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Computation Offloading for Mobile-Edge/Fog Computing
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Computation Offloading for Mobile-Edge/Fog Computing

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 1071

Special Issue Editor

Dr. Luobing Dong

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: mobile-edge/fog computing; artificial intelligence; Internet of Things; big data

Special Issue Information

Dear Colleagues,

Compared to traditional cloud computing models, mobile edge/fog computing models offer advantages such as real-time data processing and analysis, high security, privacy protection, strong scalability, location awareness, and low bandwidth consumption, having become a supporting platform for emerging Internet of Things (IoT) applications. Computation offloading, which transfers compute-intensive and latency-sensitive tasks from mobile or terminal devices to edge/fog node servers, is a key technology in mobile edge/fog computing and directly impacts its service quality. Although existing computation offloading solutions have made significant progress, challenges still remain as the scale of mobile edge/fog computing expands and scenarios become more complex. These challenges primarily involve the performance of computation offloading solutions in real-world scenarios (e.g., latency, energy consumption); mobility (e.g., mobility of terminal devices and service nodes); security (e.g., security of edge/fog nodes, data security, network security); reliability (e.g., fault tolerance); and heterogeneity (e.g., incompatibility of devices and data types). Furthermore, with the rapid development of artificial intelligence technologies, the integration of computation offloading with artificial intelligence provides a new approach to address these challenges. This Special Issue is aimed at addressing the issues of computation offloading in mobile edge/fog computing and improving the service quality.

Topics of interest include, but are not limited to:

  • Computation offloading strategies and optimization methods for mobile edge/fog computing;
  • Security and privacy of data in computation offloading;
  • Reliability and fault tolerance of computation offloading solutions;
  • Caching strategies for computation offloading;
  • Optimal resource allocation for mobile edge/fog computing nodes;
  • Computation offloading solutions for heterogeneous edge/fog nodes;
  • Multi-user computation offloading optimization solutions;
  • Trust evaluation and authentication mechanisms for edge/fog nodes;
  • Dynamic task computation offloading solutions;
  • Joint optimization of computation offloading, resource allocation, and task scheduling;
  • AI-based computation offloading solutions;
  • Solutions for computation offloading solutions in related fields, such as vehicular networks, IoT, virtual reality, and drones.

Dr. Luobing Dong
Guest Editor

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

  • moblie edge/fog computing
  • computation offloading
  • security and privacy
  • security
  • reliability intelligence

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Published Papers (2 papers)

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Research

24 pages, 10988 KiB  
Article
Neural Network Implementation for Fire Detection in Critical Infrastructures: A Comparative Analysis on Embedded Edge Devices
by Jon Aramendia, Andrea Cabrera, Jon Martín, Jose Ángel Gumiel and Koldo Basterretxea
Electronics 2025, 14(9), 1809; https://doi.org/10.3390/electronics14091809 - 29 Apr 2025
Abstract
This paper explores the application of artificial intelligence on edge devices to enhance security in critical infrastructures, with a specific focus on the use case of a battery-powered mobile system for fire detection in tunnels. The study leverages the YOLOv5 convolutional neural network [...] Read more.
This paper explores the application of artificial intelligence on edge devices to enhance security in critical infrastructures, with a specific focus on the use case of a battery-powered mobile system for fire detection in tunnels. The study leverages the YOLOv5 convolutional neural network (CNN) for real-time detection, focusing on a comparative analysis across three low-power platforms, NXP i.MX93, Xilinx Kria KV260, and NVIDIA Jetson Orin Nano, evaluating their performance in terms of detection accuracy (mAP), inference time, and energy consumption. The paper also presents a methodology for implementing neural networks on various platforms, aiming to provide a scalable approach to edge artificial intelligence (AI) deployment. The findings offer valuable insights into the trade-offs between computational efficiency and power consumption, guiding the selection of edge computing solutions in security-critical applications. Full article
(This article belongs to the Special Issue Computation Offloading for Mobile-Edge/Fog Computing)
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17 pages, 2073 KiB  
Article
Few-Shot Learning with Multimodal Fusion for Efficient Cloud–Edge Collaborative Communication
by Bo Gao, Xing Liu and Quan Zhou
Electronics 2025, 14(4), 804; https://doi.org/10.3390/electronics14040804 - 19 Feb 2025
Viewed by 532
Abstract
As demand for high-capacity, low-latency communication rises, mmWave systems are essential for enabling ultra-high-speed transmission in fifth-generation mobile communication technology (5G) and upcoming 6G networks, especially in dynamic, data-scarce environments. However, deploying mmWave systems in dynamic environments presents significant challenges, especially in beam [...] Read more.
As demand for high-capacity, low-latency communication rises, mmWave systems are essential for enabling ultra-high-speed transmission in fifth-generation mobile communication technology (5G) and upcoming 6G networks, especially in dynamic, data-scarce environments. However, deploying mmWave systems in dynamic environments presents significant challenges, especially in beam selection, where limited training data and environmental variability hinder optimal performance. In such scenarios, computation offloading has emerged as a key enabler, allowing computationally intensive tasks to be shifted from resource-constrained edge devices to powerful cloud servers, thereby reducing latency and optimizing resource utilization. This paper introduces a novel cloud–edge collaborative approach integrating few-shot learning (FSL) with multimodal fusion to address these challenges. By leveraging data from diverse modalities—such as red-green-blue (RGB) images, radar signals, and light detection and ranging (LiDAR)—within a cloud–edge architecture, the proposed framework effectively captures spatiotemporal features, enabling efficient and accurate beam selection with minimal data requirements. The cloud server is tasked with computationally intensive training, while the edge node focuses on real-time inference, ensuring low-latency decision making. Experimental evaluations confirm the model’s robustness, achieving high beam selection accuracy under one-shot and five-shot conditions while reducing computational overhead. This study highlights the potential of combining cloud–edge collaboration with FSL and multimodal fusion for next-generation wireless networks, paving the way for scalable, intelligent, and adaptive mmWave communication systems. Full article
(This article belongs to the Special Issue Computation Offloading for Mobile-Edge/Fog Computing)
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