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Industrial Internet of Things (IIoTs) and Industry 4.0

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (25 June 2022) | Viewed by 54885

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Special Issue Editors

Dr. Mohsin Raza

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Guest Editor

Department of Computer Science, Edge Hill University, Ormskirk L39 4QP, UK
Interests: IoT; IIoT; wireless networks; Industry 4.0; digital twins; process optimization; ultra-reliable low latency communications (URLLC)
Special Issues, Collections and Topics in MDPI journals

Dr. Ghufran Ahmed

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Guest Editor

Department of Computer Science, FAST-NUCES, Karachi 44000, Pakistan
Interests: IoT; wireless networks; data routing; energy efficiency; Industry 4.0; cyberphysical systems

Dr. Muhammad Awais

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Guest Editor

School of Computing Sciences, University of East Anglia, Norwich, UK
Interests: Internet of Things; digital healthcare; physical activity classification; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Jawad Ahmad

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Guest Editor

School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: cryptography; blockchain; chaos theory; image encryption; IoT; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, industries have experienced significant transformation from manual processes to automated process handling and control through the inclusion of information and communication technology (ICT). Emerging technologies such as the Industrial Internet of Things (IIoT), artificial intelligence, big data analytics, smart manufacturing, digital twins, cognitive intelligence, and cyberphysical systems have played a significant role in the realization of Industry 4.0. Industry 4.0, through its integrated ICT technologies, has the potential to enable manufacturers and the supply chain to save time, boost productivity, reduce waste and costs, and respond flexibly and efficiently to consumers’ requirements.

Industry 4.0 is not only about digitalization of manufacturing components and processes, but also about creating smart factories for evolution in supply chain and the production line. Such smart factories significantly rely on the IIoT. The IIoT enables the interconnection of smart heterogeneous objects (e.g., sensor actuators, RFID tags, embedded computers, and mobile devices) using standard communication protocols, and Big Data captured from the interconnected IoT-based objects, and real-time analysis. The IIoT can improve interconnectivity, flexibility, scalability, time efficiency, cost effectiveness, security, productivity, and operational efficiency in the industries where the IoT serves as a base platform to establish an intelligent network of devices which can interrelate data and processes to effectively establish feedback control systems within the context of industrial automation. The IIoT also plays a significant role in the realization of cyberphysical systems and digital twins in Industry 4.0.

With the rapid advancements in the domain, it is vital to disseminate knowledge among the research community and stakeholders to adapt to new trends and advancements, to highlight the challenges experienced in industry in order to excel further in Industry 4.0. Therefore, we invite colleagues in the research community to disseminate their novel contributions in the domain of Industry 4.0, with a special focus on but not limited to:

Developing and building new models and architectures for the IoT;
Decision support systems for IIoT-enabled Industry 4.0
Digital twin modeling for Industry 4.0
IIoT for emergency systems;
Cloud-based solutions for IIoT;
AI-empowered innovative solutions for Industry 4.0;
Ultra-reliable low latency communications (URLLC) in the IIoT;
Data mining techniques for IIoT-enabled Industry 4.0;
Machine-learning-based smart IIoT solutions;
IIoT for regulatory and supervisory control systems;
Energy harvesting and energy budgeting in the IIoT;
Data routing in Industry 4.0;
Smart IIoT solutions;
Cyberphysical systems;
Big data analytics and process optimization in Industry 4.0;
IT tools and data analytics for Industry 4.0;
Cutting-edge technologies in Industry 4.0;
Performance, scalability, and reliability in the IIoT;
Machine learning techniques for developing new frameworks and models;
Deep learning for predictive modeling and realization of cyberphysical systems and digital twins;
Data security and privacy aspects in the IIoT;
Standard, platforms, testbed, and validation for the IIoT.

Dr. Mohsin Raza
Dr. Ghufran Ahmed
Dr. Muhammad Awais
Dr. Jawad Ahmad
Guest Editors

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Keywords

Industrial Internet of Things (IIoT)
Industry 4.0
artificial intelligence
deep learning
machine learning
big data analytics
digital twins
decision support systems
cyberphysical systems
data security in Industry 4.0
ultra-reliable low latency communications

Published Papers (12 papers)

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Research

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23 pages, 7088 KiB

Open AccessArticle

Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication

by Qiao Gang, Aman Muhammad, Zahid Ullah Khan, Muhammad Shahbaz Khan, Fawad Ahmed and Jawad Ahmad

