UAV and Mobile Edge Computing for 6G Communication

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 1878

Special Issue Editors


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Guest Editor
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Interests: UAV communication; mobile edge computing; secure communication

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Guest Editor
National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: UAV communication; mobile edge computing

E-Mail Website
Guest Editor
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Interests: MIMO communication
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Special Issue Information

Dear Colleagues,

The sixth-generation (6G) network is expected to provide substantially higher capacity and lower latency with massive device connections, enabling intelligent applications. However, these applications are often computationally intensive with higher computation requirements, imposing great pressure on the computation ability of mobile devices. Mobile edge computing (MEC), as a possible solution to alleviating this pressure, is attracting increasing interest from both industry and academia. In MEC networks, mobile devices’ computation tasks can be offloaded to edge servers, effectively enhancing computation ability and reducing communication transmission delay. Furthermore, unmanned aerial vehicles (UAVs) can provide convenient and instantaneous services. An equipped MEC server on a UAV could flexibly provide effective offloading computation services and enhance communication transmission quality for mobile devices.

This Special Issue aims to disseminate the latest research on the UAV and mobile edge computing for 6G communication. Reviews and survey papers on this topic are welcome. Potential topics include, but are not limited to:

  • Theoretical framework of UAV and MEC;
  • Intelligent UAV and MEC;
  • RIS-aided UAV and MEC;
  • Energy efficiency of UAV and MEC networks;
  • Interference management for UAV and MEC networks;
  • UAV- and MEC-enabled D2D networks;
  • UAV- and MEC-enabled heterogeneous networks;
  • Network architecture and protocols for UAV and MEC;
  • Resource allocation in UAV and MEC;
  • Security issues for UAV and MEC;
  • Testbed design and implementation for UAV- and MEC-aided 6G networks.

Prof. Dr. Weidang Lu
Prof. Dr. Su Hu
Dr. Huimei Han
Guest Editors

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Keywords

  • UAV communication
  • mobile edge computing
  • resource allocation
  • inteligent communication
  • secure communication

Published Papers (1 paper)

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Research

15 pages, 500 KiB  
Article
Unmanned Aerial Vehicle-Assisted Federated Learning Method Based on a Trusted Execution Environment
by Jia Liao, Baihui Jiang, Peng Zhao, Lei Ning and Liming Chen
Electronics 2023, 12(18), 3938; https://doi.org/10.3390/electronics12183938 - 18 Sep 2023
Cited by 3 | Viewed by 1188
Abstract
In the face of increasing concerns around privacy and security in the use of unmanned aerial vehicles (UAVs) for mobile edge computing (MEC), this study proposes a novel approach to secure UAV-assisted federated learning. This research integrates a trusted execution environment (TEE) into [...] Read more.
In the face of increasing concerns around privacy and security in the use of unmanned aerial vehicles (UAVs) for mobile edge computing (MEC), this study proposes a novel approach to secure UAV-assisted federated learning. This research integrates a trusted execution environment (TEE) into UAV-assisted federated learning and proposes a robust aggregation algorithm based on cosine distance, denoted as CosAvg. This study further designs and evaluates a TEE-based federated learning model, comparing its resource overhead with other secure aggregation frameworks, like homomorphic encryption (HE) and differential privacy (DP). Experimental results indicate a significant reduction in resource overhead for TEE against DP and HE. Moreover, the proposed CosAvg algorithm demonstrated superior robustness against adversarial scenarios, maintaining high accuracy in the presence of malicious clients. The integration of TEE and the CosAvg algorithm provides a secure and robust solution for UAV-assisted federated learning, effectively defending both gradient inversion attacks and byzantine attacks. Full article
(This article belongs to the Special Issue UAV and Mobile Edge Computing for 6G Communication)
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