Special Issue "Recent Trends in Innovation for Industry 4.0 Sensor Networks"

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708).

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editors

Dr. Akhlaqur Rahman
Guest Editor
School of Industrial Automation and Electrical Engineering, Engineering Institute of Technology, Melbourne, VIC 3000, Australia
Interests: cloud robotics; task offloading; Industry 4.0; smart manufacturing; Internet of Things (IoT); decision making; optimization
Dr. Shuva Paul
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 papers will be 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 quarterly 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 1000 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.


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

Published Papers (1 paper)

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Open AccessArticle
Face Recognition in an Unconstrained and Real-Time Environment Using Novel BMC-LBPH Methods Incorporates with DJI Vision Sensor
J. Sens. Actuator Netw. 2020, 9(4), 54; https://doi.org/10.3390/jsan9040054 (registering DOI) - 28 Nov 2020
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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