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Applied Sciences
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Edge Computing in 6G Networks
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Edge Computing in 6G 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 September 2023) | Viewed by 4856

Special Issue Editors

Dr. Yueyue Dai

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: MEC; federated learning; blockchain
Special Issues, Collections and Topics in MDPI journals
Prof. Shu Fu

E-Mail Website
Guest Editor
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: B5G/6G ultra-dense cellular network; UAV; low orbit satellite communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the upcoming sixth-generation mobile communication system (6G), the needs of users will be further explored and realized, and ongoing technological planning and evolutions will be achieved. With the development of artificial intelligence (AI), edge computing, and digital twin technologies, intelligent applications will be deeply integrated with edge services to realize various functions, such as virtual users and intelligent networks. However, wireless devices with limited resources may not efficiently process intelligent applications. Additionally, network users are becoming more aware of privacy and are reluctant to share their private data with the outside world. Therefore, this Special Issue aims to offer a platform for researchers from academia and industry alike to publish their recent research findings and to discuss the opportunities, challenges, and solutions related to edge intelligence in 6G networks. We welcome the submission of original research papers on state-of-the-art approaches, methodologies, and technologies which enable edge intelligence in 6G networks. Potential topics of interest include, but are not limited to, the following:

  • New architectures and frameworks of edge intelligence in 6G networks.
  • Novel concepts, theories, principles, and algorithms of edge intelligence in 6G networks.
  • Data compression in 6G networks.
  • Resource management for edge intelligence in 6G networks.
  • Privacy, trust, and security issues on edge intelligence in 6G networks.
  • Digital twin for 6G networks.
  • Network virtualization for edge intelligence in 6G networks.

Dr. Yueyue Dai
Dr. Shu Fu
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 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

  • edge intelligence
  • 6G
  • resource management
  • privacy and security
  • network virtualization
  • digital twin

Published Papers (3 papers)

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Research

16 pages, 23939 KiB  
Article
Autonomous Control System with Passive Positioning for Unmanned-Aerial-Vehicle-Assisted Edge Communication in 6G
by Yue Hu, Yunzhe Jiang, Yinqiu Liu and Xiaoming He
Appl. Sci. 2023, 13(19), 11014; https://doi.org/10.3390/app131911014 - 06 Oct 2023
Viewed by 567
Abstract
UAVs can be deployed in many scenarios to provide various types of services via 6G edge communication. In these scenarios, it is necessary to obtain the position of the UAVs in a timely and accurate manner to avoid UAV collisions. In this paper, [...] Read more.
UAVs can be deployed in many scenarios to provide various types of services via 6G edge communication. In these scenarios, it is necessary to obtain the position of the UAVs in a timely and accurate manner to avoid UAV collisions. In this paper, we consider improved passive localization algorithms aimed at reducing convergence time and adapting to extreme conditions. For the sake of reducing the complexity of signals and ensuring the reliability of receiving processes, we reconsidered the angle between arrival signals as the feature in positioning. Then, according to the characteristics of the positioning process, we draw on the cyclical process of the iterative greedy algorithm to construct the coding, destruction, and reorganization process to guide the movement of the UAV. Moreover, an improved Metropolis criterion is added to prevent falling into the local optimal solution. Finally, the proposed algorithm is verified in the simulation results. The results show that the algorithm can achieve precise positioning and excellent track planning within a small number of iterations, and it reduces the amount of information carried by the signal and convergence time compared with the traditional method. Full article
(This article belongs to the Special Issue Edge Computing in 6G Networks)
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14 pages, 5542 KiB  
Article
A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
by Yifei Hu, Wenbin Gong and Fangming Zhou
Appl. Sci. 2023, 13(7), 4281; https://doi.org/10.3390/app13074281 - 28 Mar 2023
Cited by 2 | Viewed by 1215
Abstract
Satellite edge computing (SEC) has garnered significant attention for its potential to deliver services directly to users. However, the uneven distribution of receiving tasks among satellites in the constellation can lead to uneven utilization of computing resources. This paper proposes a task offloading [...] Read more.
Satellite edge computing (SEC) has garnered significant attention for its potential to deliver services directly to users. However, the uneven distribution of receiving tasks among satellites in the constellation can lead to uneven utilization of computing resources. This paper proposes a task offloading strategy for SEC that aims to minimize the average delay and energy consumption of tasks by assigning them to appropriate satellite nodes. The approach uses Lyapunov optimization to convert the long-term optimization problem with task queue length constraints into an assignment problem within a single time slot and solve it based on the Hungarian algorithm. Experimental simulations have shown that the proposed algorithm performs better than other baseline algorithms. Full article
(This article belongs to the Special Issue Edge Computing in 6G Networks)
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11 pages, 1118 KiB  
Article
Software-Defined Heterogeneous Edge Computing Network Resource Scheduling Based on Reinforcement Learning
by Yaofang Li and Bin Wu
Appl. Sci. 2023, 13(1), 426; https://doi.org/10.3390/app13010426 - 29 Dec 2022
Cited by 3 | Viewed by 2485
Abstract
With the rapid development of wireless networks, wireless edge computing networks have been widely considered. The heterogeneous characteristics of the 6G edge computing network bring new challenges to network resource scheduling. In this work, we consider a heterogeneous edge computing network with heterogeneous [...] Read more.
With the rapid development of wireless networks, wireless edge computing networks have been widely considered. The heterogeneous characteristics of the 6G edge computing network bring new challenges to network resource scheduling. In this work, we consider a heterogeneous edge computing network with heterogeneous edge computing nodes and task requirements. We design a software-defined heterogeneous edge computing network architecture to separate the control layer and the data layer. According to different requirements, the tasks in heterogeneous edge computing networks are decomposed into multiple subtasks at the control layer, and the edge computing node alliance responding to the tasks is established to perform the decomposed subtasks. In order to optimize both network energy consumption and network load balancing, we model the resource scheduling problem as a Markov Decision Process (MDP), and design a Proximal Policy Optimization (PPO) resource scheduling algorithm based on deep reinforcement learning. Simulation analysis shows that the proposed PPO resource scheduling can achieve low energy consumption and ideal load balancing. Full article
(This article belongs to the Special Issue Edge Computing in 6G Networks)
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