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Drones
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UAV-Assisted Intelligent Vehicular Networks 2nd Edition
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UAV-Assisted Intelligent Vehicular Networks 2nd Edition

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 9656

Special Issue Editors

Dr. Dawei Wang

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
Interests: physical-layer security; cognitive radio networks; marine communications; machine learning; resource allocation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ruonan Zhang

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
Interests: wireless channel measurement and modeling; architecture and protocol design of wireless networks; satellite communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The success of the MDPI Drones Special Issue “UAV-Assisted Intelligent Vehicular Networks” led us to propose this second edition, for which we are pleased to invite you to submit original contributions.

We are on the cusp of a new era of intelligent transportation. As a key enabler for intelligent transportation systems (ITSs), vehicular networks encompass a broad range of information technologies, including vehicle-to-everything (V2X), mobile edge computing (MEC), cloud computing, and blockchain. Although vehicular networks offer an improved performance with advanced services, the explosive growth of communication devices and the rising demand for many emerging services will bring new communication challenges to vehicular networks. It is anticipated that the communication systems integrated with unmanned aerial vehicles (UAVs) will satisfy these requirements in next-generation vehicular networks. Due to their high flexible mobility, UAV-assisted vehicular networks will bring far-reaching and transformative benefits, with significantly enhanced reliability and security, extremely high data rates, massive and hyper-fast wireless access, and much smarter, longer, and greener three-dimensional (3D) communications coverage.

This Special Issue will focus on (but not be limited to) the following topics:

  • Protocol design and analysis for UAV-assisted V2X;
  • Resource management and mobility management;
  • Energy harvesting and management for UAV-assisted V2X;
  • Non-orthogonal multiple access (NOMA)-enhanced UAV-assisted vehicular networks;
  • UAV-assisted vehicular network applications and services;
  • V2X communications in 5G and beyond;
  • UAV-assisted vehicular networks based on artificial intelligence (AI);
  • Sensors for vehicular technologies;
  • Terminal intelligence;
  • Security- and privacy-preserving schemes for UAV-assisted vehicular networks;
  • Channel measurement and modeling for UAV-assisted vehicular networks

Dr. Dawei Wang
Prof. Dr. Ruonan Zhang
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

  • intelligent transportation systems (ITSs)
  • unmanned aerial vehicles (UAVs)
  • vehicle-to-everything (V2X)
  • vehicular networks
  • mobile edge computing (MEC)

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

Related Special Issue

Published Papers (5 papers)

