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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 28047

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Dr. Dawei Wang

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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,

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 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; as well as much smarter, longer, and greener three-dimensional (3D) communications coverage.

This Special Issue will focus on (but not 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

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Keywords

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

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Related Special Issue

UAV-Assisted Intelligent Vehicular Networks 2nd Edition in Drones (5 articles)

Published Papers (12 papers)

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Research

17 pages, 3561 KiB

Open AccessArticle

Intelligent Resource Allocation Using an Artificial Ecosystem Optimizer with Deep Learning on UAV Networks

by Ahsan Rafiq, Reem Alkanhel, Mohammed Saleh Ali Muthanna, Evgeny Mokrov, Ahmed Aziz and Ammar Muthanna

Drones 2023, 7(10), 619; https://doi.org/10.3390/drones7100619 - 3 Oct 2023

Cited by 2 | Viewed by 1872

Abstract

An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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15 pages, 622 KiB

Open AccessArticle

Hierarchical Matching Algorithm for Relay Selection in MEC-Aided Ultra-Dense UAV Networks

by Wei Liang, Shaobo Ma, Siyuan Yang, Boxuan Zhang and Ang Gao

Drones 2023, 7(9), 579; https://doi.org/10.3390/drones7090579 - 14 Sep 2023

Cited by 4 | Viewed by 1247

Abstract

With the rapid development of communication technology, unmanned aerial vehicle–mobile edge computing (UAV-MEC) networks have emerged with powerful capabilities. However, existing research studies have neglected the issues involving user grouping and relay selection structures under UAV cluster-assisted communication. Therefore, in this article, we present a comprehensive communication–computing resource allocation for UAV-MEC networks. In particular, ground users make stable user groups first, and then multiple UAVs act as relays in order to assist these user groups in simultaneously uploading their tasks to the terrestrial base station at the edge server. Moreover, in order to maximize the system’s overall throughput, a more flexible and hierarchical matching relay selection algorithm is proposed in terms of matching the ground user groups and corresponding UAVs. For vulnerable users, we also propose a weighted relay selection algorithm to maximize the system performance. Furthermore, simulation results show that the proposed relay selection algorithm achieves a significant gain in comparison with the other benchmarks, and the stability of the proposed algorithms could be verified. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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16 pages, 573 KiB

Open AccessArticle

LAP and IRS Enhanced Secure Transmissions for 6G-Oriented Vehicular IoT Services

by Lingtong Min, Jiawei Li, Yixin He and Qin Si

Drones 2023, 7(7), 414; https://doi.org/10.3390/drones7070414 - 22 Jun 2023

Cited by 1 | Viewed by 1263

Abstract

In 6G-oriented vehicular Internet of things (IoT) services, the integration of a low altitude platform (LAP) and intelligent reflecting surfaces (IRS) provides a promising solution to achieve seamless coverage and massive connections at low cost. However, due to the open nature of wireless channels, how to protect the transmission of privacy information in LAP-based IRS symbiotic vehicular networks remains a challenge. Motivated by the above, this paper investigates the LAP and IRS enhanced secure transmission problem in the presence of an eavesdropper. Specifically, we first deploy a fixed LAP equipped with IRS to overcome the blockages and introduce artificial noise against the eavesdropper. Next, we formulate a total secure channel capacity maximization problem by optimizing the phase shift, power distribution coefficient, and channel allocation. To effectively solve the formulated problem, we design an iterative algorithm with polynomial complexity, where the optimization variables are solved in turn. In addition, the complexity and convergence of the proposed iterative algorithm are analyzed theoretically. Finally, numerical results show that our proposed secure transmission scheme outperforms the comparison schemes in terms of the total secure channel capacity. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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16 pages, 2339 KiB

Open AccessArticle

A Multi-Subsampling Self-Attention Network for Unmanned Aerial Vehicle-to-Ground Automatic Modulation Recognition System