Sustainability 2022, 14(15), 9683; https://doi.org/10.3390/su14159683 - 6 Aug 2022

Cited by 9 | Viewed by 1502

Abstract

This study aims to realize Sustainable Development Goals (SDGs), i.e., SDG 9: Industry Innovation and Infrastructure and SDG 14: Life below Water, through the improvement of localization estimation accuracy in magneto-inductive underwater wireless sensor networks (MI-UWSNs). The accurate localization of sensor nodes in MI communication can effectively be utilized for industrial IoT applications, e.g., underwater gas and oil pipeline monitoring, and in other important underwater IoT applications, e.g., smart monitoring of sea animals, etc. The most-feasible technology for medium- and short-range communication in IIoT-based UWSNs is MI communication. To improve underwater communication, this paper presents a machine learning-based prediction of localization estimation accuracy of randomly deployed sensor Rx nodes through anchor Tx nodes in the MI-UWSNs. For the training of ML models, extensive simulations have been performed to create two separate datasets for the two configurations of excitation current provided to the Tri-directional (TD) coils, i.e., configuration1-case1_configuration2-case1 (c1c1_c2c1) and configuration1-case2_configuration2-case2 (c1c2_c2c2). Two ML models have been created for each case. The accuracies of both models lie between 95% and 97%. The prediction results have been validated by both the test dataset and verified simulation results. The other important contribution of this paper is the development of a novel assembling technique of a MI-TD coil to achieve an approximate omnidirectional magnetic flux around the communicating coils, which, in turn, will improve the localization accuracy of the Rx nodes in IIoT-based MI-UWSNs. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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19 pages, 3039 KiB

Open AccessArticle

A Predictive Checkpoint Technique for Iterative Phase of Container Migration

by Gursharan Singh, Parminder Singh, Mustapha Hedabou, Mehedi Masud and Sultan S. Alshamrani

Sustainability 2022, 14(11), 6538; https://doi.org/10.3390/su14116538 - 26 May 2022

Cited by 6 | Viewed by 1599

Abstract

Cloud computing is a cost-effective method of delivering numerous services in Industry 4.0. The demand for dynamic cloud services is rising day by day and, because of this, data transit across the network is extensive. Virtualization is a significant component and the cloud servers might be physical or virtual. Containerized services are essential for reducing data transmission, cost, and time, among other things. Containers are lightweight virtual environments that share the host operating system’s kernel. The majority of businesses are transitioning from virtual machines to containers. The major factor affecting the performance is the amount of data transfer over the network. It has a direct impact on the migration time, downtime and cost. In this article, we propose a predictive iterative-dump approach using long short-term memory (LSTM) to anticipate which memory pages will be moved, by limiting data transmission during the iterative phase. In each loop, the pages are shortlisted to be migrated to the destination host based on predictive analysis of memory alterations. Dirty pages will be predicted and discarded using a prediction technique based on the alteration rate. The results show that the suggested technique surpasses existing alternatives in overall migration time and amount of data transmitted. There was a 49.42% decrease in migration time and a 31.0446% reduction in the amount of data transferred during the iterative phase. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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21 pages, 3623 KiB

Open AccessArticle

A Novel Approach towards Sustainability Assessment in Manufacturing and Stakeholder’s Role

by Aamir Rasheed and William Ion

Sustainability 2022, 14(6), 3221; https://doi.org/10.3390/su14063221 - 9 Mar 2022

Cited by 5 | Viewed by 2141

Abstract

With increasing consumer awareness about sustainability and governmental policies to address environmental challenges and social responsibility, the manufacturing sector is under continuous pressure to adopt more sustainable practices. Trends show that factories of the future (FOF) will need to adapt to market demands, growing economic and ecological efficiency requirements, and corporate social responsibility; such versatility is vital to address consumer disquiet and sustainability expectations. Various approaches have been proposed to assess sustainability over the last few decades. Most of these approaches have limitations in that they are of marginal relevance to the manufacturing environment, tend to focus on only one aspect of sustainability, or are too complicated for most organisations to implement. Moreover, numerous studies have demonstrated a gap in sustainability expectations among various stakeholders, and no active mechanisms exist to prioritise sustainability in manufacturing. This paper introduced a novel approach to address both the manufacturer and multiple stakeholders’ expectations about sustainability prioritisations in manufacturing practices. It achieved this using a modified quality function deployment (QFD) tool along with AHP and normalisation techniques. A set of system boundaries was adopted to evaluate sustainability in the manufacturing context; this research was a ‘Gate to Gate’ border. These indicators and a score-based approach will help organisations better grasp how manufacturing operations interact with sustainability and decision-making. They will help lead to improvements in the allocation of corporate resources used to manage and improve sustainability performance in manufacturing. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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15 pages, 1637 KiB

