Industrial Internet Of Things Sensors Research Articles

Harnessing Automation and IIoT in Air Traffic Control for Advanced Efficiency and Safety

The combination of automation and Industrial Internet of Things (IIoT) technology has transformed air traffic control (ATC), improving efficiency and safety in aviation. This article investigates the varied influence of automation and IIoT on ATC operations, emphasizing their importance in maximizing airspace use, boosting situational awareness, and limiting operational hazards. The advent of automatic flight data processing, radar surveillance, and automated decision-support technologies has reduced controller workload and increased operational efficiency in ATC. IIoT sensors and devices enable ATC systems to integrate real-time data on aircraft positions, weather, and runway status, allowing controllers to make more informed decisions and manage air traffic flow. Finally, automation and IIoT have evolved into critical tools for upgrading air traffic control, assisting controllers in managing airspace congestion, optimizing operational performance and safely and quick, efficient traffic movement. This study examines how automation and IIoT alter ATC operations, resulting in a more efficient, robust, and sustainable aviation environment. Keywords— Industrial Internet of Things (IIoT), Air Traffic Control (ATC), Automation, Operational Hazards, Radar surveillance, IoT sensors, Aviation environment.

Assessing Factory's Industry 4.0 Readiness: A Practical Method for IIoT Sensor and Network Analysis

Manufacturing industries are aware of the benefits of Industry 4.0 (I4.0) and the pivotal role of the Industrial Internet of Things (IIoT) to facilitate the transition. However, they often underestimate the intricate connection between the implementation of IIoT and their existing sensing and communication systems. Hindering their progress towards I4.0 implementation, many factories still rely on rigid sensing systems, characterized by limited adaptability and inflexibility. This raises questions regarding the industrial readiness for I4.0 implementation and its ability to meet the sensing and connectivity standards of IIoT. Specifically, it is unclear whether the current brownfield installations, consisting of sensors and networking systems, adequately support the demanding I4.0 applications that necessitate a multi-functional and collaborative IIoT system. Our objective is to assess a factory's progress towards I4.0 by examining the readiness of its sensing system and the communication capabilities of its network. We propose a two-stage analysis of factory readiness, including a functional segment-based sensor analysis and an application class-based network analysis. We present a case study conducted at an iron-making plant in The Netherlands to illustrate our method. Key findings of the case study include: (1) a lack of multi-functionality across segments for the majority of sensors (90%), (2) a considerable portion of network traffic (73%) requires high reliability, and (3) only 3% of the current network traffic necessitate ultra-reliable, low latency communication. Furthermore, we discuss how our method provides decision-makers with valuable guidance for the digital transformation of established and newly built manufacturing industries.

Binary-Convolution Data-Reduction Network for Edge–Cloud IIoT Anomaly Detection

Industrial anomaly detection, which relies on the analysis of industrial internet of things (IIoT) sensor data, is a critical element for guaranteeing the quality and safety of industrial manufacturing. Current solutions normally apply edge–cloud IIoT architecture. The edge side collects sensor data in the field, while the cloud side receives sensor data and analyzes anomalies to accomplish it. The more complete the data sent to the cloud side, the higher the anomaly-detection accuracy that can be achieved. However, it will be extremely expensive to collect all sensor data and transmit them to the cloud side due to the massive amounts and distributed deployments of IIoT sensors requiring expensive network traffics and computational capacities. Thus, it becomes a trade-off problem: “How to reduce data transmission under the premise of ensuring the accuracy of anomaly detection?”. To this end, the paper proposes a binary-convolution data-reduction network for edge–cloud IIoT anomaly detection. It collects raw sensor data and extracts their features at the edge side, and receives data features to discover anomalies at the cloud side. To implement this, a time-scalar binary feature encoder is proposed and deployed on the edge side, encoding raw data into time-series binary vectors. Then, a binary-convolution data-reduction network is presented at the edge side to extract data features that significantly reduce the data size without losing critical information. At last, a real-time anomaly detector based on hierarchical temporal memory (HTM) is established on the cloud side to identify anomalies. The proposed model is validated on the NAB dataset, and achieves 70.0, 64.6 and 74.0 on the three evaluation metrics of SP, RLFP and RLFN, while obtaining a reduction rate of 96.19%. Extensive experimental results demonstrate that the proposed method achieves new state-of-the-art results in anomaly detection with data reduction. The proposed method is also deployed on a real-world industrial project as a case study to prove the feasibility and effectiveness of the proposed method.