by Yongjian Shen, Hao Yuan, Pengyu Zhang, Yuheng Li, Minkang Cai and Jingwen Li

Drones 2023, 7(6), 376; https://doi.org/10.3390/drones7060376 - 4 Jun 2023

Cited by 4 | Viewed by 1779

Abstract

In this paper, we investigate the deep learning applications of radio automatic modulation recognition (AMR) applications in unmanned aerial vehicle (UAV)-to-ground AMR systems. The integration of deep learning in a UAV-aided signal processing terminal can recognize the modulation mode without the provision of parameters. However, the layers used in current models have a small data processing range, and their low noise resistance is another disadvantage. Most importantly, large numbers of parameters and high amounts of computation will burden terminals in the system. We propose a multi-subsampling self-attention (MSSA) network for UAV-to-ground AMR systems, for which we devise a residual dilated module containing ordinary and dilated convolution to expand the data processing range, followed by a self-attention module to improve the classification, even in the presence of noise interference. We subsample the signals to reduce the number of parameters and amount of calculation. We also propose three model sizes, namely large, medium, and small, and the smaller the model, the more suitable it will be for UAV-to-ground AMR systems. We conduct ablation experiments with state-of-the-art and baseline models on the common AMR and radio machine learning (RML) 2018.01a datasets. The proposed method achieves the highest accuracy of

97.00 %

at a 30 dB signal-to-noise ratio (SNR). The weight file of the small MSSA is only 642 KB. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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13 pages, 642 KiB

Open AccessCommunication

Optimal Position and Target Rate for Covert Communication in UAV-Assisted Uplink RSMA Systems

by Zhengxiang Duan, Xin Yang, Tao Zhang and Ling Wang

Drones 2023, 7(4), 237; https://doi.org/10.3390/drones7040237 - 28 Mar 2023

Cited by 1 | Viewed by 1657

Abstract

With the explosive increase in demand for wireless communication, the issue of wireless communication security has also become a growing concern. In this paper, we investigate a novel covert communication for unmanned aerial vehicle (UAV)-assisted uplink rate-splitting multiple access (RSMA) systems, where a UAV adopts the rate-splitting (RS) strategy to increase the total transmission rate while avoiding deteriorating the covert transmission of a ground user. In the proposed system, a ground user and a UAV adopt the RSMA scheme to simultaneously communicate with a base station surveilled by an evil monitor. The UAV acts as both the transmitter and the friendly jammer to cover the ground user’s transmission with random power. To maximize the expected sum rate (ESR), we first study the RS strategy and obtain the optimal power allocation factor. Then, the closed-form of minimum detection error probability (DEP), ESR, and optimal target rate of the UAV are derived. Constrained by the minimum DEP and expected covert rate (ECR), we maximize the ESR by optimizing the position and target rate of the UAV. Numerical results show that the proposed scheme outperforms the traditional NOMA systems in terms of ESR with the same DEP and ECR. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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25 pages, 13596 KiB

Open AccessFeature PaperArticle

Multi-UAV Trajectory Planning during Cooperative Tracking Based on a Fusion Algorithm Integrating MPC and Standoff

by Bo Li, Chao Song, Shuangxia Bai, Jingyi Huang, Rui Ma, Kaifang Wan and Evgeny Neretin

Drones 2023, 7(3), 196; https://doi.org/10.3390/drones7030196 - 14 Mar 2023

Cited by 18 | Viewed by 3023

Abstract

In this paper, an intelligent algorithm integrating model predictive control and Standoff algorithm is proposed to solve trajectory planning that UAVs may face while tracking a moving target cooperatively in a complex three-dimensional environment. A fusion model using model predictive control and Standoff algorithm is thus constructed to ensure trajectory planning and formation maintenance, maximizing UAV sensors’ detection range while minimizing target loss probability. Meanwhile, with this model, a fully connected communication topology is used to complete the UAV communication, multi-UAV formation can be reconfigured and planned at the minimum cost, keeping off deficiency in avoiding real-time obstacles facing the Standoff algorithm. Simulation validation suggests that the fusion algorithm proves to be more capable of maintaining UAVs in stable formation and detecting the target, compared with the model predictive control algorithm alone, in the process of tracking the moving target in a complex 3D environment. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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18 pages, 3573 KiB

Open AccessFeature PaperArticle

Files Cooperative Caching Strategy Based on Physical Layer Security for Air-to-Ground Integrated IoV

by Weiguang Wang, Hui Li, Yang Liu, Wei Cheng and Rui Liang

Drones 2023, 7(3), 163; https://doi.org/10.3390/drones7030163 - 26 Feb 2023

Cited by 2 | Viewed by 1620

Abstract

Mobile edge cache (MEC)-enabled air-to-ground integrated Internet of Vehicles (IoV) technology can solve wireless network backhaul congestion and high latency, but security problems such as eavesdropping are often ignored when designing cache strategies. In this paper, we propose a joint design of cache strategy and physical layer transmission to improve the security offloading ratio of MEC-enabled air-to-ground IoV. By using the random geometry theory and Laplace transform, we derive the closed-form expression of the network security offloading ratio, which is defined as the probability that the request vehicle (RV) successfully finds the required file around it and obtains the file with a data rate larger than a given threshold. During the file acquisition process, we collectively consider the impact of the successful connection and secure transmission in the vehicle wireless communication. Then, we establish an optimization problem for maximizing the network security offloading ratio, in which the cache strategy and the secure transmission rate are jointly optimized. Furthermore, we propose an alternating optimization algorithm to solve the joint optimization problem. Simulation experiments verify the correctness of our theoretical derivation, and prove that the proposed cache strategy is superior to other existing cache strategies. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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13 pages, 546 KiB

