Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Recent Advances of Cloud, Edge, and Parallel Computing
share announcement

Recent Advances of Cloud, Edge, and Parallel Computing

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1497

Special Issue Editors

Dr. Seongah Jeong

E-Mail Website
Guest Editor
School of Electronics Engineering, Kyungpook National University, Daegu 14566, Republic of Korea
Interests: edge and cloud computing; aerospace and vehicular communications; wireless power transfer and physical-layer security technologies
Dr. Jin-Hyun Ahn

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Myongji University, Yongin, Gyeonggi, Republic of Korea
Interests: federated learning; split learning; active learning; MEC-aided video streaming
Dr. Jin-kyu Kang

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Myongji University, Yongin-si 17058, Gyeonggi-do, Republic of Korea
Interests: wireless communication; signal processing; and distributed computing

Special Issue Information

Dear Colleagues,

The rapid advancement of the Internet of Things (IoT) and 5G technologies has accelerated the developments in computing on a large scale, which brings great opportunities for various fields of science, engineering, business, and everyday life. At the same time, challenges such as an architectural bottle neck occur, e.g., a significant number of devices connected to a rather small number of servers in cloud data centers, yielding the problem of data deluge. To alleviate the computational burden, edge computing and fog computing are alternatives of cloud computing, distributing some of the computations and logics of processing from the cloud to the edge. However, several challenges remain to be addressed, such as the issue of a balanced workload among the computing nodes in a parallel and distributed manner, security, low-complexity and latency, etc. To this end, computing architectures need to be capable of implementing parallel and distributed algorithms efficiently.

This Special Issue aims to solicit conceptual, theoretical and experimental contributions to address the unsolved issues in the field of cloud, edge and parallel computing. The topics of interest include, but are not limited to, the following:

Topics:

(1) Optimization for cloud, edge and parallel computing;

(2) Learning-based algorithms for cloud, edge and parallel computing;

(3) Resource allocation and scheduling in cloud, edge and parallel computing;

(3) MIMO/RIS/IRS techniques for cloud, edge and parallel computing;

(4) Complexity analysis in cloud, edge and parallel computing;

(5) Scalability issues in in cloud, edge and parallel computing;

(6) Security issues in cloud, edge and parallel computing;

(7) Management and orchestration in cloud, edge and parallel computing;

(8) Architecture of cloud, edge and parallel computing;

(9) Applications of cloud, edge and parallel computing in next-generation networks;

(10) Advanced algorithms in cloud, edge and parallel computing.

Dr. Seongah Jeong
Dr. Jin-Hyun Ahn
Dr. Jin-kyu Kang
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

  • cloud computing
  • edge computing
  • fog computing
  • parallel computing
  • distributed computing
  • optimization
  • learning
  • security

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

23 pages, 2787 KiB  
Article
Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT
by Chanthol Eang, Seyha Ros, Seungwoo Kang, Inseok Song, Prohim Tam, Sa Math and Seokhoon Kim
Electronics 2024, 13(7), 1218; https://doi.org/10.3390/electronics13071218 - 26 Mar 2024
Viewed by 384
Abstract
Internet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to be completed quickly within the time [...] Read more.
Internet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to be completed quickly within the time constraints of IoT devices. To address this challenge, researchers have proposed computation offloading, where computing tasks are sent to edge servers instead of being executed locally on user devices. This approach involves using edge servers located near users in cellular network base stations, and also known as Mobile Edge Computing (MEC). The goal is to offload tasks to edge servers, optimizing both latency and energy consumption. The main objective of this paper mentioned in the summary is to design an algorithm for time- and energy-optimized task offloading decision-making in MEC environments. Therefore, we developed a Lagrange Duality Resource Optimization Algorithm (LDROA) to optimize for both decision offloading and resource allocation for tasks, whether to locally execute or offload to an edge server. The LDROA technique produces superior simulation outcomes in terms of task offloading, with improved performance in computation latency and cost usage compared to conventional methods like Random Offloading, Load Balancing, and the Greedy Latency Offloading scheme. Full article
(This article belongs to the Special Issue Recent Advances of Cloud, Edge, and Parallel Computing)
►▼ Show Figures

Figure 1

Review

Jump to: Research

37 pages, 3219 KiB  
Review
Digital Twin-Enabled Internet of Vehicles Applications
by Junting Gao, Chunrong Peng, Tsutomu Yoshinaga, Guorong Han, Siri Guleng and Celimuge Wu
Electronics 2024, 13(7), 1263; https://doi.org/10.3390/electronics13071263 - 28 Mar 2024
Viewed by 737
Abstract
The digital twin (DT) paradigm represents a groundbreaking shift in the Internet of Vehicles (IoV) landscape, acting as an instantaneous digital replica of physical entities. This synthesis not only refines vehicular design but also substantially augments driver support systems and streamlines traffic governance. [...] Read more.
The digital twin (DT) paradigm represents a groundbreaking shift in the Internet of Vehicles (IoV) landscape, acting as an instantaneous digital replica of physical entities. This synthesis not only refines vehicular design but also substantially augments driver support systems and streamlines traffic governance. Diverging from the prevalent research which predominantly examines DT’s technical assimilation within IoV infrastructures, this review focuses on the specific deployments and goals of DT within the IoV sphere. Through an extensive review of scholarly works from the past 5 years, this paper provides a fresh and detailed perspective on the significance of DT in the realm of IoV. The applications are methodically categorized across four pivotal sectors: industrial manufacturing, driver assistance technology, intelligent transportation networks, and resource administration. This classification sheds light on DT’s diverse capabilities to confront and adapt to the intricate challenges in contemporary vehicular networks. The intent of this comprehensive overview is to catalyze innovation within IoV by providing an essential reference for researchers who aspire to swiftly grasp the complex dynamics of this evolving domain. Full article
(This article belongs to the Special Issue Recent Advances of Cloud, Edge, and Parallel Computing)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop