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Edge–Cloud Computing for the Internet of Things: Embedding Intelligence in...
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Edge–Cloud Computing for the Internet of Things: Embedding Intelligence in the Edge with Artificial Intelligence

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 2587

Special Issue Editors

Dr. Chao Qiu

E-Mail Website
Guest Editor
College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
Interests: edge computing; blockchain; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christian Becker

E-Mail Website
Guest Editor
Distributed Systems, Institute for Parallel and Distributed Systems, Universität Stuttgart, Universitätsstraße 38, 70569 Stuttgart, German
Interests: ubiquitous computing; edge computing; self-organizing systems; adaptation; Internet of Things; distributed systems

Special Issue Information

Dear Colleagues,

Recently, the term ‘Internet of Things’ (IoT) has elicited escalating attention as an emerging paradigm to satisfy the requirements of agility, flexibility, and ubiquitous accessibility. One of the radical shifts that the IoT has introduced is the ability to sense data from nearby surroundings and carry out self-executing functions. More specifically, its agility, flexibility, and ubiquitous accessibility have encouraged cooperation between artificial learning, edge–cloud computing, and the IoT, referred to as edge intelligence. This pushes learning intelligence from a remote learning center to the network edges. Essentially, physical proximity between data generation sources, users, and learning agents promises a number of high QoS supplements, such as mild bandwidth, energy efficiency, time effectiveness, privacy protection, and on-premises activity. Although edge intelligence has been widely researched to solve the present problems, numerous handicaps prevent it from being used as a generic platform.

This Special Issue invites research on novel functionalities and technologies (including protocols) in edge–cloud computing, IoT, edge intelligence, and artificial intelligence, with respect to several perspectives, including amalgamated algorithms, allocation schemes, incentive modeling and optimization, etc. The issue will provide tutorial information, disseminate recent results, review economic opportunities, examine technical challenges, discuss possible paths to regulatory solutions, and identify future trends.

Dr. Chao Qiu
Prof. Dr. Christian Becker
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

  • theoretical modeling and performance analysis for edge computing in the IoT
  • edge intelligence using crowd-funded computing and networking resource
  • adaptability edge computing services for users’ AI insight
  • effect of blockchain in edge intelligence and how it helps the IoT
  • security, trust, privacy, and identity in edge intelligence helped by the IoT

Published Papers (1 paper)

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Research

19 pages, 4373 KiB  
Article
Dynamic Offloading Method for Mobile Edge Computing of Internet of Vehicles Based on Multi-Vehicle Users and Multi-MEC Servers
by Xiaochao Dang, Lin Su, Zhanjun Hao and Xu Shang
Electronics 2022, 11(15), 2326; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11152326 - 26 Jul 2022
Cited by 4 | Viewed by 1932
Abstract
With the continuous development of intelligent transportation system technology, vehicle users have higher and higher requirements for low latency and high service quality of task computing. The computing offloading technology of mobile edge computing (MEC) has received extensive attention in the Internet of [...] Read more.
With the continuous development of intelligent transportation system technology, vehicle users have higher and higher requirements for low latency and high service quality of task computing. The computing offloading technology of mobile edge computing (MEC) has received extensive attention in the Internet of Vehicles (IoV) architecture. However, due to the limited resources of the MEC server, it cannot meet the task requests from multiple vehicle users simultaneously. For this reason, making correct and fast offloading decisions to provide users with a service with low latency, low energy consumption, and low cost is still a considerable challenge. Regarding the issue above, in the IoV environment where vehicle users race, this paper designs a three-layer system task offloading overhead model based on the Edge-Cloud collaboration of multiple vehicle users and multiple MEC servers. To solve the problem of minimizing the total cost of the system performing tasks, an Edge-Cloud collaborative, dynamic computation offloading method (ECDDPG) based on a deep deterministic policy gradient is designed. This method is deployed at the edge service layer to make fast offloading decisions for tasks generated by vehicle users. The simulation results show that the performance is better than the Deep Q-network (DQN) method and the Actor-Critic method regarding reward value and convergence. In the face of the change in wireless channel bandwidth and the number of vehicle users, compared with the basic method strategy, the proposed method has better performance in reducing the total computational cost, computing delay, and energy consumption. At the same time, the computational complexity of the system execution tasks is significantly reduced. Full article
(This article belongs to the Special Issue Edge–Cloud Computing for the Internet of Things: Embedding Intelligence in the Edge with Artificial Intelligence)
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