Open AccessArticle

Dynamic Robust Spectrum Sensing Based on Goodness-of-Fit Test Using Bilateral Hypotheses

by Shaoyang Men, Pascal Chargé and Zhe Fu

Drones 2023, 7(1), 18; https://doi.org/10.3390/drones7010018 - 27 Dec 2022

Cited by 1 | Viewed by 1997

Abstract

Dynamic spectrum detection has attracted increasing interest in drone or drone controller detection problems. Spectrum sensing as a promising solution allows us to provide a dynamic spectrum map within the target frequency band by estimating the occupied sub-bands in a specific period. In this paper, a robust Student’s t-distribution model is built to tackle the scenario with a small number of observed samples. Then, relying on the characteristics of the statistical model, we propose an appropriate goodness-of-fit (GoF) test statistic regarding a small number of samples. Moreover, to obtain a reliable sensing, bilateral hypotheses of the test statistic are both used to make a decision. Numerical simulations show the superiority of the proposed method compared with other schemes, including the unilateral hypothesis-based GoF testing and the conventional energy detection, in a small number of sample cases. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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17 pages, 4661 KiB

Open AccessArticle

Vehicle to Everything (V2X) and Edge Computing: A Secure Lifecycle for UAV-Assisted Vehicle Network and Offloading with Blockchain

by Abdullah Ayub Khan, Asif Ali Laghari, Muhammad Shafiq, Shafique Ahmed Awan and Zhaoquan Gu

Drones 2022, 6(12), 377; https://doi.org/10.3390/drones6120377 - 25 Nov 2022

Cited by 28 | Viewed by 3776

Abstract

Due to globalization and advances in network technology, the Internet of Vehicles (IoV) with edge computing has gained increasingly more attention over the last few years. The technology provides a new paradigm to design interconnected distributed nodes in Unmanned Aerial Vehicle (UAV)-assisted vehicle networks for communications between vehicles in smart cities. The process hierarchy of the current UAV-assisted networks is also becoming more multifaceted as more vehicles are connected, requiring accessing and exchanging information, performing tasks, and updating information securely. This poses serious issues and limitations to centralized UAV-assisted vehicle networks, directly affecting computing-intensive tasks and data offloading. This paper bridges these gaps by providing a novel, transparent, and secure lifecycle for UAV-assisted distributed vehicle communication using blockchain hyperledger technology. A modular infrastructure for Vehicle-to-Everything (V2X) is designed and ‘B-UV2X’, a blockchain hyperledger fabric-enabled distributed permissioned network-based consortium structure, is proposed. The participating nodes of the vehicle are interconnected with others in the chain of smart cities and exchange different information such as movement, etc., preserving operational logs on the blockchain-enabled immutable ledger. This automates IoV transactions over the proposed UAV-assisted vehicle-enabled consortium network with doppler spread. Thus, for this purpose, there are four different chain codes that are designed and deployed for IoV registration, adding new transactions, updating the ledger, monitoring resource management, and customized multi-consensus of proof-of-work. For lightweight IoV authentication, B-UV2X uses a two-way verification method with the defined hyperledger fabric consensus mechanism. Transaction protection from acquisition to deliverance and storage uses the NuCypher threshold proxy re-encryption mechanism. Simulation results for the proposed B-UV2X show a reduction in network consumption by 12.17% compared to a centralized network system, an increase in security features of up to 9.76%, and a reduction of 7.93% in the computational load for computed log storage. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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16 pages, 3925 KiB

Open AccessArticle

Enhanced Artificial Gorilla Troops Optimizer Based Clustering Protocol for UAV-Assisted Intelligent Vehicular Network

by Hadeel Alsolai, Jaber S. Alzahrani, Mohammed Maray, Mohammed Alghamdi, Ayman Qahmash, Mrim M. Alnfiai, Amira Sayed A. Aziz and Anwer Mustafa Hilal

