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Drones
Special Issues
Recent Advances in UAVs for Wireless Networks
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Recent Advances in UAVs for Wireless Networks

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 9081

Special Issue Editors

Dr. Shiva Raj Pokhrel

E-Mail Website
Guest Editor
School of Info Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Melbourne Burwood Campus, Burwood, VIC, 3217, Australia
Interests: autonomous vehicles; federated learning; blockchain modelling; optimization; recommender systems; cloud computing; dynamics control; Internet of Things; cyber-physical systems; manufacturing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hai L. Vu

E-Mail Website
Guest Editor
Monash Institute of Transport Studies, Faculty of Engineering, Monash University, Clayton, VIC, Australia
Interests: intelligent transport systems; Dedicated Short Range Communication (DSRC) Systems; data-intensive systems; data mining and visualization
Prof. Dr. Jinho Choi

E-Mail Website
Guest Editor
School of Info Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Melbourne Burwood Campus, Burwood, VIC, Australia
Interests: Internet of Things; wireless communications; statistical signal processing

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) such as drones in 6G are envisaged to be capable of understanding the semantics of spatial and temporal environmental changes, adjusting their trajectories, intelligently collecting local data, orchestrating their platoons and immediately responding to unforeseen events while interactively collaborating with other drones, machines, and humans. Traditional information theory establishes a basic theoretical limit on the speed of reliable communications where every bit is treated identically, and the system is unaware of the meaning or application of the information. While this limit has successfully served content delivery networks for decades, many emerging applications with UAVs, drones, ranging from tactile internet and autonomous vehicles to disaster response and haptic applications, involve interactions between machines and humans where information content would play a role in the design and performance of the communication channel on the move.

This Special Issue aims to collect cutting-edge leapfrog submissions of distributed optimization, decentralized computing, and distributed learning (e.g., federated learning) over future drones' or UAVs networks from academia and industry, thereby advancing the development of fundamental theories for communication-efficient spatial learning and the application of blockchain and machine learning algorithms at scales relevant for semantic communication.

Blockchain, machine learning and data-driven space networking of UAVs/drones have recently been highly regarded as important facilitators for the 6G networks. Most existing space learning systems are centralized with data streamed from devices, drones, and satellites. Nevertheless, such a centralized approach may lead to privacy issues, breach applications' latency restrictions, or become ineffective due to high cost, bandwidth, or border constraints (such as energy, battery life and data rate). This summons a real momentum towards semantic communication-based distributed learning in the space, which is anticipated to be an exciting approach for solving impending issues by providing UAVs/drones that collaboratively train a context-aware learning model using data generated and shared in real-time.

Potential topics may include, but are not limited to, the following: 

  • Drones IoT applications, Principles and Challenges for Semantic Communication: such as the autonomous shipping, platooning, autonomous vehicles, smart ocean, tactile internet, and disaster response systems.
  • Novel distributed optimization, distributed ledger technology (DLT) theories and machine learning techniques for drones IoT, edge computing, platooning and age of information.
  • Transport, network, and physical layer diversity protocols for semantic communication of the UAVs/drone's system
  • Techniques reducing the number/frequency of drones' communications in distributed learning and streaming applications (e.g., applying transfer learning, zero/one-shot learning, etc.)
  • Quantization, sparsification, and network coding methods for context-aware communication via distributed learning of UAVs/drones
  • Novel methods for distributed learning and context-aware model training over drones with limited communication resources such as energy and bandwidth
  • Impact of drones' network dynamic topology (e.g., time-varying graph) on efficient distributed learning and for semantic communication
  • Distributed learning with practical semantic communication conditions, such as wireless interference, noisy/time-varying/fading channels, and multiple access
  • Fundamental performance limits for distributed learning and semantic communication over UAVs/drones with limited communication resources
  • Joint communication, computing, and sensing for DLT decentralized learning over future context aware swarm networks.
  • New architectures for automation and orchestration of platooning, decentralized learning and semantic communication over UAVs/drones
  • DLT-based Privacy and security issues of distributed learning for the drones IoT
  • Building UAVs testbed and drones simulators for DLT for distributed learning and communication over the space
  • Adaptive DLT design and dynamic semantic optimization of IoT space networks for improving the performance of remote federated/transfer learning
  • Revolutionary drones IoT network protocol designs for remote collaborative federated/transfer learning and semantic communication
  • Decentralized learning for intelligent drones' data processing, signal processing, space signal detection and estimation.

Dr. Shiva Raj Pokhrel
Prof. Dr. Hai L. Vu
Prof. Dr. Jinho Choi
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

  • context-awareness
  • distributed learning
  • federated learning
  • quantization, sparsification, and network coding methods
  • transfer learning
  • semantic communication
  • context aware swarm networks
  • transport, network, and physical layer diversity protocols
  • intelligent drones' data processing, signal processing, space signal detection and estimation
  • UAVs testbed and drones simulators
  • automation and orchestration of platooning, decentralized learning and semantic communication
  • performance limits for distributed learning and semantic communication over UAVs/drones
  • distributed learning with practical semantic communication conditions

Published Papers (3 papers)