Open AccessArticle

A Weighted Minimum Redundancy Maximum Relevance Technique for Ransomware Early Detection in Industrial IoT

by Yahye Abukar Ahmed, Shamsul Huda, Bander Ali Saleh Al-rimy, Nouf Alharbi, Faisal Saeed, Fuad A. Ghaleb and Ismail Mohamed Ali

Sustainability 2022, 14(3), 1231; https://doi.org/10.3390/su14031231 - 21 Jan 2022

Cited by 21 | Viewed by 2443

Abstract

Ransomware attacks against Industrial Internet of Things (IIoT) have catastrophic consequences not only to the targeted infrastructure, but also the services provided to the public. By encrypting the operational data, the ransomware attacks can disrupt the normal operations, which represents a serious problem for industrial systems. Ransomware employs several avoidance techniques, such as packing, obfuscation, noise insertion, irrelevant and redundant system call injection, to deceive the security measures and make both static and dynamic analysis more difficult. In this paper, a Weighted minimum Redundancy maximum Relevance (WmRmR) technique was proposed for better feature significance estimation in the data captured during the early stages of ransomware attacks. The technique combines an enhanced mRMR (EmRmR) with the Term Frequency-Inverse Document Frequency (TF-IDF) so that it can filter out the runtime noisy behavior based on the weights calculated by the TF-IDF. The proposed technique has the capability to assess whether a feature in the relevant set is important or not. It has low-dimensional complexity and a smaller number of evaluations compared to the original mRmR method. The TF-IDF was used to evaluate the weights of the features generated by the EmRmR algorithm. Then, an inclusive entropy-based refinement method was used to decrease the size of the extracted data by identifying the system calls with strong behavioral indication. After extensive experimentation, the proposed technique has shown to be effective for ransomware early detection with low-complexity and few false-positive rates. To evaluate the proposed technique, we compared it with existing behavioral detection methods. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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16 pages, 1821 KiB

Open AccessArticle

An Approach towards IoT-Based Predictive Service for Early Detection of Diseases in Poultry Chickens

by Ghufran Ahmed, Rauf Ahmed Shams Malick, Adnan Akhunzada, Sumaiyah Zahid, Muhammad Rabeet Sagri and Abdullah Gani

Sustainability 2021, 13(23), 13396; https://doi.org/10.3390/su132313396 - 3 Dec 2021

Cited by 19 | Viewed by 5166

Abstract

The poultry industry contributes majorly to the food industry. The demand for poultry chickens raises across the world quality concerns of the poultry chickens. The quality measures in the poultry industry contribute towards the production and supply of their eggs and their meat. With the increasing demand for poultry meat, the precautionary measures towards the well-being of the chickens raises the concerns of the industry stakeholders. The modern technological advancements help the poultry industry in monitoring and tracking the health of poultry chicken. These advancements include the identification of the chickens’ sickness and well-being using video surveillance, voice observations, ans feces examinations by using IoT-based wearable sensing devices such as accelerometers and gyro devices. These motion-sensing devices are placed over a chicken and transmit the chicken’s movement data to the cloud for further analysis. Analyzing such data and providing more accurate predictions about chicken health is a challenging issue. In this paper, an IoT based predictive service framework for the early detection of diseases in poultry chicken is proposed. The proposed study contributes by extending the dataset through generating the synthetic data using Generative Adversarial Networks (GAN). The experimental results classify the sick and healthy chicken in a poultry farms using machine learning classification modeling on the synthetic data and the real dataset. Theoretical analysis and experimental results show that the proposed system has achieved an accuracy of 97%. Moreover, the accuracy of the different classification models are compared in the proposed study to provide more accurate and best performing classification technique. The proposed study is mainly focused on proposing an Industrial IoT-based predictive service framework that can classify poultry chickens more accurately in real time. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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14 pages, 1041 KiB

Open AccessArticle

Secure IIoT-Enabled Industry 4.0

by Zeeshan Hussain, Adnan Akhunzada, Javed Iqbal, Iram Bibi and Abdullah Gani

Sustainability 2021, 13(22), 12384; https://doi.org/10.3390/su132212384 - 10 Nov 2021