Drones 2022, 6(11), 358; https://doi.org/10.3390/drones6110358 - 16 Nov 2022

Cited by 12 | Viewed by 2058

Abstract

The increasing demands of several emergent services brought new communication problems to vehicular networks (VNs). It is predicted that the transmission system assimilated with unmanned aerial vehicles (UAVs) fulfills the requirement of next-generation vehicular network. Because of its higher flexible mobility, the UAV-aided vehicular network brings transformative and far-reaching benefits with extremely high data rates; considerably improved security and reliability; massive and hyper-fast wireless access; much greener, smarter, and longer 3D communications coverage. The clustering technique in UAV-aided VN is a difficult process because of the limited energy of UAVs, higher mobility, unstable links, and dynamic topology. Therefore, this study introduced an Enhanced Artificial Gorilla Troops Optimizer–based Clustering Protocol for a UAV-Assisted Intelligent Vehicular Network (EAGTOC-UIVN). The goal of the EAGTOC-UIVN technique lies in the clustering of the nodes in UAV-based VN to achieve maximum lifetime and energy efficiency. In the presented EAGTOC-UIVN technique, the EAGTO algorithm was primarily designed by the use of the circle chaotic mapping technique. Moreover, the EAGTOC-UIVN technique computes a fitness function with the inclusion of multiple parameters. To depict the improved performance of the EAGTOC-UIVN technique, a widespread simulation analysis was performed. The comparison study demonstrated the enhancements of the EAGTOC-UIVN technique over other recent approaches. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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24 pages, 845 KiB

Open AccessArticle

Physical-Layer Security for UAV-Assisted Air-to-Underwater Communication Systems with Fixed-Gain Amplify-and-Forward Relaying

by Yi Lou, Ruofan Sun, Julian Cheng, Gang Qiao and Jinlong Wang

Drones 2022, 6(11), 341; https://doi.org/10.3390/drones6110341 - 3 Nov 2022

Cited by 8 | Viewed by 2404

Abstract

We analyze a secure unmanned aerial vehicle-assisted two-hop mixed radio frequency (RF) and underwater wireless optical communication (UWOC) system using a fixed-gain amplify-and-forward (AF) relay. The UWOC channel was modeled using a mixture exponential-generalized Gamma distribution to consider the combined effects of air [...] Read more.

α

μ

distributions. Specifically, we first derived both the probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio of the system. Based on the PDF and CDF expressions, we derived the closed-form expressions for the tight lower bound of the secrecy outage probability (SOP) and the probability of non-zero secrecy capacity (PNZ), which are both expressed in terms bivariate Fox’s H-function. To utilize these analytical expressions, we derived asymptotic expressions of SOP and PNZ using only well-known functions. We also used asymptotic expressions to determine the suboptimal transmitting power to maximize energy efficiency. Furthermore, we investigated the effect of levels of air bubbles and temperature gradients in the UWOC channel, and studied the nonlinear characteristics of the transmission medium and the number of multipath clusters of the RF channel on the secrecy performance. Finally, all analyses were validated using a simulation. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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15 pages, 403 KiB

Open AccessArticle

Joint Placement and Power Optimization of UAV-Relay in NOMA Enabled Maritime IoT System

by Woping Xu, Junhui Tian, Li Gu and Shaohua Tao

Drones 2022, 6(10), 304; https://doi.org/10.3390/drones6100304 - 18 Oct 2022

Cited by 6 | Viewed by 2710

Abstract

In this paper, an unmanned aerial vehicle is utilized as an aerial relay to connect onshore base station with offshore users in a maritime IoT system with uplink non-orthogonal multiple access enabled. A coordinated direct and relay transmission scheme is adopted in the proposed system, where close shore maritime users directly communicate with onshore BS and offshore maritime users need assistance of an aerial relay to communicate with onshore BS. We aim to minimize the total transmit energy of the aerial relay by jointly optimizing the UAV hovering position and transmit power allocation. The minimum rate requirements of maritime users and transmitters’ power budgets are considered. The formulated optimization problem is non-convex due to its non-convex constraints. Therefore, we introduce successive convex optimization and block coordinate descent to decompose the original problem into two subproblems, which are alternately solved to optimize the UAV energy consumption with satisfying the proposed constraints. Numerical results indicate that the proposed algorithm outperformed the benchmark algorithm, and shed light on the potential of exploiting the energy-limited aerial relay in IoT systems. Full article

(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks)

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