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Recent Trends in Innovation for Industry 4.0 Sensor Networks
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Recent Trends in Innovation for Industry 4.0 Sensor Networks

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Network Services and Applications".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 21545

Special Issue Editors

Dr. Akhlaqur Rahman

E-Mail Website
Guest Editor
School of Industrial Automation and Electrical Engineering, Engineering Institute of Technology, Melbourne, VIC 3000, Australia
Interests: task offloading; optimization; IoT communication; augmented reality; cyber security; virtual manufacturing system
Dr. Shuva Paul

E-Mail Website
Guest Editor
Washington State University, Pullman, WA, USA
Interests: computational intelligence; machine learning; game theory; cyber physical system; power system security

Special Issue Information

Dear colleagues,

The emergence of cloud computing, Internet of Things (IoT), and industrial wireless technology has elevated the potential of integrating autonomous sensing and actuation in the evolving dynamic and complex industrial applications. Following the first three revolutions of “Mechanization”, “Mass Production”, and “Digitization,” the 4th industrial revolution (Industry 4.0) has brought emerging autonomous technologies in the industrial realm, thus transforming traditional factories into smart factories of the future. Due to all the attributes of virtualization, decentralization, and real-time capability, Industry 4.0 is envisioned to be a key area for infusion of these sensor networks and technologies, especially in automating applications, such as sensing, actuating, and monitoring via the insurgence of new and improved applications of sensor networks, which includes progress in sensor deployment approaches, context-awareness, faster threat detection and mitigation for sensor network security, and smoother integration with cloud through fog and edge technologies.

In order to further improve the state-of-the-art facilities of industrial wireless sensor networks (IWSN), numerous challenges are still being faced in each of these contexts to ensure reliable, robust, adaptable, and resilient provisioning of services to meet the requirements for quality of service (QoS). The Special Issue targets scientific contributions on the development, innovations, and implementations of these industrial wireless sensor networks, deployed for real-time Industry 4.0 applications. Topics include but are not limited to:

* Industrial IoT testbeds;

* Testbed design for Industry 4.0 wireless sensor networks;

* Integration of Industry 4.0 WSN with cloud processing capabilities;

* Industry 4.0 WSN for medical/healthcare services and applications;

* Industry 4.0 WSN for advanced monitoring and control strategies of critical infrastructures;

* Big data and machine learning applications in IWSN;

* Real-time resource provisioning for IWSN;

* Integration to Augmented/Virtual Reality to sensor networks for smart manufacturing;

* Threat/intrusion detection and adversarial interaction for IWSN;

* Development of sensor networks through edge and fog computing;

* Use cases of IWSN enabled for Industry 4.0 applications.

Dr. Akhlaqur Rahman
Dr. Shuva Paul
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. Journal of Sensor and Actuator Networks 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 2000 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

  • industrial wireless sensor network
  • industrial IoT
  • real-time resource provisioning
  • threat detection
  • network security
  • edge computing

Published Papers (3 papers)

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Research

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18 pages, 1621 KiB  
Article
A Study on Sensor System Latency in VR Motion Sickness
by Ripan Kumar Kundu, Akhlaqur Rahman and Shuva Paul
J. Sens. Actuator Netw. 2021, 10(3), 53; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10030053 - 06 Aug 2021
Cited by 12 | Viewed by 5006
Abstract
One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and [...] Read more.
One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in VR devices. The error between the predicted data and the actual data is compared for statistical methods and deep learning techniques. The Kalman Filtering method is suitable for predicting since it is quicker to predict; however, the error is relatively high. However, the error property is good for the Dead Reckoning algorithm, even though the curve fitting is not satisfactory compared to Kalman Filtering. To overcome this poor performance, we adopted deep-learning-based LSTM for prediction. The LSTM showed improved performance when compared to the Dead Reckoning and Kalman Filtering algorithm. The simulation results suggest that the deep learning techniques outperformed the statistical methods in terms of error comparison. Overall, Convolutional LSTM outperformed the other deep learning techniques (much better than LSTM and Bidirectional LSTM) in terms of error. Full article
(This article belongs to the Special Issue Recent Trends in Innovation for Industry 4.0 Sensor Networks)
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12 pages, 4475 KiB  
Article
Face Recognition in an Unconstrained and Real-Time Environment Using Novel BMC-LBPH Methods Incorporates with DJI Vision Sensor
by Md Manjurul Ahsan, Yueqing Li, Jing Zhang, Md Tanvir Ahad and Munshi Md. Shafwat Yazdan
J. Sens. Actuator Netw. 2020, 9(4), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan9040054 - 28 Nov 2020
Cited by 12 | Viewed by 3694
Abstract
Face recognition (FR) in an unconstrained environment, such as low light, illumination variations, and bad weather is very challenging and still needs intensive further study. Previously, numerous experiments on FR in an unconstrained environment have been assessed using Eigenface, Fisherface, and Local binary [...] Read more.
Face recognition (FR) in an unconstrained environment, such as low light, illumination variations, and bad weather is very challenging and still needs intensive further study. Previously, numerous experiments on FR in an unconstrained environment have been assessed using Eigenface, Fisherface, and Local binary pattern histogram (LBPH) algorithms. The result indicates that LBPH FR is the optimal one compared to others due to its robustness in various lighting conditions. However, no specific experiment has been conducted to identify the best setting of four parameters of LBPH, radius, neighbors, grid, and the threshold value, for FR techniques in terms of accuracy and computation time. Additionally, the overall performance of LBPH in the unconstrained environments are usually underestimated. Therefore, in this work, an in-depth experiment is carried out to evaluate the four LBPH parameters using two face datasets: Lamar University data base (LUDB) and 5_celebrity dataset, and a novel Bilateral Median Convolution-Local binary pattern histogram (BMC-LBPH) method was proposed and examined in real-time in rainy weather using an unmanned aerial vehicle (UAV) incorporates with 4 vision sensors. The experimental results showed that the proposed BMC-LBPH FR techniques outperformed the traditional LBPH methods by achieving the accuracy of 65%, 98%, and 78% in 5_celebrity dataset, LU dataset, and rainy weather, respectively. Ultimately, the proposed method provides a promising solution for facial recognition using UAV. Full article
(This article belongs to the Special Issue Recent Trends in Innovation for Industry 4.0 Sensor Networks)
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Review

Jump to: Research

32 pages, 2598 KiB  
Review
Industry 4.0 Applications for Medical/Healthcare Services
by Shuva Paul, Muhtasim Riffat, Abrar Yasir, Mir Nusrat Mahim, Bushra Yasmin Sharnali, Intisar Tahmid Naheen, Akhlaqur Rahman and Ambarish Kulkarni
J. Sens. Actuator Netw. 2021, 10(3), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10030043 - 30 Jun 2021
Cited by 45 | Viewed by 12079
Abstract
At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In [...] Read more.
At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm. Full article
(This article belongs to the Special Issue Recent Trends in Innovation for Industry 4.0 Sensor Networks)
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