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Dear Colleagues,

The convergence of 5G and mobile edge computing has brought many opportunities and challenges for research and industries. Many IoT applications can benefit from 5G and mobile edge computing. Specifically, the tremendous speed of 5G may facilitate mobile edge computing tasks, such as task offloading, distributed caching, and quality of service optimization. The coupling of mobile edge computing and 5G will mitigate the drawbacks of traditional cloud computing models and take full advantage of the unexploited computing resources available in edge devices. Briefly, 5G-enabled mobile edge computing can be leveraged in the context of highly mobile application scenarios such as the Internet of Vehicles (IoV), mobile IoT, and ubiquitous computing. This Special Issue will focus on “Advances in 5G Wireless Edge Computing”.

The potential research topics include, but are not limited to, the following areas:

Deep learning application for task-offloading in mobile edge computing;
5G-enabled mobile edge computing applications;
Distributed caching for mobile edge computing;
Artificial intelligence-enabled fog and edge computing;
Vehicular edge computing and vehicular edge applications;
Blockchain caching for fog and edge computing;
Machine learning for quality of service (QoS) optimization in mobile edge computing;
Mobile edge computing for distributed social networks;
Mobile edge computing for Internet of Vehicles applications;
Security and privacy in mobile edge computing applications;
Security and privacy in 5G and beyond networks.

All papers submitted to the Special Issue will be thoroughly reviewed by at least two independent experts.

Dr. Nyothiri Aung
Dr. Tao Zhu
Dr. Sahraoui Dhelim
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. 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 2000 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

artificial intelligence in 5G
mobile edge computing
edge computing
5G and beyond networks
fog computing
deep learning
cloud computing
machine learning
Internet of Things
task offloading
distributed caching

Published Papers (2 papers)

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Research

Open AccessArticle

WGM-dSAGA: Federated Learning Strategies with Byzantine Robustness Based on Weighted Geometric Median

Hongqi Zhang

and

Electronics 2023, 12(5), 1190; https://doi.org/10.3390/electronics12051190 - 01 Mar 2023

Viewed by 372

Abstract

Federated learning techniques accomplish federated modeling and share global models without sharing data. Federated learning offers a good answer to complex data and privacy security issues. Although there are many ways to target federated learning, Byzantine attacks are the ones we concentrate on. Byzantine attacks primarily impede learning by tampering with the local model parameters provided by a client to the master node throughout the federation learning process, leading to a final global model that diverges from the optimal solution. To address this problem, we combine aggregation rules with Byzantine robustness using a gradient descent optimization algorithm based on variance reduction. We propose a WGM-dSAGA method with Byzantine robustness, called weighted geometric median-based distributed SAGA. We replace the original mean aggregation strategy in the distributed SAGA with a robust aggregation rule based on weighted geometric median. When less than half of the clients experience Byzantine attacks, the experimental results demonstrate that our proposed WGM-dSAGA approach is highly robust to different Byzantine attacks. Our proposed WGM-dSAGA algorithm provides the optimal gap and variance under a Byzantine attack scenario. Full article

(This article belongs to the Special Issue Advances in 5G Wireless Edge Computing)

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Open AccessArticle

NT-GNN: Network Traffic Graph for 5G Mobile IoT Android Malware Detection

and

Electronics 2023, 12(4), 789; https://doi.org/10.3390/electronics12040789 - 04 Feb 2023

Viewed by 757

Abstract

IoT Android application is the most common implementation system in the mobile ecosystem. As assaults have increased over time, malware attacks will likely happen on 5G mobile IoT Android applications. The huge threat posed by malware to communication systems security has made it one of the main focuses of information security research. Therefore, this paper proposes a new graph neural network model based on a network traffic graph for Android malware detection (NT-GNN). While some current malware detection systems use network traffic data for detection, they ignore the complex structural relationships of network traffic, focusing exclusively on network traffic between pairs of endpoints. Additionally, our suggested network traffic graph neural network model (NT-GNN) considers the graph node and edge aspects, capturing the connection between various traffic flows and individual traffic attributes. We first extract the network traffic graph and then detect it using a novel graph neural network architecture. Finally, we experimented with the proposed NT-GNN model on the well-known Android malware CICAndMal2017 and AAGM datasets and achieved 97% accuracy. The results reflect the sophisticated nature of our methodology. Furthermore, we want to provide a new method for malicious code detection. Full article

(This article belongs to the Special Issue Advances in 5G Wireless Edge Computing)

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