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Electronics
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Artificial Intelligence and Future Implications of an ICT Convergence System...
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Artificial Intelligence and Future Implications of an ICT Convergence System and Network

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 2501

Special Issue Editor

Prof. Dr. Yeonwoo Lee

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Mokpo National University, Cheonggye-myeon, Muan-gun, Jeollanam-do, Korea
Interests: cognitive radio; smart grid; artificial intelligence algorithm; nature-inspired algorithm; 6G communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Upon the advent of the dramatic upgrade and development of existing communication systems, ICT convergence present in various fields of expertise, including artificial intelligent (AI)-aided applications in wireless communication systems, smart devices, cloud computing, advanced-imaging processing systems, smart-grid energy systems, healthcare, and bio-informatics. The trend of technology in the future is the increased use of intelligent algorithms in the field of ICT convergence systems. The emerging convergence technologies, at present, include the application of AI to designs, analysis, IoT, 5G/6G communication system, and the maintenance of wireless communication networks. Many studies appear in the literature concerning the use of AI in conventional wireless networks; however, to date, there are few compelling contributions concerning the practical intelligence algorithms of AI that are applicable to ICT convergence systems and networks.

In this Special Issue, we aim to explore the practical applications of AI to various ICT convergence systems and networks, including artificial intelligent (AI)-aided applications in wireless communication systems, smart devices, cloud computing, advanced-imaging processing system, smart-grid energy systems, healthcare, and bio-informatics. The topics of interest include, but are not limited to, the following areas:

  • AI algorithms applicable to wireless network optimizations;
  • AI-assisted resource allocation, MAC layer design for 5G/6G, or other advanced ICT convergence systems;
  • Advanced computing for IoT, 5G/6G, or other advanced ICT convergence system;
  • The integration of big data and AI for wireless networks, or other advanced ICT convergence systems;
  • AI, machine learning, and/or deep learning for IoT, or other specialized network designs;
  • AI and its intelligent algorithms in the smart-grid network and its smart devices;
  • Practical applications of AI in ICT convergence systems and/or networks;
  • Intelligent energy-saving management for 5G/6G, smart-grid network, or other advanced ICT convergence systems.

We look forward to receiving your contributions.

Prof. Dr. Yeonwoo Lee
Guest Editor

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. Electronics 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 2400 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

  • AI
  • ICT convergence
  • smart-grid network
  • wireless network
  • 5G/6G
  • integration of big data
  • AI-assisted resource allocation
  • IoT

Published Papers (1 paper)

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Research

20 pages, 10058 KiB  
Article
Design of Vessel Data Lakehouse with Big Data and AI Analysis Technology for Vessel Monitoring System
by Sun Park, Chan-Su Yang and JongWon Kim
Electronics 2023, 12(8), 1943; https://doi.org/10.3390/electronics12081943 - 20 Apr 2023
Cited by 4 | Viewed by 2011
Abstract
The amount of data in the maritime domain is rapidly increasing due to the increase in devices that can collect marine information, such as sensors, buoys, ships, and satellites. Maritime data is growing at an unprecedented rate, with terabytes of marine data being [...] Read more.
The amount of data in the maritime domain is rapidly increasing due to the increase in devices that can collect marine information, such as sensors, buoys, ships, and satellites. Maritime data is growing at an unprecedented rate, with terabytes of marine data being collected every month and petabytes of data already being made public. Heterogeneous marine data collected through various devices can be used in various fields such as environmental protection, defect prediction, transportation route optimization, and energy efficiency. However, it is difficult to manage vessel related data due to high heterogeneity of such marine big data. Additionally, due to the high heterogeneity of these data sources and some of the challenges associated with big data, such applications are still underdeveloped and fragmented. In this paper, we propose the Vessel Data Lakehouse architecture consisting of the Vessel Data Lake layer that can handle marine big data, the Vessel Data Warehouse layer that supports marine big data processing and AI, and the Vessel Application Services layer that supports marine application services. Our proposed a Vessel Data Lakehouse that can efficiently manage heterogeneous vessel related data. It can be integrated and managed at low cost by structuring various types of heterogeneous data using an open source-based big data framework. In addition, various types of vessel big data stored in the Data Lakehouse can be directly utilized in various types of vessel analysis services. In this paper, we present an actual use case of a vessel analysis service in a Vessel Data Lakehouse by using AIS data in Busan area. Full article
(This article belongs to the Special Issue Artificial Intelligence and Future Implications of an ICT Convergence System and Network)
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