A Comprehensive Survey of Industry 4.0, IIoT and Areas of Implementation

Ainsworth, Tyler; Brake, Jeremy; Gonzalez, Peter; Toma, David; Browne, Aidan F.

doi:10.1109/southeastcon45413.2021.9401860

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…IoT adoption in the industrial sector has given birth to the new technology IIoT [15,26] . Hence, it can be defined as an extensive network connecting many sensors and actuators implemented in various fields, such as farming, healthcare, automotive, and smart grid [27] .…”

Section: Literature Surveymentioning

confidence: 99%

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Mohy-eddine

Guezzaz

Benkirane

et al. 2023

Big Data Min. Anal.

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Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson's Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18 s and 6.25 s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models.

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Section: Literature Surveymentioning

confidence: 99%

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Mohy-eddine

Guezzaz

Benkirane

et al. 2023

Big Data Min. Anal.

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“…In [15], recent applications and advancements in the fields of IIoT and Industry 4.0 are reviewed. Sensors and their use in new and old systems, security considerations around such systems, potential challenges with IIoT and Industry 4.0, and recent IIoT and Industry 4.0 installations in industrial facilities are mostly covered.…”

Section: B Related Work and Contributionmentioning

confidence: 99%

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

2022

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Industry 4.0 relates to the digital revolution of manufacturing and other markets such as retail, distribution, oil and gas, and infrastructure. On the other hand, the Industrial Internet of Things (IIoT) is a technological advancement that leads to Industry 4.0 implementation by boosting the manufacturing sector's productivity and economic impact. IIoT provides the ability to provide global connectivity between components in different locations. The manufacturing sector has had various difficulties implementing IIoT, primarily due to IoT characteristics. We offer an in-depth review of Industry 4.0 and IIoT in this survey. The primary motivation behind this survey is to introduce the most recent advancements related to Industry 4.0 and IIoT and to address the existing limitations. First, we provide a novel taxonomy of IIoT challenges that includes aspects of each challenge, such as the terminology and approaches utilized to solve these challenges. Besides IIoT challenges, this survey provides an in-depth demonstration of the many concepts related to IIoT, such as architecture and use cases. Second, the study provides a comprehensive review to study the state-of-the-art of Industry 4 in terms of concepts, requirements and supporting technology. In addition, we discuss the correlation between enabling technology and technical requirements in detail. Finally, our survey targets deep learning, edge computing, and big data as key techniques for future IIoT. Furthermore, the presented technique is thoroughly examined to present a different method for future adoption. Additionally, to the proposed technique, we propose a new architecture for the future of IIoT based on three primary techniques.

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“…Organizations can compete in international markets by efficiently utilizing quality to outperform competitors. Integrating several cutting-edge technologies, such as machine learning, the Internet of Things, cyber security, 3D printing, mixed reality, autonomous robots, cloud computing, cognitive computing, quantum computing, augmented reality, cyberphysical spaces, nanotechnology, genetic engineering, artificial intelligence, and blockchain technology, has significantly impacted industrial systems in recent times [1]. Industry 4.0 has changed substantially modern enterprises' engineering, manufacturing techniques, procedures, and technologies [2].…”

Section: Introductionmentioning

confidence: 99%

“…This phenomenon, known as the Fourth Industrial Revolution is founded on advanced engineering and manufacturing technology. Extensive digitalization, advanced robotics, adaptive automation, modeling and simulation, artificial intelligence, additive and precision manufacturing, big data analytics, and material nanoengineering are all part of it [1], [3]. One issue to address is how traditional management systems and processes have accommodated changes in cycle time compression, product development stages, and staff efforts in order to meet customer expectations and requirements [4], [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing the Delphi Method for Selecting Smart Quality Key Factors in the Manufacturing Context

Silitonga,

Sukwadi,

Sejahtera Surbakti

et al. 2024

TEM Journal

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The quality of products and services used in global marketplaces is critical. Manufacturing companies can compete in international marketplaces by leveraging quality effectively to outperform competition. Industry 4.0, a plan promoted by industrialized countries, has resulted in implementing "smart manufacturing," which places a premium on quality control. As a result of improved quality control enabled by new technology, the industrial sector has been changed. Nonetheless, smart quality implementation is still in its early stages, and more empirical study is needed to understand its benefits and drawbacks fully. A survey will be developed to assess how people perceive smart quality awareness and manufacturers' readiness to embrace Smart Quality. This study applied Delphi methodology to investigate the critical factors and particular indicators of implementing the Smart Quality model in manufacturing organizations. Experts from academics and business management analysed the Delphi approach and discovered eight factors and sixteen indicators. The findings will lead to a new method for assessing Smart Quality.

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A Comprehensive Survey of Industry 4.0, IIoT and Areas of Implementation

Cited by 14 publications

References 31 publications

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

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

Utilizing the Delphi Method for Selecting Smart Quality Key Factors in the Manufacturing Context

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