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Artificial Intelligence—Internet of Things (AI-IoT)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 10 November 2024 | Viewed by 952

Special Issue Editors


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Guest Editor
Department of Computing Technologies, Swinburne University of Technology, John Street, Hawthorn, VIC 3112, Australia
Interests: industry 4.0;cloud computing; internet of Things; mobile computing

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Guest Editor
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, John Street, Hawthorn, VIC 3112, Australia
Interests: IoT; distributed Systems; edge/fog computing; industry 4.0; data analytics

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Guest Editor
Department of Computer Science, Electrical and Space Engineering (SRT), Luleå University of Technology, 971 87 Luleå, Sweden
Interests: quality of experience; context-aware computing; mobile and pervasive computing; cloud computing; mobile cloud computing

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Guest Editor
Computer Science Indian Institute of Technology Ropar, Rupnagar, India
Interests: real-time systems; scheduling theory; parallel and distributed computing

Special Issue Information

Dear Colleagues,

Artificial Intelligence–Internet of Things (AI-IoT) has evolved significantly with the advent of contemporary technologies, such as Artificial intelligence and Internet of Things. AI-IoT is transforming core industries, such as Industry 4.0, healthcare, smart cities, etc., at an exponential rate by providing real-time data, automation, and interconnectivity that allows for greater efficiencies and improved product quality. Artificial Intelligence (AI) is playing an increasingly important role in these industries, such as powering smarter devices, performing advanced analytics, and leading to more intuitive interfaces.

The increasing application of AI in IoT has changed the traditional way of operators operating in Industry 4.0, healthcare, smart cities, etc. The key benefits of AI to industries are as follows: (i) predictive maintenance, (ii) real-time monitoring, (iii) improved operational efficiency, (iv) smart manufacturing, and (v) improved and intelligent decision making.

Grand challenges imposed by AI-IoT, which need to be addressed, include data availability and the quality of available data, the complexity arising due to the integration of various technologies and systems, the sensitivity and vulnerability of data, and ethical concerns arising due to automated decision-making. This Special Issue solicits high-quality research papers, work-in-progress papers, surveys, and real-world application/deployment studies that address these challenges. Potential topics of interest for this Special Issue include (but are not limited to) the following:

Prof. Dr. Prem Prakash Jayaraman
Dr. Kaneez Fizza
Dr. Karan Mitra
Dr. Nitin Auluck
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. Sensors 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 2600 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

  • opportunities and challenges in AI-IoT
  • AI-IoT applications, use cases, and real-world deployments
  • data availability and quality for AI-IoT
  • security of AI-IoT systems against cyber attacks
  • responsible AI for IoT
  • privacy-preserved data analytics for AI-IoT

Published Papers (1 paper)

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Research

21 pages, 814 KiB  
Article
Multi-Objective Distributed Client Selection in Federated Learning-Assisted Internet of Vehicles
by Narisu Cha and Long Chang
Sensors 2024, 24(13), 4180; https://doi.org/10.3390/s24134180 - 27 Jun 2024
Viewed by 189
Abstract
Federated learning is an emerging distributed machine learning framework in the Internet of Vehicles (IoV). In IoV, millions of vehicles are willing to train the model to share their knowledge. Maintaining an active state means the participants must update their state to the [...] Read more.
Federated learning is an emerging distributed machine learning framework in the Internet of Vehicles (IoV). In IoV, millions of vehicles are willing to train the model to share their knowledge. Maintaining an active state means the participants must update their state to the FL server in a fixed interval and participate in the next round. However, the cost of maintaining an active state is very large when there are a huge number of participating vehicles. In this paper, we propose a distributed client selection scheme to reduce the cost of maintaining the active state for all participants. The clients with the highest evaluation are elected among the neighbors. In the evaluator, four variables are considered, including the sample quantity, available throughput, computational capability, and the quality of the local dataset. We adopt fuzzy logic as the evaluator since the closed-form solution over four variables does not exist. Extensive simulation results show that our proposal approximates the centralized client selection in terms of accuracy and can significantly reduce the communication overhead. Full article
(This article belongs to the Special Issue Artificial Intelligence—Internet of Things (AI-IoT))
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