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Research

15 pages, 2458 KiB  
Article
Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method
by Yuping Lu, Ge Xiong, Xiang Zhang, Zhifei Zhang, Tingyu Jia and Ke Xiong
Drones 2022, 6(12), 378; https://doi.org/10.3390/drones6120378 - 25 Nov 2022
Cited by 1 | Viewed by 1472
Abstract
This paper focuses on the unmanned aerial vehicles (UAVs)-aided mobile networks, where multiple ground mobile users (GMUs) desire to upload data to a UAV. In order to maximize the total amount of data that can be uploaded, we formulate an optimization problem to [...] Read more.
This paper focuses on the unmanned aerial vehicles (UAVs)-aided mobile networks, where multiple ground mobile users (GMUs) desire to upload data to a UAV. In order to maximize the total amount of data that can be uploaded, we formulate an optimization problem to maximize the uplink throughput by optimizing the UAV’s trajectory, under the constraints of the available energy of the UAV and the quality of service (QoS) of GMUs. To solve the non-convex problem, we propose a deep Q-network (DQN)-based method, in which we employ the iterative updating process and the Experience Relay (ER) method to reduce the negative effects sequence correlation on the training results, and the ε-greedy method is applied to balance the exploration and exploitation, for achieving the better estimations of the environment and also taking better actions. Different from previous works, the mobility of the GMUs is taken into account in this work, which is more general and closer to practice. Simulation results show that the proposed DQN-based method outperforms a traditional Q-Learning-based one in terms of both convergence and network throughput. Moreover, the larger battery capacity the UAV has, the higher uplink throughput can be achieved. Full article
(This article belongs to the Special Issue Recent Advances in UAVs for Wireless Networks)
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34 pages, 1752 KiB  
Article
Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
by Mahyar Nemati, Bassel Al Homssi, Sivaram Krishnan, Jihong Park, Seng W. Loke and Jinho Choi
Drones 2022, 6(11), 334; https://doi.org/10.3390/drones6110334 - 31 Oct 2022
Cited by 6 | Viewed by 4753
Abstract
Non-terrestrial networks (NTNs) have recently attracted elevated levels of interest in large-scale and ever-growing wireless communication networks through the utilization of flying objects, e.g., satellites and unmanned aerial vehicles/drones (UAVs). Interestingly, the applications of UAV-assisted networks are rapidly becoming an integral part of [...] Read more.
Non-terrestrial networks (NTNs) have recently attracted elevated levels of interest in large-scale and ever-growing wireless communication networks through the utilization of flying objects, e.g., satellites and unmanned aerial vehicles/drones (UAVs). Interestingly, the applications of UAV-assisted networks are rapidly becoming an integral part of future communication services. This paper first overviews the key components of NTN while highlighting the significance of emerging UAV networks where for example, a group of UAVs can be used as nodes to exchange data packets and form a flying ad hoc network (FANET). In addition, both existing and emerging applications of the FANET are explored. Next, it provides key recent findings and the state-of-the-art of FANETs while examining various routing protocols based on cross-layer modeling. Moreover, a modeling perspective of FANETs is provided considering delay-tolerant networks (DTN) because of the intermittent nature of connectivity in low-density FANETs, where each node (or UAV) can perform store-carry-and-forward (SCF) operations. Indeed, we provide a case study of a UAV network as a DTN, referred to as DTN-assisted FANET. Furthermore, applications of machine learning (ML) in FANET are discussed. This paper ultimately foresees future research paths and problems for allowing FANET in forthcoming wireless communication networks. Full article
(This article belongs to the Special Issue Recent Advances in UAVs for Wireless Networks)
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16 pages, 3806 KiB  
Article
Dwarf Mongoose Optimization-Based Secure Clustering with Routing Technique in Internet of Drones
by Fatma S. Alrayes, Jaber S. Alzahrani, Khalid A. Alissa, Abdullah Alharbi, Hussain Alshahrani, Mohamed Ahmed Elfaki, Ayman Yafoz, Abdullah Mohamed and Anwer Mustafa Hilal
Drones 2022, 6(9), 247; https://doi.org/10.3390/drones6090247 - 09 Sep 2022
Cited by 8 | Viewed by 1821
Abstract
Over the last few years, unmanned aerial vehicles (UAV), also called drones, have attracted considerable interest in the academic field and exploration in the research field of wireless sensor networks (WSN). Furthermore, the application of drones aided operations related to the agriculture industry, [...] Read more.
Over the last few years, unmanned aerial vehicles (UAV), also called drones, have attracted considerable interest in the academic field and exploration in the research field of wireless sensor networks (WSN). Furthermore, the application of drones aided operations related to the agriculture industry, smart Internet of things (IoT), and military support. Now, the usage of drone-based IoT, also called Internet of drones (IoD), and their techniques and design challenges are being investigated by researchers globally. Clustering and routing aid to maximize the throughput, reducing routing, and overhead, and making the network more scalable. Since the cluster network used in a UAV adopts an open transmission method, it exposes a large surface to adversaries that pose considerable network security problems to drone technology. This study develops a new dwarf mongoose optimization-based secure clustering with a multi-hop routing scheme (DMOSC-MHRS) in the IoD environment. The goal of the DMOSC-MHRS technique involves the selection of cluster heads (CH) and optimal routes to a destination. In the presented DMOSC-MHRS technique, a new DMOSC technique is utilized to choose CHs and create clusters. A fitness function involving trust as a major factor is included to accomplish security. Besides, the DMOSC-MHRS technique designs a wild horse optimization-based multi-hop routing (WHOMHR) scheme for the optimal route selection process. To demonstrate the enhanced performance of the DMOSC-MHRS model, a comprehensive experimental assessment is made. An extensive comparison study demonstrates the better performance of the DMOSC-MHRS model over other approaches. Full article
(This article belongs to the Special Issue Recent Advances in UAVs for Wireless Networks)
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