Traffic-Aware 6TiSCH Routing Method for IIoT Wireless Networks

The 6TiSCH network is a key technology of the Industrial Internet of Things (IIoT), providing high reliability and deterministic features. However, IIoT sensors and facility applications generate heavy traffic loads. Various time-slotted channel hopping (TSCH) medium access layer (MAC) and routing protocols for low-power and lossy network (RPL) methods have been studied at each network layer to address this issue. Although TSCH MAC is standard for IIoT low-rate wireless networks, it has not been adequately considered in conventional RPL studies. Conventional RPL methods do not effectively utilize the characteristics of the TSCH MAC in heavy-traffic networks and may hinder performance. In this article, we introduce a traffic-aware (TA) RPL method that utilizes the cell allocation information of TSCH MAC. The number of allocated TSCH cells determines the bandwidth reserved by each node and includes the link quality and traffic information measured by the MAC layer. TA RPL facilitates efficient load balancing using cell allocation information and improves the bandwidth utilization of the network. Additionally, the proposed method can improve the deterministic features of the IIoT wireless network because the effect of the routing decision of each device can be predicted. Moreover, the packet delivery ratio and bandwidth utilization are improved by up to 31% and 20%, respectively, compared with the conventional methods based on simulation and testbed evaluations.

Blockchain-based trust mechanism for digital twin empowered Industrial Internet of Things

Emerging technologies such as blockchain and digital twins are essential for the rapid development and employment in Industry 5.0 revolution. With the growing number of industrial IoT nodes, optimizing the network and availing of limited resources to enable secure transmission is difficult. A digital twin is the virtual representation of a physical device that completely depends on sensor data for the critical decision-making simulation process. To this end, we combine a blockchain-based distributed network with a digital twin for the Industrial Internet of Things (IIoT) applications. This paper proposes a blockchain-based Proof of Authority (PoA) trust mechanism to provide high-quality services such as security and data privacy in IIoT. Furthermore, to enhance the authority of decentralized digital twin combined blockchain networks, we introduce a Deterministic Pseudo-Random Generation (DPRG) to generate a genesis block. To evaluate the trustworthiness of the proposed system, we simulate the blockchain network with a digital twin using IIoT sensor nodes. The simulation result shows that the PoA-based authority master nodes can reduce energy consumption and enhance data security.

Personalized Privacy-Aware Task Offloading for Edge-Cloud-Assisted Industrial Internet of Things in Automated Manufacturing

Industrial Internet of Things (IIoT) devices are widely used for monitoring and controlling the process of automated manufacturing. Owing to the limited computing capacity of the IIoT sensors in the production line, the scheduling task in the production line needs to be offloaded to the edge computing server (ECS). To obtain the desired quality of service (QoS) during offloading scheduling tasks, the precise interaction information between the production line and ECSs has to be uploaded to the cloud platform, which poses privacy issues. The existing works mostly assume that all the interaction information, i.e., the offloading decision for the subtask in a scheduling task, has same privacy level, which cannot meet the various privacy requirements of the offloading decision for the subtask. Hence, we propose a local-differential-privacy-based deep reinforcement learning (LDP-DRL) approach in the edge-cloud-assisted IIoT to provide personalized privacy guarantee. The LDP mechanism can generate different levels of noise to satisfy the various privacy requirements of the offloading decision for the subtask. The prioritized experience replay is integrated in DRL to reduce the impact of noise on the QoS performance of task offloading. The formal analysis of LDP-DRL is provided in terms of privacy level and convergence. Finally, extensive experiments are conducted to evaluate the effectiveness, the capacity of privacy protection, the impact of discount factor on the convergence, and the cost efficiency of the LDP-DRL approach.

