Machine Learning in Internet of Unmanned Aerial Vehicles

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 54

Special Issue Editors

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Guest Editor
School of Information Science and Technology, Donghua University, Shanghai 201620, China
Interests: mobile computing; data privacy; internet of things; mobile edge computing; federated learning and its applications

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Guest Editor
School of Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
Interests: drones; robots; swarm drones; swarm robotics; IoT; smart sensors; mechatronics
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Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) with wide coverage and high mobility provide new opportunities for supporting IoT data collection and transmissions, which has the advantages of high agility, high flexibility, and low cost. The rise in popularity of Deep Neural Networks (DNNs) has spawned a research effort to deploy various kinds of DNN models on vehicles initially. DNN models can both accomplish complicated vehicular tasks and enable the construction of intelligent vehicular networks. However, the application of DNNs to UAVs still faces some challenges due to hardware and energy limitations.

Several studies have emerged to reduce the computational costs of training DNNs and pursue a desired trade-off between accuracy and latency. Data quantization is another significant technique that enables on-device light DNNs. According to distinct quantization targets, quantization methods can be roughly divided into two major categories: inference quantization and training quantization. Inference quantization aims to quantize weight and or activation to accelerate the forward pass, while training quantization needs to further quantize the gradient to accelerate the whole training process. This Special Issue invites original and breakthrough research in the field of machine learning for the Internet of UAVs.

Dr. Ping Zhao
Prof. Dr. Subhas Mukhopadhyay
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Computers is an international peer-reviewed open access monthly 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 1800 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.


  • unmanned aerial vehicles
  • machine learning
  • neural networks
  • deep learning
  • artificial intelligence
  • data quantization
  • inference quantization
  • training quantization

Published Papers

This special issue is now open for submission.
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