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Electronics
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Advances in 5G Wireless Edge Computing
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Advances in 5G Wireless Edge Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 19300

Special Issue Editors

Dr. Nyothiri Aung

E-Mail Website
Guest Editor
School of Computer Science, University College Dublin, Dublin, Ireland
Interests: medical image analysis; intelligent transportation systems; IoT; social networks analysis; mobile edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Zhu

E-Mail Website
Guest Editor
School of Computer science, University of South China, Hengyang 421001, China
Interests: IoT; pervasive computing; assisted living and evolutionary computation
Special Issues, Collections and Topics in MDPI journals
Dr. Sahraoui Dhelim

E-Mail Website
Guest Editor
School of Computer Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
Interests: IoT; social computing; intelligent transportation systems; IoT; social networks analysis; mobile edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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 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

  • 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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

27 pages, 1405 KiB  
Article
Multi-Agent Deep Reinforcement Learning-Based Inference Task Scheduling and Offloading for Maximum Inference Accuracy under Time and Energy Constraints
by Abdelkarim Ben Sada, Amar Khelloufi, Abdenacer Naouri, Huansheng Ning, Nyothiri Aung and Sahraoui Dhelim
Electronics 2024, 13(13), 2580; https://doi.org/10.3390/electronics13132580 - 30 Jun 2024
Cited by 2 | Viewed by 2501
Abstract
The journey towards realizing real-time AI-driven IoT applications is facing a significant hurdle caused by the limited resources of IoT devices. Particularly for battery-powered edge devices, the decision between performing task inference locally or by offloading to edge servers, all while ensuring timely [...] Read more.
The journey towards realizing real-time AI-driven IoT applications is facing a significant hurdle caused by the limited resources of IoT devices. Particularly for battery-powered edge devices, the decision between performing task inference locally or by offloading to edge servers, all while ensuring timely results and conserving energy, is a critical issue. This problem is further complicated when an edge device houses multiple local inference models. The challenge of effectively allocating inference models to tasks between local models and edge server models under strict time and energy constraints while maximizing overall accuracy is recognized as a strongly NP-hard problem and has not been addressed in the literature. Therefore, in this work we propose MASITO, a novel multi-agent deep reinforcement learning framework designed to address this intricate problem. By dividing the problem into two sub-problems namely task scheduling and edge server selection we propose a cooperative multi-agent system for addressing each sub-problem. MASITO’s design allows for faster training and more robust schedules using cooperative behavior where agents compensate for each other’s sub-optimal actions. Moreover, MASITO dynamically adapts to different network configurations which allows for high-mobility edge computing applications. Experiments on the ImageNet-mini dataset demonstrate the framework’s efficacy, outperforming genetic algorithms (GAs), simulated annealing (SA), and particle swarm optimization (PSO) in scheduling times by providing lower times ranging from 60% up to 90% while maintaining comparable average accuracy in worst-case scenarios and superior accuracy in best-case scenarios. Full article
(This article belongs to the Special Issue Advances in 5G Wireless Edge Computing)
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29 pages, 3287 KiB  
Article
Cache Optimization Methods Involving Node and Content Sharding of Blockchain in Internet of Vehicles
by Yawen Zhao and Nan Ding
Electronics 2024, 13(3), 560; https://doi.org/10.3390/electronics13030560 - 30 Jan 2024
Cited by 2 | Viewed by 1497
Abstract
Blockchain stands out in addressing the data security requirements of the Internet of Vehicles. However, blockchain has storage pressure that cannot be met by most existing nodes. The emergence of Mobile Edge Computing allows nodes closer to the users to undertake the caching [...] Read more.
Blockchain stands out in addressing the data security requirements of the Internet of Vehicles. However, blockchain has storage pressure that cannot be met by most existing nodes. The emergence of Mobile Edge Computing allows nodes closer to the users to undertake the caching and computation process. Although sharding can alleviate the storage pressure on blockchain nodes, frequent cross-shard communication can affect the overall performance of the blockchain. In this paper, combining the features of traffic flow with strong regional similarity as well as inter-node correlation, we propose two sharding methods based on the current Vehicle–Infrastructure–Clouds three-tier service model. The proposed Content Sharding method can optimize node caching and improve the cache-hitting ratio. The proposed node sharding method can effectively reduce the system service delay by assisting nodes to cache the whole blockchain together across the network. Full article
(This article belongs to the Special Issue Advances in 5G Wireless Edge Computing)
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18 pages, 3850 KiB  
Article
Joint Optimization of Task Caching and Computation Offloading for Multiuser Multitasking in Mobile Edge Computing
by Xintong Zhu, Zongpu Jia, Xiaoyan Pang and Shan Zhao
Electronics 2024, 13(2), 389; https://doi.org/10.3390/electronics13020389 - 17 Jan 2024
Cited by 2 | Viewed by 1993
Abstract
Mobile edge computing extends the capabilities of the cloud to the edge to meet the latency performance required by new types of applications. Task caching reduces network energy consumption by caching task applications and associated databases in advance on edge devices. However, determining [...] Read more.
