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Internet of Things for Smart Infrastructure System

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 3033

Special Issue Editor

School of Information and Communication Engineering, Beijing University of Posts and Telecommunication,100876 Beijing, China
Interests: IoT; future network; TSN; edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) is an emerging technology, which interconnects smart devices and services, and exchanges and processes data, in order to dynamically adapt to the surrounding environment. Smart infrastructure (such as smart home, smart building, smart urban water affairs management), enabled by technologies such as IoT, offer numerous advantages in the provision of more energy-saving, efficient and sustainable systems. However, IoT-enabled technology in a smart infrastructure system faces many challenges when considering the low complexity and cost requirement for the devices and network, as well as the various QoS requirements of different services.

The aim of this Special Issue is to present novel approaches to providing solutions in related fields, such as the inter-operable and internet-based IoT infrastructure, supporting the ability of heterogeneous devices to access, and future network technologies to provide, connectivity with various sensor devices and services.

Dr. Fangmin Xu
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. Applied Sciences 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

  • internet of things
  • smart infrastructure
  • network architecture
  • resource management
  • security
  • QoS

Published Papers (1 paper)

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Research

12 pages, 450 KiB  
Article
A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
by Xiaowei Liu, Shuwen Jiang and Yi Wu
Appl. Sci. 2022, 12(21), 11260; https://doi.org/10.3390/app122111260 - 6 Nov 2022
Cited by 6 | Viewed by 2543
Abstract
With the internet developing rapidly, mobile edge computing (MEC) has been proposed to offer computational capabilities to tackle the high latency caused by innumerable data and applications. Due to limited computing resources, the innovation of computation offloading technology for an MEC system remains [...] Read more.
With the internet developing rapidly, mobile edge computing (MEC) has been proposed to offer computational capabilities to tackle the high latency caused by innumerable data and applications. Due to limited computing resources, the innovation of computation offloading technology for an MEC system remains challenging, and can lead to transmission delays and energy consumption. This paper focuses on a task-offloading scheme for an MEC-based system where each mobile device is an independent agent and responsible for making a schedule based on delay-sensitive tasks. Nevertheless, the time-varying network dynamics and the heterogeneous features of real-time data tasks make it difficult to find an optimal solution for task offloading. Existing centralized-based or distributed-based algorithms require huge computational resources for complex problems. To address the above problem, we design a novel deep reinforcement learning (DRL)-based approach by using a parameterized indexed value function for value estimation. Additionally, the task-offloading problem is simulated as a Markov decision process (MDP) and our aim is to reduce the total delay of data processing. Experimental results have shown that our algorithm significantly promotes the users’ offloading performance over traditional methods. Full article
(This article belongs to the Special Issue Internet of Things for Smart Infrastructure System)
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