Cited by 10 | Viewed by 2763

Abstract

The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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14 pages, 3541 KiB

Open AccessArticle

Extending ONTAgri with Service-Oriented Architecture towards Precision Farming Application

by Muhammad Fahad, Tariq Javid, Hira Beenish, Adnan Ahmed Siddiqui and Ghufran Ahmed

Sustainability 2021, 13(17), 9801; https://doi.org/10.3390/su13179801 - 31 Aug 2021

Cited by 6 | Viewed by 2276

Abstract

The computer science perspective of ontology refers to ontology as a technology, however, with a different perspective in terms of interrogations and concentrations to construct engineering models of reality. Agriculture-centered architectures are among rich sources of knowledge that are developed, preserved, and released for farmers and agro professionals. Many researchers have developed different variants of existing ontology-based information systems. These systems are primarily picked agriculture-related ontological strategies based on activities such as crops, weeds, implantation, irrigation, and planting, to name a few. By considering the limitations on agricultural resources in the ONTAgri scenario, in this paper, an extension of ontology is proposed. The extended ONTAgri is a service-oriented architecture that connects precision farming with both local and global decision-making methods. These decision-making methods are connected with the Internet of Things systems in parallel for the input processing of system ontology. The proposed architecture fulfills the requirements of Agriculture 4.0. The significance of the proposed approach aiming to solve a multitude of agricultural problems being faced by the farmers is successfully demonstrated through SPARQL queries. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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13 pages, 821 KiB

Open AccessArticle

Identifying the Antecedents of University Students’ Usage Behaviour of Fitness Apps

by Jo-Hung Yu, Gordon Chih-Ming Ku, Yu-Chih Lo, Che-Hsiu Chen and Chin-Hsien Hsu

Sustainability 2021, 13(16), 9043; https://doi.org/10.3390/su13169043 - 12 Aug 2021

Cited by 5 | Viewed by 2914

Abstract

The purpose of the study is to explore the antecedents of university students’ fitness application usage behaviours by combining the theory of planned behaviour and the technology acceptance model. An anonymous questionnaire survey was adopted to address the objectives of the study. Purposive and snowball sampling was used to select eligible students from six universities in Zhanjiang City. An online survey was used to collect data from 634 eligible subjects, and partial least squares structural equation modelling was used to analyse the collected data. The results indicated that the students’ perceived usefulness (β = 0.17, p < 0.05) and perceived ease of use (β = 0.32, p < 0.05) concerning the application and their attitude (β = 0.31, p < 0.05) toward it significantly influenced their usage intentions. Furthermore, perceived usefulness (β = 0.11, p < 0.05) and perceived ease of use (β = 0.38, p < 0.05) fully mediated the relationship between subjective norms and usage intentions. However, subjective norms and perceived behavioural control did not enhance the students’ intentions to use fitness applications. That is, students’ attitudes and fitness application design are the determinants of usage intention. Accordingly, improving students’ fitness applications usage intention requires strategies that involve customised services, social networking, and collaboration with schools; this would further increase students’ engagement in physical exercise. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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26 pages, 10806 KiB

Open AccessArticle

Water Quality Monitoring and Management of Building Water Tank Using Industrial Internet of Things

by Rajesh Singh, Mohammed Baz, Anita Gehlot, Mamoon Rashid, Manpreet Khurana, Shaik Vaseem Akram, Sultan S. Alshamrani and Ahmed Saeed AlGhamdi

Sustainability 2021, 13(15), 8452; https://doi.org/10.3390/su13158452 - 28 Jul 2021

Cited by 16 | Viewed by 6380

Abstract

Water being one of the foremost needs for human survival, conservation, and management of the resource must be given ultimate significance. Water demand has increased tremendously all over the world from the past decade due to urbanization, climatic change, and ineffective management of water. The advancement in sensor and wireless communication technology encourages implementing the IoT in a wide range. In this study, an IoT-based architecture is proposed and implemented for monitoring the level and quality of water in a domestic water tank with customized hardware based on 2.4 GHz radiofrequency (RF) communication. Moreover, the ESP 8266 Wi-Fi module-based upper tank monitoring of the proposed architecture encourages provide real-time information about the tank through internet protocol (IP). The customized hardware is designed and evaluated in the Proteus simulation environment. The calibration of the pH sensor and ultrasonic value is carried out for setting the actual value in the prototype for obtaining the error-free value. The customized hardware that is developed for monitoring the level and quality of water is implemented. The real-time visualization and monitoring of the water tank are realized with the cloud-enabled Virtuino app. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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32 pages, 7442 KiB

