5G Mobile Communication for Intelligent Applications

A special issue of IoT (ISSN 2624-831X).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2254

Special Issue Editor


E-Mail Website
Guest Editor
School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC 3220, Australia
Interests: 5G mobile communication for intellegent applications; artificial intellegence for data hungry smart application; cloud IoT applications; networked unmanned aerial vehicles for fault detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is widely known that 5G is a promising technology to significantly enhance the speed and integration of many other novel paradigms to provide flexible and seamless services and applications. Artificial Intelligence (AI) learns from data and helps 5G cloud services and applications. Nevertheless, 5G is still in its infancy stage of deployment, and certain unique challenges need to be addressed. This Special Issue particularly focuses on collecting high-quality research work to show how AI can further help in 5G’s quick deployment and can address challenges related to this. Although AI is potential candidate to address deployment issues, however, AI has some inherent limitations which we also need to address. This is to provide a clear insight on the use of AI-driven 5G networks, its challenges, and research opportunities.

This Special Issue calls for high-quality unpublished research works on recent advances related to the application and deployment of AI-enabled, 5G-driven mobile networks. Open research problems, performance evaluation, and comparisons with existing solutions, theoretical as well as experimental studies, and use cases enabled by recent advances in 5G mobile communications are encouraged. High-quality review papers are also welcomed.

Potential topics include but are not limited to the following:

  • Theoretical approaches and methodologies for AI-enabled 5G networks; 
  • AI for 5G network management;
  • AI-based security applications in 5G networking;
  • AI-enabled dynamic network slicing; 
  • AI-enabled security methods for IoT, SDN, NFV; 
  • AI-based network authentication approaches; 
  • Energy-efficient network operation in the IoT;
  • AI to address heterogeneity in 5G networks.  

Dr. Morshed U. Chowdhury
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. IoT is an international peer-reviewed open access quarterly 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 1200 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
  • SDN
  • IoT
  • NFV
  • security

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

26 pages, 876 KiB  
Article
Efficient Sensing Data Collection with Diverse Age of Information in UAV-Assisted System
by Yanhua Pei, Fen Hou, Guoying Zhang and Bin Lin
IoT 2023, 4(3), 319-344; https://doi.org/10.3390/iot4030015 - 21 Aug 2023
Viewed by 1277
Abstract
With the high flexibility and low cost of the deployment of UAVs, the application of UAV-assisted data collection has become widespread in the Internet of Things (IoT) systems. Meanwhile, the age of information (AoI) has been adopted as a key metric to evaluate [...] Read more.
With the high flexibility and low cost of the deployment of UAVs, the application of UAV-assisted data collection has become widespread in the Internet of Things (IoT) systems. Meanwhile, the age of information (AoI) has been adopted as a key metric to evaluate the quality of the collected data. Most of the literature generally focuses on minimizing the age of all information. However, minimizing the overall AoI may lead to high costs and massive energy consumption. In addition, not all types of data need to be updated highly frequently. In this paper, we consider both the diversity of different tasks in terms of the data update period and the AoI of the collected sensing information. An efficient data collection method is proposed to maximize the system utility while ensuring the freshness of the collected information relative to their respective update periods. This problem is NP-hard. With the decomposition, we optimize the upload strategy of sensor nodes at each time slot, as well as the hovering positions and flight speeds of UAVs. Simulation results show that our method ensures the relative freshness of all information and reduces the time-averaged AoI by 96.5%, 44%, 90.4%, and 26% when the number of UAVs is 1 compared to the corresponding EMA, AOA, DROA, and DRL-eFresh, respectively. Full article
(This article belongs to the Special Issue 5G Mobile Communication for Intelligent Applications)
Show Figures

Figure 1

Back to TopTop