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Electronics
Special Issues
Deep Integration of Mobile/Edge Computing and AI
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Deep Integration of Mobile/Edge Computing and AI

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

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 1259

Special Issue Editors

Dr. Bo Gao

E-Mail Website
Guest Editor
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: wireless networking; multi-agent systems; Internet of Things; mobile/edge computing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Lu

E-Mail Website
Guest Editor
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: wireless networking; Internet of Things; mobile/edge computing; machine learning

Special Issue Information

Dear Colleagues,

As we embark on our journey toward the 6G era, we believe that it is the right time for academia and industry to think of and reflect on how artificial intelligence (AI) will reshape and revolutionize the future of mobile/edge computing. On one hand, AI can make mobile/edge computing more self-sufficient and more proactive. Next-generation mobile/edge computing systems will be equipped with native AI and cognitive capabilities, making data-driven operation of applications, services, and underlying platforms zero-touch and adaptive to changes. Furthermore, AI solutions will help mobile/edge computing systems to become aware of future needs, outcomes, or trends in advance, allowing their decision making to become predictive and informed, resulting in enhanced performance in terms of, e.g., efficiency, reliability, scalability, and security. On the other hand, mobile/edge computing can make AI more ubiquitous and powerful. The native AI and cognitive capabilities of next-generation mobile/edge computing systems will increasingly rely on the evolution and convergence of sensing, communication, computing, and control technologies. In particular, there will be a wide variety of mobile/edge platforms capable of real-time sensing and on-device learning, which can offer abundant resources of data and computing power for the deployment of AI algorithms, thus making AI universally accessible. Moreover, communication and collaboration among multiple mobile/edge platforms, acting as AI agents, will bring new opportunities for the generalization of AI solutions on massive yet decentralized data, thus paving the path toward connected intelligence.  

This Special Issue is devoted to bringing together original articles that contribute to all aspects of research and development related to the deep integration of mobile/edge computing and AI, aiming to shed light on the state of the art and push the boundaries of the computing landscape. This includes but is not limited to:

  • AI-native mobile/edge computing;
  • Mobile/edge-native AI;
  • On-device/lightweight/personalized machine learning for embedded/mobile/edge systems;
  • Online/real-time inference on embedded/mobile/edge systems;
  • Federated/distributed machine learning for embedded/mobile/edge systems;
  • Data management on embedded/mobile/edge systems;
  • AI-native 6G mobile communications and networks/Internet of Things;
  • Convergence of sensing, communication, computing, and control for mobile/edge AI;
  • AI-driven optimization in mobile/edge computing;
  • Hardware/software architectures for mobile/edge AI;
  • Secure/privacy-preserving AI for embedded/mobile/edge systems;
  • Explainable AI for embedded/mobile/edge systems;
  • Ethical AI for embedded/mobile/edge systems;
  • AI applications for embedded/mobile/edge systems.

Dr. Bo Gao
Dr. Yang Lu
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

  • mobile computing
  • edge computing
  • artificial intelligence
  • machine learning
  • 6G

Published Papers (1 paper)

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Research

19 pages, 4946 KiB  
Article
Dynamic Selection Slicing-Based Offloading Algorithm for In-Vehicle Tasks in Mobile Edge Computing
by Li Han, Yanru Bin, Shuaijie Zhu and Yanpei Liu
Electronics 2023, 12(12), 2708; https://doi.org/10.3390/electronics12122708 - 16 Jun 2023
Viewed by 880
Abstract
With the surge in tasks for in-vehicle terminals, the resulting network congestion and time delay cannot meet the service needs of users. Offloading algorithms are introduced to handle vehicular tasks, which will greatly improve the above problems. In this paper, the dependencies of [...] Read more.
With the surge in tasks for in-vehicle terminals, the resulting network congestion and time delay cannot meet the service needs of users. Offloading algorithms are introduced to handle vehicular tasks, which will greatly improve the above problems. In this paper, the dependencies of vehicular tasks are represented as directed acyclic graphs, and network slices are integrated within the edge server. The Dynamic Selection Slicing-based Offloading Algorithm for in-vehicle tasks in MEC (DSSO) is proposed. First, a computational offloading model for vehicular tasks is established based on available resources, wireless channel state, and vehicle loading level. Second, the solution of the model is transformed into a Markov decision process, and the combination of the DQN algorithm and Dueling Network from deep reinforcement learning is used to select the appropriate slices and dynamically update the optimal offloading strategy for in-vehicle tasks in the effective interval. Finally, an experimental environment is set up to compare the DSSO algorithm with LOCAL, MINCO, and DJROM, the results show that the system energy consumption of DSSO algorithm resources is reduced by 10.31%, the time latency is decreased by 22.75%, and the ratio of dropped tasks is decreased by 28.71%. Full article
(This article belongs to the Special Issue Deep Integration of Mobile/Edge Computing and AI)
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