Mobile edge computing extends the capabilities of the cloud to the edge to meet the latency performance required by new types of applications. Task caching reduces network energy consumption by caching task applications and associated databases in advance on edge devices. However, determining an effective caching strategy is crucial since users generate numerous repetitive tasks, but edge devices and storage resources are limited. We aimed to address the problem of highly coupled decision variables in dynamic task caching and computational offloading for multiuser multitasking in mobile edge computing systems. This paper presents a joint computation and caching framework with the aim of minimizing delays and energy expenditure for mobile users and transforming the problem into a form of reinforcement learning. Based on this, an improved deep reinforcement learning algorithm, P-DDPG, is proposed to achieve efficient computation offloading and task caching decisions for mobile users. The algorithm integrates a deep and deterministic policy grading and a prioritized empirical replay mechanism to reduce system costs. The simulations show that the designed algorithm performs better in terms of task latencies and lower computing power consumption. Full article
(This article belongs to the Special Issue Advances in 5G Wireless Edge Computing)
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24 pages, 6198 KiB  
Article
Resource Allocation in UAV-Enabled NOMA Networks for Enhanced Six-G Communications Systems
by Mostafa Mahmoud El-Gayar and Mohammed Nasser Ajour
Electronics 2023, 12(24), 5033; https://doi.org/10.3390/electronics12245033 - 17 Dec 2023
Cited by 10 | Viewed by 2712
Abstract
Enhancing energy efficiency, content distribution, latency, and transmission speeds are vital components of communication systems. Multiple access methods hold great promise for boosting these performance indicators. This manuscript evaluates the effectiveness of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) systems within [...] Read more.
Enhancing energy efficiency, content distribution, latency, and transmission speeds are vital components of communication systems. Multiple access methods hold great promise for boosting these performance indicators. This manuscript evaluates the effectiveness of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) systems within a single cell, where users are scattered randomly and rely on relays for dependability. This paper presents a model for improving energy efficiency, content distribution, latency, and transmission speeds in communication systems using NOMA and OMA systems within a single cell. Additionally, this paper also proposes a caching strategy using unmanned aerial vehicles (UAVs) as aerial base stations for ground users. These UAVs distribute cached content to minimize the overall latency of content demands from ground users while modifying their positions. We carried out simulations using various cache capacities and user counts linked to their respective UAVs. Furthermore, we evaluated OMA and NOMA in terms of the achievable rate and energy efficiency. The proposed model has achieved noteworthy enhancement across various scenarios including different sum rates, numbers of mobility users, diverse cache sizes, and amounts of power allocation. Full article
(This article belongs to the Special Issue Advances in 5G Wireless Edge Computing)
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22 pages, 4021 KiB  
Article
EdgeSVDNet: 5G-Enabled Detection and Classification of Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images
by Anas Bilal, Xiaowen Liu, Talha Imtiaz Baig, Haixia Long and Muhammad Shafiq
Electronics 2023, 12(19), 4094; https://doi.org/10.3390/electronics12194094 - 29 Sep 2023
Cited by 48 | Viewed by 1972
Abstract
The rise of vision-threatening diabetic retinopathy (VTDR) underscores the imperative for advanced and efficient early detection mechanisms. With the integration of the Internet of Things (IoT) and 5G technologies, there is transformative potential for VTDR diagnosis, facilitating real-time processing of the burgeoning volume [...] Read more.
The rise of vision-threatening diabetic retinopathy (VTDR) underscores the imperative for advanced and efficient early detection mechanisms. With the integration of the Internet of Things (IoT) and 5G technologies, there is transformative potential for VTDR diagnosis, facilitating real-time processing of the burgeoning volume of fundus images (FIs). Combined with artificial intelligence (AI), this offers a robust platform for managing vast healthcare datasets and achieving unparalleled disease detection precision. Our study introduces a novel AI-driven VTDR detection framework that integrates multiple models through majority voting. This comprehensive approach encompasses pre-processing, data augmentation, feature extraction using a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model, and classification through an enhanced SVM-RBF combined with a decision tree (DT) and K-nearest neighbor (KNN). Validated on the IDRiD dataset, our model boasts an accuracy of 99.89%, a sensitivity of 84.40%, and a specificity of 100%, marking a significant improvement over the traditional method. The convergence of the IoT, 5G, and AI technologies herald a transformative era in healthcare, ensuring timely and accurate VTDR diagnoses, especially in geographically underserved regions. Full article
(This article belongs to the Special Issue Advances in 5G Wireless Edge Computing)
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13 pages, 3902 KiB  
Article
WGM-dSAGA: Federated Learning Strategies with Byzantine Robustness Based on Weighted Geometric Median
by Xiaoxue Wang, Hongqi Zhang, Anas Bilal, Haixia Long and Xiaowen Liu
Electronics 2023, 12(5), 1190; https://doi.org/10.3390/electronics12051190 - 1 Mar 2023
Cited by 3 | Viewed by 2421
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. [...] Read more.
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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17 pages, 4758 KiB  
Article
NT-GNN: Network Traffic Graph for 5G Mobile IoT Android Malware Detection
by Tianyue Liu, Zhenwan Li, Haixia Long and Anas Bilal
Electronics 2023, 12(4), 789; https://doi.org/10.3390/electronics12040789 - 4 Feb 2023
Cited by 17 | Viewed by 4949
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 [...] Read more.
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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