IIoT Based Anomaly Detection and Maintenance Management of an Industrial Rotary System

Numerous challenges are being faced by implementing the Industrial Internet of Things (IIoT), which enables anomaly detection and optimal maintenance management of industrial production systems and their critical machines. Industries have started adopting this revolutionary technology along with other allied technologies to reap the full benefits out of Industry 4.0 environment. The objectives of this research work were to use the IIoT to develop a continuous monitoring system of the behavior of bottleneck facilities in a production system, to predict and avoid all possible failures, and to improve the overall productivity of the manufacturing system. The proposed prognostic health monitoring system employs IIoT sensors to measure the current values of the operating parameters of a machine, using built-in intelligent decision support mechanism to compare with optimal ranges of values, and to message the appropriate alarming signals as per the severity of the deviation. The system developed was tested with a prototype model comprising the Internet of Things, internet communication technology, and a machine learning algorithm, MEMS with standard input, output, storage and display of components, which was developed in a laboratory but implemented in a case study in a real industrial production plant. After the successful implementation of the developed system, the performance of the critical machine was evaluated in terms of metrics such as the average number of failures, average downtime and average service time spent. It was found that after the implementation, the downtime has decreased by nearly 22% and for the performance in terms of its output, the flow rate exhibited a steady increase with the passage of time.

A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications

With the real restriction of packet size and transmission power, a joint UL-AoA (Uplink Angle of Arrival) and UL-TDOA (Uplink Time Difference of Arrival) positioning method is discussed for the Industrial Internet of Things (IIoT), which demands high accuracy of position information. One motivation of the UL positioning method of IIoT is an asymmetric network and uplink traffic is dominant in most identified scenarios. Further, IIoT sensors are power limited for low cost, compared to the transmission power of base stations and other normal user equipment. This paper considers an environment of a 5G-enabled IIoT network with massive Multiple input multiple output (MIMO) antennas, beamforming and millimeter operating bands. After reviewing downlink positioning reference signal (PRS) and uplink sounding reference signal for positioning (SRS-Pos), a general system model and a theoretical localization problem is derived under the joint positioning method. The provided simulation results help to investigate the impact of system configurations on positioning measurements. Further, through the test in this paper, it is proved that joint positioning outperforms that of the time only or angle only method.

Secure IIoT Information Reinforcement Model Based on IIoT Information Platform Using Blockchain

Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing and steady data processing (analysis, prediction, prescription, and implementation) necessitate a new processing method. We propose a blockchain-based industrial internet of things information reinforcement model in this work that may reliably ensure the integrity of industrial internet of things data produced at industrial locations. The proposed model processes industrial internet of things data that may occur at endpoints at industrial sites into the blockchain by processing data generated by the same industrial internet of things device independently. As a result, the IIoT data sent to the industrial internet of things server can be evaluated more readily, and production accuracy may be enhanced. The proposed model optimizes industrial internet of things information linkage by stochastically reflecting the information based on attribute value frequency. By dynamically aggregating the related data of industrial internet of things information acquired as a seed through hierarchical subnets, the proposed model increases stability and accuracy. Furthermore, the proposed model may be used to enhance an organizations’ operational efficiency (consulting and training, for example) and strategic decision-making by utilizing fundamental knowledge about items produced at industrial locations. Furthermore, the proposed model allows for information sharing and system connectivity between industrial locations, allowing for close collaboration between industrial internet of things features. As a result of the performance evaluation, the proposed model included an industrial internet of things sensor to the blockchain, eliminating the need for an extra function in the manufacturing process and reducing the time required to validate the integrity of industrial internet of things data. In addition, as a result of analyzing industrial internet of things data by an algorithm according to the number of simulated clouds, the accuracy of industrial internet of things information was improved by 2.5% to 3%, on average.

