Special Issue "Edge Computing Applications in IoT"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 August 2019

Special Issue Editors

Guest Editor
Prof. Dr. Eui-Nam Huh

Department of Computer Science & Engineering, College of Software, Kyung Hee University, Seoul 02447, Republic of Korea
Website | E-Mail
Phone: +82-31-201-3778
Interests: cloud computing; edge computing, Internet of Things; future internet; distributed real-time systems; mobile computing; Big Data and security
Guest Editor
Dr. Mohammad Aazam

Senior Research Scientist, Carnegie Mellon University, Doha 24866, Qatar
Website | E-Mail
Interests: fog/edge computing; Internet of Things; smart healthcare; smart cities

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is prevalent in our daily life, and edge computing (EC) has become an active research field supporting low processing power, real-time response time, and more resource capacity than IoT and mobile devices. It has also been considered to effectively mitigate loads on data centers, to assist artificial intelligence (AI) services, and to increase 5G services. Therefore, edge computing applications along with the IoT field are essential technical directions in order to open the door to new opportunities enabling smart homes, smart hospitals, smart cities, smart vehicles, smart wearables, smart supply chain, e-health, automation, and a variety of other smart environments. It is helpful not only to provide human-oriented services at a lower cost, but also to create an intelligent eco-environment. Highly-researched topics have included infrastructure planning; frameworks, protocols, and algorithms for the IoT; novel intelligent hardware or software platforms; security and energy efficiency; and more. Accordingly, this Special Issue encourages authors to present their recent work showing edge computing applications in the IoT, and provides a unique opportunity for both technology and applied science to meet.

Prof. Dr. Eui-Nam Huh
Dr. Mohammad Aazam
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. Applied Sciences 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 1500 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.


  • new edge computing architectures, frameworks, platforms, and protocols for IoT
  • middleware for distributed computations and data management in edge computing for IoT
  • resource management and energy efficiency in edge computing for IoT
  • modeling and performance analysis in edge computing for IoT
  • reliable, low-latency communication and networking in edge computing for IoT
  • machine learning techniques in edge computing for IoT
  • offloading techniques for computation- and data-intensive IoT applications
  • volunteer or outsourcing computing for edge computing extension
  • trust, security, policy, and privacy issues in edge computing for IoT
  • optimization, control, and automation in edge computing for IoT
  • novel applications, experiences, and field trials with edge computing for IoT

Published Papers (1 paper)

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Open AccessArticle An Affordable Fast Early Warning System for Edge Computing in Assembly Line
Appl. Sci. 2019, 9(1), 84; https://doi.org/10.3390/app9010084
Received: 10 December 2018 / Revised: 20 December 2018 / Accepted: 23 December 2018 / Published: 26 December 2018
PDF Full-text (5058 KB) | HTML Full-text | XML Full-text
Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model [...] Read more.
Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events. Full article
(This article belongs to the Special Issue Edge Computing Applications in IoT)

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