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Edge Computing Architectures in Industry 4.0
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Edge Computing Architectures in Industry 4.0

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 14683

Special Issue Editors

Dr. Ricardo S. Alonso Rincón

E-Mail Website
Guest Editor
1. AIR Institute, Deep Tech Lab, Paseo de Belén 9A, 47011 Valladolid, Spain
2. BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
3. Higher School of Engineering and Technology, International University of La Rioja (UNIR), Logroño, Spain
Interests: Internet of Things; edge computing; distributed ledger and blockchain technologies; embedded systems; indoor location systems; cloud computing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Óscar García

E-Mail Website
Guest Editor
UNIR - Universidad Internacional de La Rioja, de García Martín, 21, 28224 Pozuelo de Alarcón, Madrid, Spain
Interests: big data; Artificial Intelligence; IoT; Industry 4.0; energy efficiency
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel A. Sánchez Vidales

E-Mail Website
Guest Editor
International University of La Rioja, Spain
Interests: Internet of Things; Industry 4.0; Edge Computing; Mobile Computing, Multi-Agent Systems

Special Issue Information

Dear Colleagues,

It will soon be ten years since the term Industry 4.0 was first mentioned at the Hannover Fair in 2011. The fourth industrial revolution brings with it an ecosystem of enabling technologies such as cyberphysical systems, robotics, cybersecurity, big data analytics, Artificial Intelligence and predictive maintenance, additive manufacturing, and of course, the Industrial Internet of Things. Thanks to IIoT devices and platforms, all the elements involved in production processes are connected to each other and to the cloud, where they are represented by their digital twins. This makes it possible to characterize the different production processes in the industry’s value chain, extract value-added knowledge, and apply Big Data Analytics techniques in the cloud, including predictive and prescriptive maintenance, as well as detection of anomalous patterns using machine learning techniques. However, various challenges and limitations arise when sending data to the cloud, such as the high energy consumption of IoT devices or the challenges regarding the security and privacy of the data transferred. Furthermore, cloud service providers charge their costs according to the amount of data that are transferred, processed and stored in the cloud. Finally, in an architecture based solely on an IoT layer and a cloud layer, services may be interrupted if communication with the cloud is cut off. In this sense, Edge Computing architectures allow pre-processing and filtering of the data being transferred to the cloud, reducing costs, avoiding security problems, and allowing machine learning models to be run at the edge of the network with lower latency and higher service availability.

For this purpose, this Special Issue will be focused on but not limited to the following topics:

  • Innovative edge computing architectures;
  • Industrial Internet of Things and edge computing;
  • Machine learning at the edge in Industry 4.0 scenarios;
  • Edge computing and cyber-physical systems;
  • Internet of Robotic Things and edge computing;
  • Management of additive manufacturing at the edge;
  • Integration of operational technology in edge computing architectures;
  • Innovative frameworks for managing data security and privacy at the edge;
  • Novel applications of edge computing and IoT in Industry 4.0 scenarios: heavy and light industry, agro-industry, smart energy, healthcare, smart transportation, smart farming, smart logistics, etc.

Dr. Ricardo S. Alonso
Dr. Óscar García
Dr. Miguel A. Sánchez Vidales
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
  • edge computing
  • Industry 4.0
  • cyber-physical systems
  • machine learning

Published Papers (4 papers)