An Energy-Efficient Partial Data Offloading-Based Priority Rate Controller Technique in Edge-Based IoT Network to Improve QoS

In last few years, the Internet of Things (IoT) platform, which comprises a significant number of devices equipped with sensors and utilized for monitoring and actuation operations, has seen significant growth. Massive volumes of data are collected by IoT devices, which are subsequently sent to the cloud for analysis and forecasting. IoT data that is directly sent to the cloud can congest IT infrastructure that is used for other purposes. As a result, data offloading has become a popular issue in academic and industrial sectors, specifically for traffic-intensive applications that can benefit from offloading to local edge and cloud infrastructure. A lot of in-depth studies have been undertaken in areas where sensor device mobility is high in order to ensure efficient data offloading. Nevertheless, very few studies have taken into account the issue of data offloading with sensor devices that have very limited mobility, like industrial IoT sensors, which really needs more attention as it is one of the most data-trafficking areas. To counter this problem, a data offloading-based heuristic technique using edge computing is proposed in IIoT-based applications. This will reduce data transferring quantity, resulting in lower data migration capacity, bandwidth usage, and load, lowering operational costs. It is being put through extensive testing to see how it compares to existing algorithms. The experimental results show that the proposed algorithm is superior in terms of energy consumption, dependability, and resilience under various situations.

Integrating process management and event processing in smart factories: A systems architecture and use cases

The developments of new concepts for an increased digitization of manufacturing industries in the context of Industry 4.0 have brought about novel system architectures and frameworks for smart production systems. These range from generic frameworks for Industry 4.0 to domain-specific architectures for Industrial Internet of Things (IIoT). While most of the approaches include a service-based architecture for selective integration with enterprise systems, a close two-way integration of the production control systems and IIoT sensors and actuators with Process-Aware Information Systems (PAIS) on the management level for automation and mining of production processes is rarely discussed. This fusion of Business Process Management (BPM) with IIoT can be mutually beneficial for both research areas, but is still in its infancy. We propose a systems architecture for IIoT that shows how to integrate the low-level hardware components–sensors and actuators–of a smart factory with BPM systems. We discuss the software components and their interactions to address challenges of device encapsulation, integration of sensor events, and interaction with existing BPM systems. This integration is demonstrated within several use cases regarding process modeling, automation and mining for a smart factory model, showing benefits of using BPM technologies to analyze, control, and adapt discrete production processes in IIoT.

Model and data driven transient thermal system modelings for contained data centers

The growing cloud computing infrastructures increase the demand for energy-intensive machines, such as high-performance servers and the associated cooling system. That brings challenges to the energy management of the concerned modern buildings such as the data centers. Transient Thermal System Modeling (TTSM) is an essential solution to illustrate the instant thermal variation inside the buildings based on heat transformation prediction. TTSM can help plan, organize and optimize corresponding settings to improve energy efficiency. The adoption of row-based and rack-based structures as a cooling solution has recently emerged, especially boosted by its high efficiency. In contrast, few studies have concerned the related TTSM. In this study, we propose two frameworks exclusively targeting the TTSM of data centers that adopted row-based or rack-based cooling systems. A model-driven framework (MDF) and a data-driven framework (DDF) are developed, respectively. The MDF is expressed by Ordinary Differential Equations (ODEs), it is built based on the lumped-capacitance method under several pre-declared assumptions as a general TTSM solution to related structures. The DDF is developed as an extension of the Long-Short-Term-Memory (LSTM) method to realize multivariate I/O (MIMO) predictions, which is commonly demanded in Industrial Internet of Things (IIoT) applications. Both frameworks are evaluated experimentally on a physical cluster equipped with a high-level IIoT sensor measurement system. The guide for parameter-tuning of the model is also provided in the experiment part for use case analysing. The validation results show that both frameworks can build reliable TTSM in terms of real-time temperature predictions. Moreover, a comprehensive comparison study is conducted with valuable application suggestions based on experimental discoveries.

Machine Learning for Automated Industrial IoT Attack Detection: An Efficiency-Complexity Trade-off

Critical city infrastructures that depend on smart Industrial Internet of Things (IoT) devices have been increasingly becoming a target of cyberterrorist or hacker attacks. Although this has led to multiple studies in the recent past, there exists a paucity of literature concerning real-time Industrial IoT attack detection. The goal of this article is to build a machine-learning approach using Industrial IoT sensor readings for accurately tracking down Industrial IoT attacks in real time. We analyze IoT system behavior under a lab-controlled series of attacks on a Secure Water Treatment (SWaT) system. The system is analytically challenging in that it results in sensor readings that resemble waveforms. To that end, we develop a novel early detection method using functional shape analysis (FSA) to extract features from the data that can capture the profile of the waveform. Our results show an efficiency-complexity trade-off between functional and non-functional methods in predicting IoT attacks.

