encompass specific support for verticals by means of slicing and 5G LANs, paving the way for a paradigm shift in terms of the relationship between service, telecom, and operational infrastructure tenants. However, such benefits come at the cost of extra complexity and, consequently, an increased vulnerability surface. This calls for further research focused on improving 5G infrastructure management, service integration and security, which cannot be safely undertaken in production environments, thus motivating the development of suitable 5G testbeds. This research work, which was developed in the scope of the POWER and Smart5Grid P2020 projects, addresses the creation of a high-fidelity environment for 5G-related research, which encompasses a gNodeB and 5G core, together with emulated User Elements (terminal devices) and IoT nodes (in this specific case, Programmable Logic Controllers), constituting a 5G Industrial IoT scenario designed for development and validation of new solutions, security research, or even advanced training purposes. The entire infrastructure is supported via container orchestration technology, providing enhanced scalability and resilience characteristics.

Intelligent VNF Placement to Mitigate DDoS Attacks on Industrial IoT

The Internet of Things (IoT) has undergone rapid popularization, reaching a wide range of application domains, such as manufactures. Hence, more and more heterogeneous IoT devices have been deployed in a variety of industrial environments, progressively becoming common objects to the supply chain. The physical infrastructure of manufacturing systems has become complex and requires efficient and dynamic solutions for managing network performance and security. Network Function Virtualization (NFV) has attracted attention when the intention is to respond to security threats on Industrial IoT (IIoT). Few works use NFV to detect and mitigate security threats on IIoT networks, but even less consider performance indicators of the network context when placing the Virtual Network Functions (VNFs). Thus, this work introduces a Machine Learning (ML) approach to place security VNFs based on NFV performance to mitigate Distributed Denial of Service (DDoS) attacks on IIoT. Experiments considering a new composed data set and diverse ML techniques show ML classification as an alternative for IIoT scenarios, achieving, according to the best-performing technique, 99.40% of accuracy in relation to the ideal placement. To facilitate the reproduction of the work, all the code and data produced are publicly available.

Cross-Domain Access Control Model in Industrial IoT Environment

The Industrial Internet of Things (IIoT) accelerates smart manufacturing and boosts production efficiency through heterogeneous industrial equipment, intelligent sensors, and actuators. The Industrial Internet of Things is transforming from a traditional factory model to a new manufacturing mode, which allows cross-domain data-sharing among multiple system departments to enable smart manufacturing. A complete industrial product comes from the combined efforts of many different departments. Therefore, secure and reliable cross-domain access control has become the key to ensuring the security of cross-domain communication and resource-sharing. Traditional centralized access control schemes are prone to single-point failure problems. Recently, many researchers have integrated blockchain technology into access control models. However, most blockchain-based approaches use a single-chain structure, which has weak data management capability and scalability, while ensuring system security, and low access control efficiency, making it difficult to meet the needs of multi-domain cooperation in IIoT scenarios. Therefore, this paper proposes a decentralized cross-domain access model based on a master–slave chain with high scalability. Moreover, the model ensures the security and reliability of the master chain through a reputation-based node selection mechanism. Access control efficiency is improved by a grouping strategy retrieval method in the access control process. The experimental benchmarks of the proposed scheme use various performance metrics to highlight its applicability in the IIoT environment. The results show an 82% improvement in the throughput for the master–slave chain structure over the single-chain structure. There is also an improvement in the throughput and latency compared to the results of other studies.

Channel Characteristics and Link Adaption for Visible Light Communication in an Industrial Scenario.

Visible light communication (VLC) is one of the key technologies for the sixth generation (6G) to support the connection and throughput of the Industrial Internet of Things (IIoT). Furthermore, VLC channel modeling is the foundation for designing efficient and robust VLC systems. In this paper, the ray-tracing simulation method is adopted to investigate the VLC channel in IIoT scenarios. The main contributions of this paper are divided into three aspects. Firstly, based on the simulated data, large-scale fading and multipath-related characteristics, including the channel impulse response (CIR), optical path loss (OPL), delay spread (DS), and angular spread (AS), are analyzed and modeled through the distance-dependent and statistical distribution models. The modeling results indicate that the channel characteristics under the single transmitter (TX) are proportional to the propagation distance. It is also found that the degree of time domain and spatial domain dispersion is higher than that in the typical rooms (conference room and corridor). Secondly, the density of surrounding objects and the effects of user heights on these channel characteristics are also investigated. Through the analysis, it can be observed that the denser objects can contribute to the smaller OPL and the larger RMS DS under the single TX case. Furthermore, due to the blocking effect of surrounding objects, the larger OPL and the smaller RMS DS can be observed at the receiver with a low height. Thirdly, due to the distance dependence of the channel characteristics and large time-domain dispersion, the link adaption method is further proposed to optimize the multipath interference problem. This method combines a luminary adaptive selection and delay adaption technique. Then, the performance of the link adaption method is verified from four aspects through simulation, including the signal-to-noise (SNR), the RMS DS, the CIRs, and the bit-error rate (BER) of a direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) system. The verification results indicate that our proposed method has a significant optimization for multipath interference.