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Research

20 pages, 578 KiB  
Article
QoS-Aware Algorithm Based on Task Flow Scheduling in Cloud Computing Environment
by Mohamed Ali Rakrouki and Nawaf Alharbe
Sensors 2022, 22(7), 2632; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072632 - 29 Mar 2022
Cited by 8 | Viewed by 2173
Abstract
This paper deals with the challenging problem of scheduling users’ tasks, while taking into consideration users’ quality of service (QoS) requirements, with the objective of reducing the energy consumption of physical machines. This paper presents a model to analyze the current state of [...] Read more.
This paper deals with the challenging problem of scheduling users’ tasks, while taking into consideration users’ quality of service (QoS) requirements, with the objective of reducing the energy consumption of physical machines. This paper presents a model to analyze the current state of the running tasks according to the results of the QoS prediction assigned by an ARIMA prediction model optimized with Kalman filter. Then, we calculate a scheduling policy with a combined particle swarm optimization (PSO) and gravitational search algorithm (GSA) algorithms according to the QoS status analysis. Experimental results show that the proposed HPSO algorithm reduces resources consumption 16.51% more than the original hybrid algorithm, and the violation of service-level agreement (SLA) is 0.053% less when the optimized prediction model is used. Full article
(This article belongs to the Special Issue Edge Computing Architectures in Industry 4.0)
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16 pages, 2110 KiB  
Article
Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market
by María E. Pérez-Pons, Ricardo S. Alonso, Oscar García, Goreti Marreiros and Juan Manuel Corchado
Sensors 2021, 21(16), 5276; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165276 - 04 Aug 2021
Cited by 15 | Viewed by 2755
Abstract
Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the [...] Read more.
Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent. Full article
(This article belongs to the Special Issue Edge Computing Architectures in Industry 4.0)
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22 pages, 4265 KiB  
Article
Intelligent Platform Based on Smart PPE for Safety in Workplaces
by Sergio Márquez-Sánchez, Israel Campero-Jurado, Jorge Herrera-Santos, Sara Rodríguez and Juan M. Corchado
Sensors 2021, 21(14), 4652; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144652 - 07 Jul 2021
Cited by 16 | Viewed by 6405
Abstract
It is estimated that we spend one-third of our lives at work. It is therefore vital to adapt traditional equipment and systems used in the working environment to the new technological paradigm so that the industry is connected and, at the same time, [...] Read more.
It is estimated that we spend one-third of our lives at work. It is therefore vital to adapt traditional equipment and systems used in the working environment to the new technological paradigm so that the industry is connected and, at the same time, workers are as safe and protected as possible. Thanks to Smart Personal Protective Equipment (PPE) and wearable technologies, information about the workers and their environment can be extracted to reduce the rate of accidents and occupational illness, leading to a significant improvement. This article proposes an architecture that employs three pieces of PPE: a helmet, a bracelet and a belt, which process the collected information using artificial intelligence (AI) techniques through edge computing. The proposed system guarantees the workers’ safety and integrity through the early prediction and notification of anomalies detected in their environment. Models such as convolutional neural networks, long short-term memory, Gaussian Models were joined by interpreting the information with a graph, where different heuristics were used to weight the outputs as a whole, where finally a support vector machine weighted the votes of the models with an area under the curve of 0.81. Full article
(This article belongs to the Special Issue Edge Computing Architectures in Industry 4.0)
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18 pages, 6218 KiB  
Article
A Framework for Off-Line Operation of Smart and Traditional Devices of IoT Services
by Chung-Yen Wu and Kuo-Hsuan Huang
Sensors 2020, 20(21), 6012; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216012 - 23 Oct 2020
Cited by 4 | Viewed by 2128
Abstract
Recently, with the continuous evolution of information technology, various products such as Building Information, Internet of Things (IoT), Big Data, Cloud Computing and Machine Learning have been developed and have created a lifestyle change. A smart Internet of Things (IoT) system is formed [...] Read more.
Recently, with the continuous evolution of information technology, various products such as Building Information, Internet of Things (IoT), Big Data, Cloud Computing and Machine Learning have been developed and have created a lifestyle change. A smart Internet of Things (IoT) system is formed by combining the communication capabilities of the internet with control, monitoring and identification services to integrate people, things and objects. However, in some IoT environments that have a weak signal, such as remote areas, warehouses or basements, the network may become unstable, meaning that the IoT system is unable to provide efficient services. This paper therefore presents a framework that ensures the reliability of IoT system services so that even if the IoT system cannot connect to the network, the system can provide the services offline. To avoid increasing the installation cost or replacing existing traditional devices with modern smart devices, this framework can also be used to control traditional devices. The system operation is convenient because users can operate all their smart and traditional devices under the IoT system through voice commands and/or a handheld microcontroller, thus reducing the manual operation of the user. The framework proposed in this paper can be applied to various smart scenarios, including smart warehouses, smart restaurants, smart homes, smart farms and smart factories, to improve people’s quality of life and convenience, and create a humane and comfortable smart living environment. Full article
(This article belongs to the Special Issue Edge Computing Architectures in Industry 4.0)
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