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Research

22 pages, 1366 KiB  
Article
Mobility-Aware Task Offloading and Resource Allocation in UAV-Assisted Vehicular Edge Computing Networks
by Long Chen, Jiaqi Du and Xia Zhu
Drones 2024, 8(11), 696; https://doi.org/10.3390/drones8110696 - 20 Nov 2024
Cited by 2 | Viewed by 1806
Abstract
The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing [...] Read more.
The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing (VEC) systems, which considers the high mobility of vehicles and the limited coverage and computational capacities of drones. We introduce the Mobility-Aware Vehicular Task Offloading (MAVTO) algorithm, designed to optimize task offloading decisions, manage resource allocation, and predict vehicle positions for seamless offloading. MAVTO leverages container-based virtualization for efficient computation, offering flexibility in resource allocation in multiple offload modes: direct, predictive, and hybrid. Extensive experiments using real-world vehicular data demonstrate that the MAVTO algorithm significantly outperforms other methods in terms of task completion success rate, especially under varying task data volumes and deadlines. Full article
(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks 2nd Edition)
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27 pages, 21647 KiB  
Article
Multiple UAVs Networking Oriented Consistent Cooperation Method Based on Adaptive Arithmetic Sine Cosine Optimization
by He Huang, Dongqiang Li, Mingbo Niu, Feiyu Xie, Md Sipon Miah, Tao Gao and Huifeng Wang
Drones 2024, 8(7), 340; https://doi.org/10.3390/drones8070340 - 22 Jul 2024
Cited by 1 | Viewed by 1081
Abstract
With the rapid development of the Internet of Things, the Internet of Vehicles (IoV) has quickly drawn considerable attention from the public. The cooperative unmanned aerial vehicles (UAVs)-assisted vehicular networks, as a part of IoV, has become an emerging research spot. Due to [...] Read more.
With the rapid development of the Internet of Things, the Internet of Vehicles (IoV) has quickly drawn considerable attention from the public. The cooperative unmanned aerial vehicles (UAVs)-assisted vehicular networks, as a part of IoV, has become an emerging research spot. Due to the significant limitations of the application and service of a single UAV-assisted vehicular networks, efforts have been put into studying the use of multiple UAVs to assist effective vehicular networks. However, simply increasing the number of UAVs can lead to difficulties in information exchange and collisions caused by external interference, thereby affecting the security of the entire cooperation and networking. To address the above problems, multiple UAV cooperative formation is increasingly receiving attention. UAV cooperative formation can not only save energy loss but also achieve synchronous cooperative motion through information communication between UAVs, prevent collisions and other problems between UAVs, and improve task execution efficiency. A multi-UAVs cooperation method based on arithmetic optimization is proposed in this work. Firstly, a complete mechanical model of unmanned maneuvering was obtained by combining acceleration limitations. Secondly, based on the arithmetic sine and cosine optimization algorithm, the mathematical optimizer was used to accelerate the function transfer. Sine and cosine strategies were introduced to achieve a global search and enhance local optimization capabilities. Finally, in obtaining the precise position and direction of multi-UAVs to assist networking, the cooperation method was formed by designing the reference controller through the consistency algorithm. Experimental studies were carried out for the multi-UAVs’ cooperation with the particle model, combined with the quadratic programming problem-solving technique. The results show that the proposed quadrotor dynamic model provides basic data for cooperation position adjusting, and our simplification in the model can reduce the amount of calculations for the feedback and the parameter changes during the cooperation. Moreover, combined with a reference controller, the UAVs achieve the predetermined cooperation by offering improved navigation speed, task execution efficiency, and cooperation accuracy. Our proposed multi-UAVs cooperation method can improve the quality of service significantly on the UAV-assisted vehicular networks. Full article
(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks 2nd Edition)
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21 pages, 432 KiB  
Article
Distance-Enhanced Hybrid Hierarchical Modulation and QAM Modulation Schemes for UAV Terahertz Communications
by Zhenzhen Hu, Yong Xu, Yonghong Deng and Zhongpei Zhang
Drones 2024, 8(7), 300; https://doi.org/10.3390/drones8070300 - 6 Jul 2024
Cited by 2 | Viewed by 1128
Abstract
Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations require substantial data and video transmission, demanding significant spectral resources. The ultra-broad bandwidth of 0.1–10 THz in the Terahertz (THz) frequency range is essential for future UAV-based [...] Read more.
Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations require substantial data and video transmission, demanding significant spectral resources. The ultra-broad bandwidth of 0.1–10 THz in the Terahertz (THz) frequency range is essential for future UAV-based wireless communications. However, the available bandwidth in the THz frequency spectrum varies with transmission distance. To enhance spectral efficiency over this variable bandwidth, we propose using hierarchical modulation (HM) in the overlapped spectrum and traditional quadrature amplitude modulation (QAM) in the non-overlapped spectrum for closer users. Furthermore, we analyze the single-user case and utilize the block-coordinated descent (BCD) method to jointly optimize the modulation order, subcarrier bandwidth, and sub-band power to improve the system sum rate. Finally, considering the mobility and randomness of UAV users, we design a modulation switching rule to dynamically adjust to changes in distance as users move, thereby enhancing data rates. Simulation results demonstrate superior performance in data rate and design complexity compared to existing methods such as hierarchical bandwidth modulation (HBM) and HM schemes. Full article
(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks 2nd Edition)
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23 pages, 38781 KiB  
Article
Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network
by Haoran Li, Xiaoyao Hao, Juan Wen, Fangyuan Liu and Yiling Zhang
Drones 2024, 8(6), 262; https://doi.org/10.3390/drones8060262 - 13 Jun 2024
Cited by 1 | Viewed by 1713
Abstract
In the event of a sudden natural disaster, the damaged communication infrastructure cannot provide a necessary network service for vehicles. Unfortunately, this is the critical moment when the occupants of trapped vehicles need to urgently use the vehicular network’s emergency service. How to [...] Read more.
In the event of a sudden natural disaster, the damaged communication infrastructure cannot provide a necessary network service for vehicles. Unfortunately, this is the critical moment when the occupants of trapped vehicles need to urgently use the vehicular network’s emergency service. How to efficiently connect the trapped vehicle to the base station is the challenge facing the emergency vehicular network. To address this challenge, this study proposes a UAV-assisted multi-objective and multi-hop ad hoc network (UMMVN) that can be used as an emergency vehicular network. Firstly, it presents an integrated design of a search system to find a trapped vehicle, the communication relay, and the networking, which significantly decreases the UAV’s networking time cost. Secondly, it presents a multi-objective search for a trapped vehicle and navigates UAVs along multiple paths to different objectives. Thirdly, it presents an optimal branching node strategy that allows the adequate use of the overlapping paths to multiple targets, which decreases the networking cost within the limited communication and searching range. The numerical experiments illustrate that the UMMVN performs better than other state-of-the-art networking methods. Full article
(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks 2nd Edition)
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15 pages, 2066 KiB  
Article
Post-Disaster Emergency Communications Enhanced by Drones and Non-Orthogonal Multiple Access: Three-Dimensional Deployment Optimization and Spectrum Allocation
by Linyang Li, Lijun Zhu, Fanghui Huang, Dawei Wang, Xin Li, Tong Wu and Yixin He
Drones 2024, 8(2), 63; https://doi.org/10.3390/drones8020063 - 13 Feb 2024
Cited by 1 | Viewed by 2970
Abstract
Integrating the relaying drone and non-orthogonal multiple access (NOMA) technique into post-disaster emergency communications (PDEComs) is a promising way to accomplish efficient network recovery. Motivated by the above, by optimizing the drone three-dimensional (3D) deployment optimization and spectrum allocation, this paper investigates a [...] Read more.
Integrating the relaying drone and non-orthogonal multiple access (NOMA) technique into post-disaster emergency communications (PDEComs) is a promising way to accomplish efficient network recovery. Motivated by the above, by optimizing the drone three-dimensional (3D) deployment optimization and spectrum allocation, this paper investigates a quality of service (QoS)-driven sum rate maximization problem for drone-and-NOMA-enhanced PDEComs that aims to improve the data rate of cell edge users (CEUs). Due to the non-deterministic polynomial (NP)-hard characteristics, we first decouple the formulated problem. Next, we obtain the optimal 3D deployment with the aid of a long short-term memory (LSTM)-based recurrent neural network (RNN). Then, we transform the spectrum allocation problem into an optimal matching issue, based on which the Hungarian algorithm is employed to solve it. Finally, the simulation results show that the presented scheme has a significant performance improvement in the sum rate compared with the state-of-the-art works and benchmark scheme. For instance, by adopting the NOMA technique, the sum rate can be increased by 9.72% and the needs of CEUs can be satisfied by enabling the relaying drone. Additionally, the convergence, complexity, and performance gap caused by iterative optimization are discussed and analyzed. Full article
(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks 2nd Edition)
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