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Electronics
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Edge Computing for 5G and Internet of Things
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Edge Computing for 5G and Internet of Things

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

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 2443

Special Issue Editor

Dr. Sheng Zhang

E-Mail Website
Guest Editor
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
Interests: edge computing; edge intelligence; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of edge computing with 5G technology and the Internet of Things (IoT) has revolutionized the way we process, analyze, and utilize data. This dynamic combination addresses the limitations of traditional cloud-centric approaches by bringing the computation closer to the data source, resulting in enhanced speed, reduced latency, and improved efficiency.

Edge computing refers to the decentralized processing of data at or near its point of origin, eliminating the need to transmit vast amounts of information to remote data centers. When applied to the realm of 5G and IoT, this concept becomes particularly impactful. The fifth generation of wireless technology, 5G, offers exceptional bandwidth and significantly lower latency, enabling seamless connectivity for IoT devices. However, the massive influx of data generated by these devices poses challenges in terms of processing and response time. This is where edge computing steps in, providing localized data analysis and real-time decision-making capabilities.

In the context of IoT, edge computing's benefits are multifaceted. It enables rapid data processing for time-sensitive applications such as autonomous vehicles, industrial automation, and healthcare monitoring. By processing data closer to the source, edge computing reduces the strain on network infrastructure and enhances security by minimizing data exposure. Moreover, it facilitates data filtering, ensuring that only relevant information is transmitted to the cloud, conserving bandwidth and reducing costs.

However, challenges remain. Designing and managing edge computing systems require addressing issues like resource constraints, reliability, and compatibility. Furthermore, ensuring data privacy and security in distributed environments is of paramount importance.

This Special Issue strives to be a platform for publishing innovative techniques, which are essential to address technical, regulatory, and ethical considerations to fully harness the transformative power of edge computing for 5G and IoT.

Dr. Sheng Zhang
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

  • edge computing
  • Internet of Things
  • 5G
  • edge intelligence
  • resource scheduling
  • reliability
  • latency-sensitive applications

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Published Papers (2 papers)

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Research

24 pages, 2303 KiB  
Article
Probabilistic Task Offloading with Uncertain Processing Times in Device-to-Device Edge Networks
by Chang Shu, Yinhui Luo and Fang Liu
Electronics 2024, 13(10), 1889; https://doi.org/10.3390/electronics13101889 - 11 May 2024
Viewed by 924
Abstract
D2D edge computing is a promising solution to address the conflict between limited network capacity and increasing application demands, where mobile devices can offload their tasks to other peer devices/servers for better performance. Task offloading is critical to the performance of D2D edge [...] Read more.
D2D edge computing is a promising solution to address the conflict between limited network capacity and increasing application demands, where mobile devices can offload their tasks to other peer devices/servers for better performance. Task offloading is critical to the performance of D2D edge computing. Most existing works on task offloading assume the task processing time is known or can be accurately estimated. However, the processing time is often uncertain until it is finished. Moreover, the same task can have largely different execution times under different scenarios, which leads to inaccurate offloading decisions and degraded performance. To address this problem, we propose a game-based probabilistic task offloading scheme with an uncertain processing time in D2D edge networks. First, we characterize the uncertainty of the task processing time using a probabilistic model. Second, we incorporate the proposed probabilistic model into an offloading decision game. We also analyze the structural properties of the game and prove that it can reach a Nash equilibrium. We evaluate the proposed work using real-world applications and datasets. The experimental results show that the proposed probabilistic model can accurately characterize the uncertainty of completion time, and the offloading algorithm can effectively improve the overall task completion rate in D2D networks. Full article
(This article belongs to the Special Issue Edge Computing for 5G and Internet of Things)
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21 pages, 1128 KiB  
Article
Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach
by Feixiang Li, Kai Qu, Mingzhe Liu, Ning Li and Tian Sun
Electronics 2024, 13(10), 1804; https://doi.org/10.3390/electronics13101804 - 7 May 2024
Cited by 1 | Viewed by 1111
Abstract
With the increasing dissemination of the Internet of Things and 5G, mobile edge computing has become a novel scheme to assist terminal devices in executing computation tasks. To elevate the coverage and computation capability of edge computing, a collaborative computation offloading and resource [...] Read more.
With the increasing dissemination of the Internet of Things and 5G, mobile edge computing has become a novel scheme to assist terminal devices in executing computation tasks. To elevate the coverage and computation capability of edge computing, a collaborative computation offloading and resource management architecture was proposed in space–air–ground integrated networking (SAGIN). In this manuscript, we established a novel model considering the computation offloading cost constraints of the communication, computing and cache model in the SAGIN. To be specific, the joint optimization problem of collaborative computation offloading and resource management was modeled as a mixed integer nonlinear programming problem. To address this issue, this paper proposed a computation offloading and resource allocation strategy based on deep reinforcement learning (DRL). Differing from traditional methods, DRL does not need a well-established formulation or previous information, and it is capable of revising the strategy adaptively according to the environment. The simulation results demonstrate the proposed approach can achieve the optimal reward values in the case of different terminal device numbers. Furthermore, this manuscript provided the analysis with variant parameters of the proposed approach. Full article
(This article belongs to the Special Issue Edge Computing for 5G and Internet of Things)
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