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 January 2025 | Viewed by 3470

Special Issue Editors


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Guest Editor
Communication Systems Department, EURECOM, Sophia Antipolis, France
Interests: UAVs; wireless communications; distributed learning; connected robotics

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Guest Editor
Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA
Interests: connected robotics; UAV coomunications; B5G and 6G networks; Localization and sensing

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Guest Editor
Communication Systems Department, EURECOM, Sophia Antipolis, France
Interests: UAVs; connected robotics; control; reinforcement learning

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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

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Keywords

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

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Published Papers (2 papers)

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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
Viewed by 1278
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
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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 2 | Viewed by 1540
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
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