An industrial IoT sensor system for high-temperature measurement

The world is changing, with more systems becoming automated through the Internet of Things (IoT). By the end of 2020, 38.5 billion connected devices are expected to be part of the IoT, which is an increase of 25.1 billion from 2015. With the digital revolution thoroughly underway, companies have been fully aware that statistical data can help their businesses thrive and increase efficiency. In this paper, the design and implementation of an Arduino sensor system used to record the temperature and location of ladle vessels for metallurgical purposes is presented. The Arduino microcontroller has multiple modules connected to it, including a K-Type thermocouple, Global-Positioning System (GPS) Shield, Real-time clock, and a Bluetooth module. Additionally, our system was fully tested to demonstrate that it could measure 1220 Celsius for twenty-two hours. Our work can contribute to the development of Industrial IoT and smart sensor services.

Risk-Aware Cloud-Edge Computing Framework for Delay-Sensitive Industrial IoTs

The industrial Internet of Things (IIoT) has been widely deployed to provide autonomous inspection on current production status and quality of products for modern manufacturing. However, the IIoT sensors generally are short of computing capabilities and therefore could not offer acceptable latency for computation-intensive inspection tasks. Besides, the mission-critical industrial applications are extremely sensitive to inspection failure, which may lead to serious manufacturing problems or accidents. In this paper, we propose a risk-aware cloud-edge computing framework for the delay-sensitive inspections of autonomous manufacturing. Due to the uncertainty of 802.11ax, we utilize the conditional value-at-risk (CVaR) to measure the inspection risk basing on the distribution of channel access delay. We develop a branch-and-check (BNC) approach to optimally and efficiently deploy the decomposable inspection tasks with the minimum operation cost and acceptable latency. The extensive simulations guide the operational use for future IIoT and the results show that the proposed system can save a large amount of unnecessary operation cost by enabling the processor sharing strategy.

Over-the-Air Aggregation With Multiple Shared Channels and Graph-Based State Estimation for Industrial IoT Systems

We consider remote state estimation for an industrial Internet-of-Things (IoT) system, where the plant dynamics are monitored by a number of distributed industrial IoT sensors. We propose an “estimation friendly” remote state estimation framework, which not only maintains low computational complexity but also provides better estimation stability performance. Specifically, we propose a novel over-the-air-aggregation-based multiple access, which enhances the observability performance of the state estimation system and hence, provides better estimation stability. Additionally, exploiting the sparsity in the observation matrix induced by the over-the-air-aggregation-based multiple access, we propose a low-complexity 2-D message passing state estimation algorithm, where the cyclic loops in the 2-D factor graphs are removed based on the quasi-diagonal transformation of the aggregated channel matrix of the IoT sensors. As a result, the proposed state estimation scheme is of low complexity and can achieve exact maximum a posterior estimation. Using the Lyapunov drift analysis, we derive the closed-form necessary and sufficient conditions for stability of the mission-critical remote state estimation system. The numerical results demonstrate that the proposed scheme has a low computational complexity. Furthermore, it is scalable with the number of sensors and has a low power consumption.

A Trust-Evaluation-Enhanced Blockchain-Secured Industrial IoT System

The Industrial Internet of Things (IIoT), allowing direct wireless communications between industrial machines and people, is the key component of Industry 4.0. By grouping several IIoT devices (IIoT-Ds) into IIoT groups (IIoT-Gs), the IIoT-Ds within one group can share information and deter malicious users from accessing the network by modifying the access control list (ACL) in IIoT-Ds. In an IIoT-G, several IIoT-Ds jointly manage the access control of IIoT sensors. The security of ACL is protected by blockchains deployed in IIoT-Ds. Even secured by blockchains, a blockchain-based IIoT-G (B-IIoT-G) still cannot secure correct access control in a system with more than half of the blockchain-based IIoT-Ds (B-IIoT-Ds) from spreading wrong authorization information. In this article, we propose a B-IIoT-G with trust evaluation for each B-IIoT-D to evaluate the weight in voting for final decisions of authorization. By designing a new voting mechanism with trust evaluation for the access control in B-IIoT-G, the system has a higher probability of making correct authorization even with malicious B-IIoT-Ds. Simulation results have demonstrated that the mechanism with trust evaluation can still make, with a high probability, correct authorization even with more than half of the B-IIoT-Ds in B-IIoT-G being malicious.

