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Edge Computing Applied to the Industrial Environment

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 2618

Special Issue Editors


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Guest Editor
Department of Electronic Technology, Escuela Politécnica Superior, Universidad de Sevilla, 41011 Sevilla, Spain
Interests: computational intelligence; big data; blockchain; advanced analytics; smart grids; machine learning; heterogeneous data source integration; high performance computing; standard interoperability

E-Mail Website
Guest Editor
Department of Electronic Technology, Escuela Politécnica Superior, Universidad de Sevilla, 41011 Sevilla, Spain
Interests: electronic instrumentation; artificial intelligence; network management; GDMO; expert systems

Special Issue Information

Dear Colleagues,

Edge computing is new paradigm, providing high technology achievements and applications. The main objective of this Special Issue is to show the advances on Edge Computing applied to different real or simulated Industrial Environments: Agriculture, Smart Cities, Transport, Logistics, etc. and for different purposes: security, efficiency, processing, etc., involving the integration with other technologies like Internet of Things, Blockchain, digital twin, robotics, advanced analytics, and other techniques based on Artificial Intelligence. The Special Issue would make significant contributions to both the theory and practice of Edge Computing.

Edge computing technologies are becoming an integral part of our daily life due to the proliferation of different types of Internet-enabled device with computation capabilities. Managing the massive amount of data generated by these devices in an efficient, secure, and economical way is a challenging task. Edge computing have been provided a promising technique that can be used to build a secure and robust mechanism of managing data generated by the different devices. This Special Issue aims to attract contributions from a broader domain of enabler technologies of edge computing-driven solutions. Potential topics include but are not limited to the following:

  • Edge Computing and Smart Cities;
  • Edge Computing in smart grids, smart industry, smart vehicles, and smart homes;
  • Edge Computing applications;
  • Security and privacy mechanisms for Edge Computing;
  • Communications and energy efficiency in Edge Computing;

Prof. Dr. Juan I. Guerrero
Dr. Antonio Martin-Montes
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Bell Ding via <[email protected]>

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.

 

Published Papers (1 paper)

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Research

22 pages, 1215 KiB  
Article
Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation
by Junaid Akram, Arsalan Tahir, Hafiz Suliman Munawar, Awais Akram, Abbas Z. Kouzani and M A Parvez Mahmud
Sensors 2021, 21(23), 7846; https://doi.org/10.3390/s21237846 - 25 Nov 2021
Cited by 13 | Viewed by 2254
Abstract
The smart grid (SG) is a contemporary electrical network that enhances the network’s performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To [...] Read more.
The smart grid (SG) is a contemporary electrical network that enhances the network’s performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant. Full article
(This article belongs to the Special Issue Edge Computing Applied to the Industrial Environment)
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