Industrial Internet of Things: Requirements, Architecture, Challenges, and Future Research Directions

Alabadi, Montdher; Habbal, Adib; Wei, Xian

doi:10.1109/access.2022.3185049

Cited by 52 publications

(8 citation statements)

References 109 publications

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“…The analysis focuses on the challenges encountered in the field of PdM, as shown in Table 9 . Considering the challenges of PdM, insights are derived from Alabadi, Habbal & Wei (2022) , which specifically addresses the challenges associated with IoT in the context of predictive maintenance. As the suggested system relies on data collected from IoT devices, these challenges are directly relevant to the suggested approach.…”

Section: Resultsmentioning

confidence: 99%

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Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system

Alabadi,

Habbal

2023

PeerJ Computer Science

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The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks’ data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system’s development and validation processes. Rigorous validation confirms the system’s accuracy, performance, and security through an experimental testbed. The results demonstrate the system’s dynamic updating and domain independence. Prediction model surpass state-of-the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system’s robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.

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Section: Resultsmentioning

confidence: 99%

“…The fourth industrial revolution, often referred to as Industry 4.0, marks a transformative integration of the Industrial Internet of Things (IIoT) into various sectors ( Alabadi, Habbal & Wei, 2022 ). This integration ushers in an era of unparalleled efficiency, performance, and data-centric decision-making ( Namuduri et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system

Alabadi,

Habbal

2023

PeerJ Computer Science

Self Cite

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“…The main purpose of the sensing and actuation layer is to collect data and react smartly in a timely manner. This layer includes various input devices (sensors) and output devices (actuators) as depicted in reference [22]. The network layer provides the connection between the sensing and actuation, and service layers [23].…”

Section: Methodsmentioning

confidence: 99%

Transformation to a smart factory using NodeMCU with Blynk platform

Hailan

Albaker

Alwan

2023

IJEECS

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Incorporating internet of things (IoT) in industrial systems prompted the development of industrial internet of things (IIoT) systems, which in turn enable the automation of intelligent devices to gather, analyze, and transmit data from industrial systems in real-time. This paper develops a low-cost and smart industrial remote monitoring and control system based on NodeMCU microcontrollers and Blynk server platform. It is deployed to remotely monitor manufacturer's environment and industrial equipment and control them autonomously. Also, it protects the manufacturer's employees from fire catastrophe by warning them using a buzzer and notification. The system comprises two main parts, sensing and actuation. The sensing part consists of three subsystems that measure temperature and humidity, water flow, and flame. The actuation part consists of a water pump, light, and fan. A powerful user interface is developed based on the Blynk platform. The proposed system controls the water pump by sensing water flow autonomously. In addition, based on a fire detected, a protection system is implemented to shut down the electricity from load in case of fire event occurs. Several testing scenarios were carried on to check the response of the system, and the result shows successful implementation of the proposal to handle different situations.

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“…It provides higher accuracy, lower precision. Alabadi et al (2022) have presented Industrial internet of things: Requirements, architecture, challenges, and future investigation directions. The presented paper offers a comprehensive examination of Industry 4.0 with IIoT, with main objectives being to present the latest advancements in these domains, address any existing limits.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Fuzzy Decision Tree based Blockchain Federated Safety- as-a-Service for IIoT

Tripathi,

Umrao

2024

Preprint

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Blockchains are not appropriate for smart appliances because they are expensive to compute, have a lot of overhead bandwidth and cause delays. Improving data delivery, quality, and precision are especially important for ML. The combination of ML and Blockchain technologies make exact outcomes. The IIoT, has quickly been recognized and is attainment huge attention in educational areas with manufacturing, but IoT solitude danger and privacy exposures are emerging by lack of significant security technology. The Blockchain Driven Cyber-Physical system (BDCPS) is supported by IoT-cloud services. In this paper, Integrated Fuzzy Decision Tree based Blockchain Federated Safety-as-a-Service for IIoT (IFDT-BCF-SAS-IIOT) is proposed. In this study, Lightweight Homomorphic Cryptographic Algorithm is run on separate board system. The suggested system uses Delegated Proof of Work Consensus Protocol to resolve problems likes’ lightweight, warehousing transactions, evaporation, and shipment time. The data flow of blockchain is proposed to establish application of integrated fuzzy decision tree to food traceability. The proposed method is executed in Python, performance of proposed technique is analysed with performance metrics like, accuracy, precision, F1-score, recall, specificity, Mean Absolute Error, MSE, RMSE are analyzed.The proposed IFDT-BCF-SAS-IIOT method attains 32.58%, 26.73% and 24.22%, higher Specificity, 23.58%, 20.73%, 19.32% higher precision, 30.18%, 28.66% and 24.32% higher recall, 21.58%, 20.73%, 18.32% lower RMSE analysed with existing techniques like new blockchain federated safety-as-a-service system for IIoT utilizing ML (NBF-SAS-IIOT), IIoT: requirements, architecture, challenges, future research directions (IIOT-SAS-RAC), differential privacy for IIoT: opportunities, applications, challenges (IIOT-SAS-OAC) respectively.

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Industrial Internet of Things: Requirements, Architecture, Challenges, and Future Research Directions

Cited by 52 publications

References 109 publications

Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system

Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system

Transformation to a smart factory using NodeMCU with Blynk platform

Integrated Fuzzy Decision Tree based Blockchain Federated Safety- as-a-Service for IIoT

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