Special Issue "Constrained Deep Reinforcement Learning for Energy Sustainable IoT Networks"

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

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 310

Special Issue Editors

Prof. Dr. Jerry Chun Wei Lin
E-Mail Website
Guest Editor
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
Interests: AI/DL/ML; big data analytics; optimization; IoTs; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Ming-Tai Wu
E-Mail Website
Guest Editor
College of Computer Science and Engineering, Shandong University of Science ad Technology, Qingdao, China
Interests: AI; ML; data analytics
Dr. Mu-En Wu
E-Mail Website
Guest Editor
Department of Information and Finance Management, National Taipei University of Technology (NTUT), Taipei 10608, Taiwan
Interests: ML; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

It is known to all that revealing valuable information and making a wise decision from a complicated situation is the critical contribution of Artificial Intelligence. Recently, there have been many fantastic deep learning frameworks (such as Artificial Neural Network) proposed to resolve complex issues and obtain surprising results. Unfortunately, a real applied environment has more resource limitations, especially in an IoT environment. Endless data stream and poor terminal computation ability cause the traditional deep learning framework to be hardly applied. This Special Issue focuses on the environment of Energy Sustainable IoT, the critical challenge of the proposed methods is the endless and mess input data from an IoT network. Furthermore, it needs to continuously update the training model to fit the latest update. We encourage researchers to share their latest state-of-the-art solutions to interesting problems in this domain.

Prof. Dr. Jerry Chun Wei Lin
Dr. Ming-Tai Wu
Dr. Mu-En Wu
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. 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 2300 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

  • IoT
  • deep learning
  • artificial intelligence
  • energy sustainable
  • artificial neural network

Published Papers

There is no accepted submissions to this special issue at this moment.
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