Anomaly events classification and detection system in critical industrial internet of things infrastructure using machine learning algorithms

Industrial Control System is used in the industrial process for reducing the human factor burden and handling the complex industrial system process and communications between them efficiently. Internet of Things (IoT) is the fusion of devices and sensors by an information network to enable new and autonomous capabilities. The integration of IoT with industrial applications known as the Industrial Internet of Things (IIoT). The IIoT is found in several critical infrastructures such as water distribution networks. Nowadays, ICS is vulnerable to using the Internet connection to enable industrial IoT sensors to communicate with each other in Real-Time. Therefore, this paper presents an analytical study of detecting anomalies, malicious activities, and cyber-attacks in a cyber-physical of critical water infrastructure in the IIoT infrastructure. The study uses various machine learning algorithms to classify the anomaly events including several attacks and IIoT hardware failures. A real-world dataset covering 15 anomaly situations of normal system activity was analyzed for the research review of the proposed approach. The test situations involved a wide array of incidents from hardware breakdown to water SCADA device sabotage. To classify the malicious activity, various machine learning methods, such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), k-nearest neighbours (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Classification and Regression Tree (CART) are used. The results show that CART and NB have the best results for accuracy, precision, recall, and F1-score.

A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications

The Industrial Internet of things (IIoT) helps several applications that require power control and low cost to achieve long life. The progress of IIoT communications, mainly based on cognitive radio (CR), has been guided to the robust network connectivity. The low power communication is achieved for IIoT sensors applying the Low Power Wide Area Network (LPWAN) with the Sigfox, NBIoT, and LoRaWAN technologies. This paper aims to review the various technologies and protocols for industrial IoT applications. A depth of assessment has been achieved by comparing various technologies considering the key terms such as frequency, data rate, power, coverage, mobility, costing, and QoS. This paper provides an assessment of 64 articles published on electricity control problems of IIoT between 2007 and 2020. That prepares a qualitative technique of answering the research questions (RQ): RQ1: “How cognitive radio engage with the industrial IoT?”, RQ2: “What are the Proposed architectures that Support Cognitive Radio LPWAN based IIOT?”, and RQ3: What key success factors need to comply for reliable CIIoT support in the industry?”. With the systematic literature assessment approach, the effects displayed on the cognitive radio in LPWAN can significantly revolute the commercial IIoT. Thus, researchers are more focused in this regard. The study suggests that the essential factors of design need to be considered to conquer the critical research gaps of the existing LPWAN cognitive-enabled IIoT. A cognitive low energy architecture is brought to ensure efficient and stable communications in a heterogeneous IIoT. It will protect the network layer from offering the customers an efficient platform to rent AI, and various LPWAN technology were explored and investigated.

In-Network Computing Powered Mobile Edge: Toward High Performance Industrial IoT

Recently, the industrial Internet of Things (IoT) has quickly become a disruptive force reshaping how we live and work. Compared to the consumer Internet, the industrial IoT puts forward much higher performance requirements in terms of network and computing capacity. The industrial IoT system needs to process tons of data generated by millions of IoT sensors in real-time. Recently, with the advent of programmable network devices (e.g., SmartNIC, programmable switch), the in-network computing (INC) paradigm has received a large amount of attention. INC refers to offload application-specific tasks from end-host to network devices. Benefiting from the line-rate processing capacity of network devices, the INC paradigm presents superior performance in terms of high-throughput low-latency computing. Therefore, INC is considered a promising technique to meet performance requirements in the industrial IoT. However, while network devices are capable of performing application-specific tasks, they are still far from the universal computing platform. In this article, we propose an INC powered mobile edge architecture, where we offload lightweight critical tasks to the INC devices and leave the rest to the MEC. To identify critical tasks, we introduce the complex event processing (CEP) tool in our architecture. Also, we present two industrial IoT use cases to evaluate the feasibility of our architecture.