Special Issue "Edge Artificial Intelligence in Future Sustainable Computing Systems"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 1 December 2021.

Special Issue Editors

Prof. Dr. Arun Kumar Sangaiah
E-Mail Website
Guest Editor
School of Computing Science and Engineering, Vellore (632014) Institute of Technology (VIT), Vellore, India
Interests: e-learning; machine learning; software engineering; computational intelligence; IoT
Special Issues and Collections in MDPI journals
Dr. Xi Zheng
E-Mail Website
Guest Editor
Department of Computing, Macquarie University, Sydney, Australia
Interests: service computing; IoT security and reliability analysis
Dr. Changqing Luo
E-Mail Website
Guest Editor
College of Engineering, Virginia Commonwealth University, Richmond (E4249), Virginia, United States
Interests: security; privacy; complex networks
Dr. Shuihua Wang
E-Mail Website
Guest Editor
Department of Mathematics, University of Leicester, Leicester (LE1 7RH), United Kingdom
Interests: deep learning; biomedical image analysis; pattern recognition; transfer learning; data analysis
Dr. Ankit Chaudhary
E-Mail Website
Guest Editor
Department of Computer Science, University of Missouri–St. Louis, Saint Louis (MO 63121), United States
Interests: data science and cyber security

Special Issue Information

Dear Colleagues,

Living in the era of machines and automated systems, the impact of Artificial Intelligence (AI) in people lives cannot be ignored. In a regular day of a human being starting from morning until night, the role of edge/smart devices play in computing, communicating, entertainment, work, and various other aspects of our lives is enormous. It is not an understatement to say AI has become a part of our life, and we greatly rely on its services. Edge AI is an emerging computing model which allows IoT data management and service supply to be moved from cloud to the local edge devices (IoT-connected devices at the edge) which might grow exponentially into billions of connected devices.

Similarly, sustainable computing has been extended to become a key research area covering the fields of computer science and engineering, electrical engineering, and other engineering disciplines. Recently, we have been witnessing more works being published on sustainable computing that include bio-energy efficiency, natural resources preservation, and emphasize the role of AI in achieving system design and operation objectives. The sustainable bio-energy impact/design of more efficient edge infrastructure is a key challenge for organizations to realize new intelligent computing paradigms. Thus, the uses of edge AI techniques for intelligent decision support being exploited to create effective computing systems.

The data being generated are increasing exponentially every year. This has pushed the capability of machine learning systems to largely extract and learn information from the underlying data. The current expansion of Edge AI demands new computing and networking infrastructure in sustainable environments in industrial systems. Hence, it is becoming challenging for Edge computing to deal with these emerging IoT environments.

Various businesses have started incorporating AI systems in their work. All these together have opened doors to boundless opportunities for innovative research and ideas in the field of Edge computing and AI with sustainable computing. The aim of this Special Issue is to cover all the latest developments and progress made in the field of AI and edge Computing, ranging over a variety of topics of sustainable computing and other related domains. Each contribution should describe in detail the use of artificial intelligence/smart computing/evolutionary computing in the field of edge industrial with sustainable computing and Internet of Things (IoT) application areas.

Prof. Dr. Arun Kumar Sangaiah
Dr. Xi Zheng
Dr. Changqing Luo
Dr. Shuihua Wang
Dr. Ankit Chaudhary
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. Sustainability 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 1900 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

  • Edge Computing
  • Artificial Intelligence
  • Sustainable Computing
  • Internet of Things
  • Big Data

Published Papers (1 paper)

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Research

Article
A Study on the Influence of Number/Distribution of Sensing Points of the Smart Insoles on the Center of Pressure Estimation for the Internet of Things Applications
Sustainability 2021, 13(5), 2934; https://doi.org/10.3390/su13052934 - 08 Mar 2021
Viewed by 611
Abstract
The past decade has seen the emergence of numerous new wearable devices, including many that have been widely adopted by both physicians and consumers. In this paper, we discuss the design and application of smart insoles to measure gait and plantar pressure. Herein, [...] Read more.
The past decade has seen the emergence of numerous new wearable devices, including many that have been widely adopted by both physicians and consumers. In this paper, we discuss the design and application of smart insoles to measure gait and plantar pressure. Herein, we investigate the potential applications of insoles with fewer sensing spots and the consequent reduction in the amount of data acquired from both feet. The main purpose is to discuss the influence of the layout of these pressure sensing points of the insole design on the center of pressure (COP) calculation. The insole used in this study has 89 pressure sensing spots, and we used data from 36, 29, 20, and 11 sensing points in simplified calculation types. Among these four simplified calculation types, Type 1 exhibited the best accuracy of the COP calculation, and Type 4 obtained the worst results. Type 2 and Type 3 exhibited inferior accuracy of the COP calculation, but they still sufficed for applications that did not require high accuracy. Aside from the factor of the number of sensing spots used in the calculation, we also demonstrated that the location of selected sensors could influence the accuracy of COP calculation in the analyses by using the different combinations of metatarsal areas and other areas (heel, central, lateral toes, and hallux). The results of this research could be a reference for making a simplified form of pressure sensing Internet-of-Health Things (IoHT) insole with a reduced product cost. Full article
(This article belongs to the Special Issue Edge Artificial Intelligence in Future Sustainable Computing Systems)
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