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Industry 4.0: From Future of IoT to Industrial IoT

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 16897

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Special Issue Editors

Dr. Sudan Jha

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Guest Editor

School of Computer Science and Engineering, Lovely Professional University, Phagwara, India

Dr. Hoang Viet Long

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Guest Editor

Faculty of Information Technology, People's Police University of Technology and Logistics, Bac Ninh, Viet Nam

Dr. Gyanendra Prasad Joshi

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Guest Editor

Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: MAC; routing protocols for next-generation wireless networks; wireless sensor networks; cognitive radio networks; RFID systems; IoT; smart city; deep learning; digital convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Industrial Internet of Things (IIoT) has now become a reality in the context of industrial transformation. It is becoming increasingly more pervasive in the industry as digitization is becoming a business reality for many organizations in some of the most trending sectors, such as health, agriculture, manufacturing, logistics, oil and gas, energy, and other utilities. This has compelled scientists and researchers to contribute more to advanced techniques in the field of distributed computing, integrated artificial intelligence, data mining, data aviation, machine learning, M2M communication, and many more. The reason behind this is that smart machines are better than humans at accurately capturing, analyzing, and communicating real-time data. A typical IIoT system consists of intelligent systems (applications, controllers, sensors, and security mechanisms), data communication infrastructure (cloud computing, edge computing, etc.), data analytics (to support business intelligence and corporate decision making), and, most importantly, the human element. The benefits that IIoT promises include enhanced safety, better reliability, smart metering, inventory management, equipment tracking, facilities management, etc. There are, however, numerous issues as well that are becoming the focus of active research. These refer to concerns with respect to service availability, data security, and device communication. Lack of ubiquitous interoperability between heterogeneous devices is also a major concern. On the other hand, industry 4.0 is the information-intensive transformation trend towards automation and data exchange of manufacturing technologies and processes. It includes cyberphysical systems, the Internet of things, cloud computing and cognitive computing, and creating the smart factory.

In this Special Issue, we aim to bring together academic and industrial researchers to explore the opportunities of IIoT for upcoming various domains and to focus its impact on the solutions of the aforementioned challenges and propose feasible solutions. We encourage papers covering various topics of interest that include but are not limited to the following list:

Recent developments and future of IIoT
Distributed computing, Fog and cloud computing for IIoT
A historical roadmap from IoT–IIoT–Industry 4.0
Industrial IoT using digitization and automation
IIoT applications and use cases for Industry 4.0
Automation for industrial processes
Intelligence in industries
IIoT implementation with business perspective
Business cases for IIoT deployment
Integration of IIoT with data science
Examining the access network technology and protocols in IIoT
Interoperability, convergence, and security issues for IIoT
Artificial Intelligence and data integrity for IIoT applications
Industrial internet connectivity framework (IICF)
Role of IIoT vs. IoT for Smart manufacturing, farming, health, grids, etc.
Various networks for IIoT
Optimization techniques for IIoT
Smart industrial systems
IIoT architectures and frameworks
Sensor technology in IIoT

Papers must be tailored to the issues related to IIoT and its future trends in Industry 4.0 systems. The scope of the SI is not limited to the abovementioned topics. However, the editors maintain the right to reject papers they deem to be out of the scope of this SI. Only original, unpublished contributions and invited articles will be considered for the issue.

Dr. Sudan Jha
Dr. Hoang Viet Long
Dr. Gyanendra Prasad Joshi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Internet of Things (IoT)
Industrial Internet of things (IIoT)
Cyber security, cyber-attacks
Big data, cloud computing, machine learning
Computer vision
Generic adversarial networks
Smart transportation and smart vehicles
Smart grid computing
Supply chain management
Geographical information system
Smart City, healthcare industry
Security, intrusion detection
IIoT WAN technologies and protocols

Published Papers (4 papers)

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Research

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20 pages, 1482 KiB

Open AccessFeature PaperArticle

Model-Driven Approach for Realization of Data Collection Architectures for Cyber-Physical Systems of Systems to Lower Manual Implementation Efforts

by Emanuel Trunzer, Birgit Vogel-Heuser, Jan-Kristof Chen and Moritz Kohnle

Sensors 2021, 21(3), 745; https://doi.org/10.3390/s21030745 - 22 Jan 2021

Cited by 5 | Viewed by 2280

Abstract

Data collection from distributed automated production systems is one of the main prerequisites to leverage information gain from data analysis in the context of Industrie 4.0, e.g., for the optimization of product quality. However, the realization of data collection architectures is associated with immense implementation efforts due to the heterogeneity of systems, protocols, and interfaces, as well as the multitude of involved disciplines in such projects. Therefore, this paper contributes with an approach for the model-driven generation of data collection architectures to significantly lower manual implementation efforts. Via model transformations, the corresponding source code is automatically generated from formalized models that can be created using a graphical domain-specific language. The automatically generated architecture features support for various established IIoT protocols. In a lab-scale evaluation and a unique generalized extrapolation study, the significant effort savings compared to manual programming could be quantified. In conclusion, the proposed approach can successfully mitigate the current scientific and industrial challenges to enable wide-scale access to industrial data. Full article

(This article belongs to the Special Issue Industry 4.0: From Future of IoT to Industrial IoT)

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20 pages, 3939 KiB

Open AccessArticle

ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET

by Le Minh Tuan, Le Hoang Son, Hoang Viet Long, L. Rajaretnam Priya, K. Ruba Soundar, Y. Harold Robinson and Raghvendra Kumar

