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Drones
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Advances in IoT and Wireless Networks of UAVs: State of...
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Advances in IoT and Wireless Networks of UAVs: State of the Art, Achievements and Perspectives

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 25 September 2025 | Viewed by 5917

Special Issue Editors

Prof. Dr. David Gesbert

E-Mail Website
Guest Editor
Communication Systems Department, EURECOM, Sophia Antipolis, France
Interests: UAVs; wireless communications; distributed learning; connected robotics
Dr. Rajeev Gangula

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA
Interests: connected robotics; UAV coomunications; B5G and 6G networks; Localization and sensing
Dr. Omid Esrafilian

E-Mail Website
Guest Editor
Communication Systems Department, EURECOM, Sophia Antipolis, France
Interests: UAVs; connected robotics; control; reinforcement learning
Dr. Satyam Agarwal

E-Mail Website
Guest Editor
Department of Electrical Engineering, IIT Ropar, Punjab 140001, India
Interests: B5G/6G networks and architecture; UAV networks

Special Issue Information

Dear Colleagues,

The role of unmanned aerial vehicles (UAVs) in next-generation wireless networks and the Internet of Things (IoT) has recently gained significant attention. Two complementary research directions have emerged: (a) the design of future wireless networks providing ubiquitous and reliable connectivity to a network of UAVs and (b) UAVs complementing the existing wireless network infrastructure. Networked UAVs have vast applications ranging from delivery, remote surveillance, rescue missions, and inspection. However, operational success depends on the ability to operate UAVs in beyond visual line of sight (BVLoS) conditions. Therefore, existing wireless networks need to be optimized, and to an extent re-designed, to accommodate these aerial users. In the latter research area, due to their inherent flexibility, UAV-mounted access points, base stations, or relays can augment the existing fixed wireless network infrastructure. Such networks offer the flexible deployment of radio resources when and where they are most needed. Use cases include disaster recovery scenarios, search-and-rescue operations, the servicing of temporary cultural/sporting events, on-demand hotspot coverage, and IoT data harvesting for smart cities, agriculture, etc.

This Special Issue aims to share the progress and efforts being made by researchers in UAV networks. Special emphasis is given to soliciting novel concepts and transformative design ideas that are emerging in this area which lie at the crossroads of wireless networking, robotic navigation, and sensing.

Authors are encouraged to submit original research papers and review articles on a wide range of topics including, but not limited to:

  • Cellular connected UAVs;
  • Connectivity for UAV corridors in aerial mobility;
  • UAV-enabled Open Radio Access Networks (O-RANs) ;
  • Localization and sensing with UAVs;
  • Prototypes, testbeds, and experimental results;
  • 3D radio mapping with UAVs;
  • UAV energy-efficient trajectory planning;
  • Machine learning for UAV-aided wireless networking ;
  • Data harvesting with UAVs in IoT networks;
  • UAV mesh networks.

Prof. Dr. David Gesbert
Dr. Rajeev Gangula
Dr. Omid Esrafilian
Dr. Satyam Agarwal
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. Drones 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 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

  • UAVs
  • aerial corridor connectivity
  • internet of drone things (IoDT)
  • O-RAN
  • UAV localization and sensing
  • UAV path planning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