Open AccessArticle

A Framework for Industry 4.0 Readiness and Maturity of Smart Manufacturing Enterprises: A Case Study

by Zeki Murat Çınar, Qasim Zeeshan and Orhan Korhan

Sustainability 2021, 13(12), 6659; https://doi.org/10.3390/su13126659 - 11 Jun 2021

Cited by 44 | Viewed by 9562

Abstract

Recently, researchers have proposed various maturity models (MMs) for assessing Industry 4.0 (I4.0) adoption; however, few have proposed a readiness framework (F/W) integrated with technology forecasting (TF) to evaluate the growth of I4.0 adoption and consequently provide a roadmap for the implementation of I4.0 for smart manufacturing enterprises. The aims of this study were (1) to review the research related to existing I4.0 MMs and F/Ws; (2) to propose a modular MM with four dimensions, five levels, 60 second-level dimensions, and 246 sub-dimensions, and a generic F/W with four layers and seven hierarchy levels; and (3) to conduct a survey-based case study of an automobile parts manufacturing enterprise by applying the MM and F/W to assess the I4.0 adoption level and TF model to anticipate the growth of I4.0. MM and F/W integrated with TF provides insight into the current situation and growth of the enterprise regarding I4.0 adoption, by identifying the gap areas, and provide a foundation for I4.0 integration. Case study findings show that the enterprise’s overall maturity score is 2.73 out of 5.00, and the forecasted year of full integration of I4.0 is between 2031 and 2034 depending upon the policy decisions. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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19 pages, 2536 KiB

Open AccessArticle

Digital Certificate Verification Scheme for Smart Grid using Fog Computing (FONICA)

by Shahid Mahmood, Moneeb Gohar, Jin-Ghoo Choi, Seok-Joo Koh, Hani Alquhayz and Murad Khan

Sustainability 2021, 13(5), 2549; https://doi.org/10.3390/su13052549 - 26 Feb 2021

Cited by 10 | Viewed by 2354

Abstract

Smart Grid (SG) infrastructure is an energy network connected with computer networks for communication over the internet and intranets. The revolution of SGs has also introduced new avenues of security threats. Although Digital Certificates provide countermeasures, however, one of the issues that exist, is how to efficiently distribute certificate revocation information among Edge devices. The conventional mechanisms, including certificate revocation list (CRL) and online certificate status protocol (OCSP), are subjected to some limitations in energy efficient environments like SG infrastructure. To address the aforementioned challenges, this paper proposes a scheme incorporating the advantages and strengths of the fog computing. The fog node can be used for this purpose with much better resources closer to the edge. Keeping the resources closer to the edge strengthen the security aspect of smart grid networks. Similarly, a fog node can act as an intermediate Certification Authority (CA) (i.e., Fog Node as an Intermediate Certification Authority (FONICA)). Further, the proposed scheme has reduced storage, communication, processing overhead, and latency for certificate verification at edge devices. Furthermore, the proposed scheme reduces the attack surface, even if the attacker becomes a part of the network. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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Review

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22 pages, 29850 KiB

Open AccessEditor’s ChoiceReview

Impact of IoT on Manufacturing Industry 4.0: A New Triangular Systematic Review

by Tahera Kalsoom, Shehzad Ahmed, Piyya Muhammad Rafi-ul-Shan, Muhammad Azmat, Pervaiz Akhtar, Zeeshan Pervez, Muhammad Ali Imran and Masood Ur-Rehman

Sustainability 2021, 13(22), 12506; https://doi.org/10.3390/su132212506 - 12 Nov 2021

Cited by 47 | Viewed by 11749

Abstract

The Internet of Things (IoT) has realised the fourth industrial revolution concept; however, its applications in the manufacturing industry are relatively sparse and primarily investigated without contextual peculiarities. Our research undertakes an intricate critical review to investigate significant aspects of IoT applications in the manufacturing Industry 4.0 perspective to address this gap. We adopt a systematic literature review approach by Denyer and Tranfield (2009) to carry out critical analyses that help develop future research domains based on empirical studies. We describe key knowledge gaps in the existing literature and empirical studies by exploring the main contribution categories and finding six critical differences between traditional and manufacturing Industry 4.0 and 10 enablers and 11 challenges of IoT applications. Finally, an agenda for future research is proposed with 11 research domains to focus on the recognised gaps. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoTs) and Industry 4.0)

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