Sensors 2020, 20(12), 3394; https://doi.org/10.3390/s20123394 - 16 Jun 2020

Cited by 9 | Viewed by 2605

Abstract

One of the crucial problems in Industry 4.0 is how to strengthen the performance of mobile communication within mobile ad-hoc networks (MANETs) and mobile computational grids (MCGs). In communication, Industry 4.0 needs dynamic network connectivity with higher amounts of speed and bandwidth. In order to support multiple users for video calling or conferencing with high-speed transmission rates and low packet loss, 4G technology was introduced by the 3G Partnership Program (3GPP). 4G LTE is a type of 4G technology in which LTE stands for Long Term Evolution, followed to achieve 4G speeds. 4G LTE supports multiple users for downlink with higher-order modulation up to 64 quadrature amplitude modulation (QAM). With wide coverage, high reliability and large capacity, LTE networks are widely used in Industry 4.0. However, there are many kinds of equipment with different quality of service (QoS) requirements. In the existing LTE scheduling methods, the scheduler in frequency domain packet scheduling exploits the spatial, frequency, and multi-user diversity to achieve larger MIMO for the required QoS level. On the contrary, time-frequency LTE scheduling pays attention to temporal and utility fairness. It is desirable to have a new solution that combines both the time and frequency domains for real-time applications with fairness among users. In this paper, we propose a channel-aware Integrated Time and Frequency-based Downlink LTE Scheduling (ITFDS) algorithm, which is suitable for both real-time and non-real-time applications. Firstly, it calculates the channel capacity and quality using the channel quality indicator (CQI). Additionally, data broadcasting is maintained by using the dynamic class-based establishment (DCE). In the time domain, we calculate the queue length before transmitting the next packets. In the frequency domain, we use the largest weight delay first (LWDF) scheduling algorithm to allocate resources to all users. All the allocations would be taken placed in the same transmission time interval (TTI). The new method is compared against the largest weighted delay first (LWDF), proportional fair (PF), maximum throughput (MT), and exponential/proportional fair (EXP/PF) methods. Experimental results show that the performance improves by around 12% compared with those other algorithms. Full article

(This article belongs to the Special Issue Industry 4.0: From Future of IoT to Industrial IoT)

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19 pages, 2507 KiB

Open AccessArticle

Integrating IoT and Blockchain for Ensuring Road Safety: An Unconventional Approach

by Deepak Prashar, Nishant Jha, Sudan Jha, Gyanendra Prasad Joshi and Changho Seo

Sensors 2020, 20(11), 3296; https://doi.org/10.3390/s20113296 - 10 Jun 2020

Cited by 20 | Viewed by 5119

Abstract

The Internet of things (IoT), the Internet of vehicles, and blockchain technology have become very popular these days because of their versatility. Road traffic, which is increasing day by day, is causing more and more deaths worldwide. The world needs a product that would reduce the number of road accidents. This paper suggests combining IoT and blockchain technology to mitigate road hazards. The new intelligent transportation system technologies and the subsequent emergence of 5G technologies will be a blessing, delivering the necessary speed to ensure both safety and quality of service (QoS). Hashgraph technology, a distributed ledger technology is used to create communication networks between the different vehicles and other relevant parameters. Scheduling the requests according to the priorities for ensuring better QoS quotient can be effectively done using hashgraph. We demonstrated how the hashgraph outstrips other equivalents platforms. The proposed model was simulated using OMNeT++ with proper design and network description files. A hardware implementation of the proposed model was also done. Messages were transferred between the vehicles and prioritized using a hashgraph. This paper proposes an effective model in reducing the accidents in terms of parameters like speed, security, stability, and fairness. Full article

(This article belongs to the Special Issue Industry 4.0: From Future of IoT to Industrial IoT)

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Review

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18 pages, 1123 KiB

Open AccessReview

Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future

by Rojeena Bajracharya, Rakesh Shrestha and Haejoon Jung

Sensors 2020, 20(10), 2774; https://doi.org/10.3390/s20102774 - 13 May 2020

Cited by 46 | Viewed by 5551

Abstract

This paper aims to unlock the unlicensed band potential in realizing the Industry 4.0 communication goals of the Fifth-Generation (5G) and beyond. New Radio in the Unlicensed band (NR-U) is a new NR Release 16 mode of operation that has the capability to offer the necessary technology for cellular operators to integrate the unlicensed spectrum into 5G networks. NR-U enables both uplink and downlink operation in unlicensed bands, supporting 5G advanced features of ultra-high-speed, high bandwidth, low latency, and improvement in the reliability of wireless communications, which is essential to address massive-scale and highly-diverse future industrial networks. This paper highlights NR-U as a next-generation communication technology for smart industrial network communication and discusses the technology trends adopted by 5G in support of the Industry 4.0 revolution. However, due to operation in the shared/unlicensed spectrum, NR-U possesses several regulatory and coexistence challenges, limiting its application for operationally intensive environments such as manufacturing, supply chain, transportation systems, and energy. Thus, we discuss the significant challenges and potential solution approaches such as shared maximum channel occupancy time (MCOT), handover skipping, the self-organized network (SON), the adaptive back-off mechanism, and the multi-domain coexistence approach to overcome the unlicensed/shared band challenges and boost the realization of NR-U technology in mission-critical industrial applications. Further, we highlight the role of machine learning in providing the necessary intelligence and adaptation mechanisms for the realization of industrial 5G communication goals. Full article

(This article belongs to the Special Issue Industry 4.0: From Future of IoT to Industrial IoT)

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