20 pages, 2741 KiB  
Article
Intelligent Firefighting Technology for Drone Swarms with Multi-Sensor Integrated Path Planning: YOLOv8 Algorithm-Driven Fire Source Identification and Precision Deployment Strategy
by Bingxin Yu, Shengze Yu, Yuandi Zhao, Jin Wang, Ran Lai, Jisong Lv and Botao Zhou
Drones 2025, 9(5), 348; https://doi.org/10.3390/drones9050348 - 3 May 2025
Viewed by 407
Abstract
This study aims to improve the accuracy of fire source detection, the efficiency of path planning, and the precision of firefighting operations in drone swarms during fire emergencies. It proposes an intelligent firefighting technology for drone swarms based on multi-sensor integrated path planning. [...] Read more.
This study aims to improve the accuracy of fire source detection, the efficiency of path planning, and the precision of firefighting operations in drone swarms during fire emergencies. It proposes an intelligent firefighting technology for drone swarms based on multi-sensor integrated path planning. The technology integrates the You Only Look Once version 8 (YOLOv8) algorithm and its optimization strategies to enhance real-time fire source detection capabilities. Additionally, this study employs multi-sensor data fusion and swarm cooperative path-planning techniques to optimize the deployment of firefighting materials and flight paths, thereby improving firefighting efficiency and precision. First, a deformable convolution module is introduced into the backbone network of YOLOv8 to enable the detection network to flexibly adjust its receptive field when processing targets, thereby enhancing fire source detection accuracy. Second, an attention mechanism is incorporated into the neck portion of YOLOv8, which focuses on fire source feature regions, significantly reducing interference from background noise and further improving recognition accuracy in complex environments. Finally, a new High Intersection over Union (HIoU) loss function is proposed to address the challenge of computing localization and classification loss for targets. This function dynamically adjusts the weight of various loss components during training, achieving more precise fire source localization and classification. In terms of path planning, this study integrates data from visual sensors, infrared sensors, and LiDAR sensors and adopts the Information Acquisition Optimizer (IAO) and the Catch Fish Optimization Algorithm (CFOA) to plan paths and optimize coordinated flight for drone swarms. By dynamically adjusting path planning and deployment locations, the drone swarm can reach fire sources in the shortest possible time and carry out precise firefighting operations. Experimental results demonstrate that this study significantly improves fire source detection accuracy and firefighting efficiency by optimizing the YOLOv8 algorithm, path-planning algorithms, and cooperative flight strategies. The optimized YOLOv8 achieved a fire source detection accuracy of 94.6% for small fires, with a false detection rate reduced to 5.4%. The wind speed compensation strategy effectively mitigated the impact of wind on the accuracy of material deployment. This study not only enhances the firefighting efficiency of drone swarms but also enables rapid response in complex fire scenarios, offering broad application prospects, particularly for urban firefighting and forest fire disaster rescue. Full article
(This article belongs to the Special Issue Advances in IoT and Wireless Networks of UAVs: State of the Art, Achievements and Perspectives)
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22 pages, 4864 KiB  
Article
High-Altitude-UAV-Relayed Satellite D2D Communications for 6G IoT Network
by Jie Wang, Tao Hong, Fei Qi, Lei Liu and Xieyao He
Drones 2024, 8(10), 532; https://doi.org/10.3390/drones8100532 - 29 Sep 2024
Cited by 1 | Viewed by 2336
Abstract
High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language [...] Read more.
High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language Model (LLM) framework is introduced that dynamically predicts optimal HA-UAV connectivity for IoT devices, enhancing network performance and adaptability. The study emphasizes HA-UAVs’ operational efficiency, broad coverage, and potential to transform global communications, particularly in remote and underserved areas. Our proposed satellite-HA-UAV-IoT architecture with LLM optimization demonstrated substantial improvements, including a 25% increase in network throughput (from 20 Mbps to 25 Mbps at a 20 km distance), a 40% reduction in latency (from 25 ms to 15 ms), and a 28% enhancement in energy efficiency (from 0.25 μJ/bit to 0.18 μJ/bit), significantly advancing the performance and adaptability of next-generation IoT networks. These advancements pave the way for unprecedented connectivity and set the stage for future communication technologies. Full article
(This article belongs to the Special Issue Advances in IoT and Wireless Networks of UAVs: State of the Art, Achievements and Perspectives)
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26 pages, 1906 KiB  
Article
Federated Reinforcement Learning for Collaborative Intelligence in UAV-Assisted C-V2X Communications
by Abhishek Gupta and Xavier Fernando
Drones 2024, 8(7), 321; https://doi.org/10.3390/drones8070321 - 12 Jul 2024
Cited by 4 | Viewed by 2274
Abstract
This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial vehicle (UAV). Different sensors in vehicles capture different types of data, [...] Read more.
This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial vehicle (UAV). Different sensors in vehicles capture different types of data, contributing to data heterogeneity. C-V2X communication networks impose additional communication overhead in order to converge to a global model when the sensor data are not independent-and-identically-distributed (non-i.i.d.). Consequently, the training time for local model updates also varies considerably. Using FRL, we accelerated this convergence by minimizing communication rounds, and we delayed it by exploring the correlation between the data captured by various vehicles in subsequent time steps. Additionally, as UAVs have limited battery power, processing of the collected information locally at the vehicles and then transmitting the model hyper-parameters to the UAVs can optimize the available power consumption pattern. The proposed FRL algorithm updates the global model through adaptive weighing of Q-values at each training round. By measuring the local gradients at the vehicle and the global gradient at the UAV, the contribution of the local models is determined. We quantify these Q-values using nonlinear mappings to reinforce positive rewards such that the contribution of local models is dynamically measured. Moreover, minimizing the number of communication rounds between the UAVs and vehicles is investigated as a viable approach for minimizing delay. A performance evaluation revealed that the FRL approach can yield up to a 40% reduction in the number of communication rounds between vehicles and UAVs when compared to gross data offloading. Full article
(This article belongs to the Special Issue Advances in IoT and Wireless Networks of UAVs: State of the Art, Achievements and Perspectives)
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