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Electronics
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Advanced Technologies in Edge Computing and Applications
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Advanced Technologies in Edge Computing and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 1492

Special Issue Editors

Dr. Yuchao Zhang

E-Mail Website
Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: data-driven network; NetAI; large-scale datacenter network; cloud and edge computing
Dr. Yi Zhao

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: cyberspace security; artificial intelligence security; internet architecture

Special Issue Information

Dear Colleagues, 

The rapid evolution of edge computing has positioned it as a critical facilitator of the next generation of Internet and intelligent applications. Among the most significant advancements in this field is the deployment of large models (e.g., LLM) in edge devices; the field promises to revolutionize large models, with their potential for real-time, localized data analysis and decision-making. Differentiated from cloud computing, this paradigm shift is crucial for applications requiring real-time capabilities, low latency, and high bandwidth, such as the Internet of Things (IoT), 5G networks, autonomous vehicles, and more.

This Special Issue aims to bring together researchers, academics, and industry practitioners to share their latest findings, innovations, and challenges in the field of edge computing. We invite high-quality original research papers that address theoretical, experimental, or applied aspects of edge computing.

Contributions that are welcome may include, but are not limited to, the following topics:

  • Edge computing for large models;
  • Edge computing for big data and analytics;
  • Edge computing for blockchain applications;
  • Privacy protection for edge computing;
  • Adversarial attacks and defenses in edge computing.

Dr. Yuchao Zhang
Dr. Yi Zhao
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

  • edge computing
  • federated learning
  • large models
  • security
  • privacy

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

Published Papers (3 papers)

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Research

22 pages, 3438 KiB  
Article
A High-Accuracy Advanced Persistent Threat Detection Model: Integrating Convolutional Neural Networks with Kepler-Optimized Bidirectional Gated Recurrent Units
by Guangwu Hu, Maoqi Sun and Chaoqin Zhang
Electronics 2025, 14(9), 1772; https://doi.org/10.3390/electronics14091772 - 27 Apr 2025
Viewed by 114
Abstract
Advanced Persistent Threat (APT) refers to a highly targeted, sophisticated, and prolonged form of cyberattack, typically directed at specific organizations or individuals. The primary objective of such attacks is the theft of sensitive information or the disruption of critical operations. APT attacks are [...] Read more.
Advanced Persistent Threat (APT) refers to a highly targeted, sophisticated, and prolonged form of cyberattack, typically directed at specific organizations or individuals. The primary objective of such attacks is the theft of sensitive information or the disruption of critical operations. APT attacks are characterized by their stealth and complexity, often resulting in significant economic losses. Furthermore, these attacks may lead to intelligence breaches, operational interruptions, and even jeopardize national security and political stability. Given the covert nature and extended durations of APT attacks, current detection solutions encounter challenges such as high detection difficulty and insufficient accuracy. To address these limitations, this paper proposes an innovative high-accuracy APT attack detection model, CNN-KOA-BiGRU, which integrates Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and the Kepler optimization algorithm (KOA). The model first utilizes CNN to extract spatial features from network traffic data, followed by the application of BiGRU to capture temporal dependencies and long-term memory, thereby forming comprehensive temporal features. Simultaneously, the Kepler optimization algorithm is employed to optimize the BiGRU network structure, achieving globally optimal feature weights and enhancing detection accuracy. Additionally, this study employs a combination of sampling techniques, including Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links, to mitigate classification bias caused by dataset imbalance. Evaluation results on the CSE-CIC-IDS2018 experimental dataset demonstrate that the CNN-KOA-BiGRU model achieves superior performance in detecting APT attacks, with an average accuracy of 98.68%. This surpasses existing methods, including CNN (93.01%), CNN-BiGRU (97.77%), and Graph Convolutional Network (GCN) (95.96%) on the same dataset. Specifically, the proposed model demonstrates an accuracy improvement of 5.67% over CNN, 0.91% over CNN-BiGRU, and 2.72% over GCN. Overall, the proposed model achieves an average improvement of 3.1% compared to existing methods. Full article
(This article belongs to the Special Issue Advanced Technologies in Edge Computing and Applications)
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22 pages, 3380 KiB  
Article
F2SOD: A Federated Few-Shot Object Detection
by Peng Li, Tianyu Zhang, Chen Qing and Shuzhuang Zhang
Electronics 2025, 14(8), 1651; https://doi.org/10.3390/electronics14081651 - 19 Apr 2025
Viewed by 219
Abstract
With the popularity of edge computation, object detection applications face challenges of limited data volume and data privacy. To address these, we propose a federated few-shot object detection framework, F2SOD. It involves three steps: collaborative base model training with base class [...] Read more.
With the popularity of edge computation, object detection applications face challenges of limited data volume and data privacy. To address these, we propose a federated few-shot object detection framework, F2SOD. It involves three steps: collaborative base model training with base class data, novel data augmentation via an improved diffusion model, and collaborative base model fine-tuning for novel model using augmented data. Specifically, we present a data augmentation method based on diffusion models with a twofold-tag prompt construction and object location embedding. In addition, we present distributed framework for training base and novel models, where the base model integrates the Squeeze-and-Excitation attention mechanism into the feature re-weighting module. Experiments on public datasets demonstrate that F2SOD achieves efficient few-shot object detection, outperforming State-of-the-Art methods in both accuracy and efficiency. Full article
(This article belongs to the Special Issue Advanced Technologies in Edge Computing and Applications)
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22 pages, 1818 KiB  
Article
Cooperative Service Caching and Task Offloading in Mobile Edge Computing: A Novel Hierarchical Reinforcement Learning Approach
by Tan Chen, Jiahao Ai, Xin Xiong and Guangwu Hu
Electronics 2025, 14(2), 380; https://doi.org/10.3390/electronics14020380 - 19 Jan 2025
Viewed by 932
Abstract
In the current mobile edge computing (MEC) system, the user dynamics, diversity of applications, and heterogeneity of services have made cooperative service caching and task offloading decision increasingly important. Service caching and task offloading have a naturally hierarchical structure, and thus, hierarchical reinforcement [...] Read more.
In the current mobile edge computing (MEC) system, the user dynamics, diversity of applications, and heterogeneity of services have made cooperative service caching and task offloading decision increasingly important. Service caching and task offloading have a naturally hierarchical structure, and thus, hierarchical reinforcement learning (HRL) can be used to effectively alleviate the dimensionality curse in it. However, traditional HRL algorithms are designed for short-term missions with sparse rewards, while existing HRL algorithms proposed for MEC lack delicate a coupling structure and perform poorly. This article introduces a novel HRL-based algorithm, named hierarchical service caching and task offloading (HSCTO), to solve the problem of the cooperative optimization of service caching and task offloading in MEC. The upper layer of HSCTO makes decisions on service caching while the lower layer is in charge of task offloading strategies. The upper-layer module learns policies by directly utilizing the rewards of the lower-layer agent, and the tightly coupled design guarantees algorithm performance. Furthermore, we adopt a fixed multiple time step method in the upper layer, which eliminates the dependence on the semi-Markov decision processes (SMDPs) theory and reduces the cost of frequent service replacement. We conducted numerical evaluations and the experimental results show that HSCTO improves the overall performance by 20%, and reduces the average energy consumption by 13% compared with competitive baselines. Full article
(This article belongs to the Special Issue Advanced Technologies in Edge Computing and Applications)
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