FSL: federated sequential learning-based cyberattack detection for Industrial Internet of Things

Industrial Internet of Things (IIoT) brings revolutionary technical supports to modern industries. However, today’s IIoT still faces the challenges of modeling varying time-series in common data isolation while considering data security. To accurately characterize industrial dynamics, we propose a possible solution based on federated sequence learning (FSL) with cyber attack detection capabilities. Under a federated framework, FSL constructs a collaborative global model without violating local data integrity. Taking advantages of the locally sequential modeling, FSL captures the intrinsic industrial time-series responses. Furthermore, data heterogeneity among distributed clients is also considered, which is important to maintenance a robust but sensitive attack detection. Experiments on classic distributed datasets demonstrate that FSL is capable to accurately model data heterogeneity caused by data isolation and dynamics of time-series. Real IIoT attack detection experiments using a distributed testbed show that our FSL provides better detection performances for industrial time-series sensory data compared to existing methods. Therefore, the proposed attack detection approach FSL is promising in real IIoT scenarios in terms of feasibility, robustness and accuracy.

Dealing with applications in Industrial IoT

Industrial Internet of Things (IIoT) also known as Industry 4.0, consists of industries that utilize IoTs to facilitate logistics, manufacturing, transportation, oil and gas, energy/utilities, mining and metals, aviation, and others. Though a lot of companies have proceeded into a lot of areas utilizing IIoT, its applicability in a lot of is still not explored and remains unpredictable. Its evolution is similar to that of the Internet in the early 90s, where it initially appeared and slowly gained momentum in the 20th century to almost becoming and indispensable aspect of human life in the 21st century. Robotics and control are already a part of most of the manufacturing companies, however, when it comes to real-time applications in manufacturing, IIoT may not come to the rescue. Another aspect that separates the internet and IIoT is that the internet is based on the bet effort delivery model which is acceptable for e-commerce and man-machine interactions. However, failures in an IIoT scenario such as air-traffic monitoring, automated manufacturing can have serious consequences. Industry 4.0 is an initiative taken up by industries to use IoT in industries and many such initiatives have been taken up by different companies and countries. For example, GE has already invested $1billion to improve its asset performance and business operation by using IIoT services. Germany has started an initiative called “Industry 4.0”, a strategic initiative that brought together leaders from private and public sectors as well as from academia to create a detailed plan of action for incorporating digital technologies into German industries. Other countries such as Italy, Netherlands, and France have invested in setting up smart factories and industries. China has also started a project called “Made in China 2025” to integrate digital technologies and industrialization. A major hindrance in adopting IIoT is that the smart devices often speak different “languages” that existing industrial automation software uses, which creates a communication gap, and hence standardization is necessary. Overcoming such bottlenecks and adopting IIoT can help automate the production techniques, for instance, manufacturers could increase their productivity by a margin of 30%. The capabilities of IIoT can be explored in areas such as sensor driven computing, industrial analytics, and intelligent machine applications. The main aim of this topic is to explain various applications inf IoT in industries.

A Delay-Sensitive Multibase-Station Multichannel Access System for Smart Factory

The intelligence of industrial production depends on real-time and reliable transmission and timely feedback of information in the Industrial Internet of Things (IIoT) systems. However, the scarcity of spectrum resources, a harsh industrial electromagnetic environment, and a need for real-time processing of massive traffic have posed severe challenges to the real-time and reliable access of the MAC layer. Aiming to solve the real-time transmission problem in the IIoT system, a delay-sensitive multibase-station multichannel access scheme is proposed. First, a multibase-station multichannel access model for real-time transmission is developed. The way to match sensing nodes with base stations and channels is formulated as a delay-sensitive multibase-station multichannel access (DMMA) problem. Moreover, it is theoretically proved that the DMMA problem is NP-Hard. Finally, a delay-sensitive greedy schedule (DG-Schedule) scheme based on the minimum deadline priority is proposed to obtain a feasible solution to the DMMA problem. According to experiments on the TI CC2530, DG-Schedule can reduce packet loss by 11% and average latency by 88% compared to CSMA/CA. Therefore, the DG-Schedule scheme is more suitable for IIoT scenarios.

ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

In recent years, the Industrial Internet of Things (IIoT) has led to significant steps forward in many industries, thanks to the exploitation of several technologies, ranging from Big Data processing to artificial intelligence (AI). Among the various IIoT scenarios, large-scale data centers can reap significant benefits from adopting Big Data analytics and AI-boosted approaches since these technologies can allow effective predictive maintenance. However, most of the off-the-shelf currently available solutions are not ideally suited to the high-performance computing (HPC) context, e.g., they do not sufficiently take into account the very heterogeneous data sources and the privacy issues that hinder the adoption of the cloud solution, or they do not fully exploit the computing capabilities available in loco in a supercomputing facility. In this article, we tackle this issue, and we propose an IIoT holistic and vertical framework for predictive maintenance in supercomputers. The framework is based on a big lightweight data monitoring infrastructure, specialized databases suited for heterogeneous data, and a set of high-level AI-based functionalities tailored to HPC actors’ specific needs. We present the deployment and assess the usage of this framework in several in-production HPC systems.