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Drones
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Advances in Detection, Security, and Communication for UAV: 2nd Edition
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Advances in Detection, Security, and Communication for UAV: 2nd Edition

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5202

Special Issue Editors

Prof. Dr. Liuguo Yin

E-Mail Website
Guest Editor
School of Information Science and Technology, Tsinghua University, Beijing 100084, China
Interests: aerospace communication network; wireless multimedia communication; multi-domain cooperative communication; LDPC encoding and decoding; source-channel joint encoding; quantum security communication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shu Fu

E-Mail Website
Guest Editor
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: B5G/6G ultra-dense cellular network; UAV; low orbit satellite communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of Drones on “Advances in Detection, Security, and Communication for UAV: 2nd Edition”.

With the rapid development of wireless networks, unmanned aerial vehicles (UAVs) have become a field that cannot be ignored in the domain of communication. UAVs are widely used in many fields due to their high flexibility and range of potential. However, the rapid development and wide application of UAVs have also brought a series of challenges, including security, privacy, communication reliability, interference, physical layer security, and coping ability in complex environments. Comprehensive analysis and research to solve these challenges is a key task in the field of drones. To address these challenges, a series of emerging technologies have shown great development potential, covering artificial intelligence (AI), semantic technology, 6G communication, space-air-ground integration technology, endogenous security technology, physical layer security technology, covert communication technology, integrated sensing and communication (ISAC) technology, etc. These new technologies bring new possibilities for the system architecture, key technologies, products, and application fields of UAVs. In order to promote the development of detection, security, and communication for UAVs, this Special Issue aims to provide a platform for researchers in academia and industry to publish their recent research results and discuss opportunities, challenges, and solutions related to UAV detection, security, and communication. We welcome the submission of original research papers on the most advanced technologies and applications related to detection, security, and communication for UAV.

Topics of interest include, but are not limited to, the following scope:

  • New concept, theory, principle, and application of UAV system architecture;
  • Cross-layer optimization for joint detection, security, and communication functions of UAV network;
  • Advanced architecture and application of integrated sensing and communication (ISAC) for UAV;
  • Endogenous security architecture and mechanism for UAV network;
  • UAV enhanced dynamic network towards 6G;
  • Artificial intelligence enhanced UAV networking;
  • Multi-agent game and cooperation mechanism of UAVs;
  • Modulation and coding for UAV communication;
  • Semantic communication for UAV network;
  • Covert communication for UAV;
  • Physical secure communication for UAV;
  • Interference management for UAV;
  • Detection and data collection for UAV;
  • UAV networking for space-air-ground integration;
  • Emergency communication by UAV;
  • Data collection by multi-UAV cooperation;
  • Image processing of UAV inspection for power, forest, and ocean.

Prof. Dr. Liuguo Yin
Prof. Dr. Shu Fu
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

  • UAV communication
  • UAV network and 6G
  • UAV system architecture
  • interference management
  • artificial intelligence
  • detection and data collection
  • image processing
  • UAV network security

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Related Special Issue

Published Papers (6 papers)

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Research

19 pages, 1476 KiB  
Article
Network Design and Content Deployment Optimization for Cache-Enabled Multi-UAV Socially Aware Networks
by Yikun Zou, Gang Wang, Guanyi Chen, Jinlong Wang, Siyuan Yu, Chenxu Wang and Zhiquan Zhou
Drones 2025, 9(8), 568; https://doi.org/10.3390/drones9080568 - 12 Aug 2025
Abstract
Unmanned aerial vehicles (UAVs) with high mobility and self-organization capabilities can establish highly connected networks to cache popular content for edge users, which improves network stability and significantly reduces access time. However, an uneven distribution of demand and storage capacity may reduce the [...] Read more.
Unmanned aerial vehicles (UAVs) with high mobility and self-organization capabilities can establish highly connected networks to cache popular content for edge users, which improves network stability and significantly reduces access time. However, an uneven distribution of demand and storage capacity may reduce the utilization of the storage capacity of UAVs without a proper UAV coordination mechanism. This work proposes a multi-UAV-enabled caching socially aware network (SAN) where UAVs can switch roles by adjusting the social attributes, effectively enhancing data interaction within the UAVs. The proposed network breaks down communication barriers at the UAV layer and integrates the collective storage resources by incorporating social awareness mechanisms to mitigate these imbalances. Furthermore, we formulate a multi-objective optimization problem (MOOP) with the objectives of maximizing both the diversity of cached content and the total request probability (RP) of the network, while employing a multi-objective particle swarm optimization (MOPSO) algorithm with a mutation strategy to approximate the Pareto front. Finally, the impact of key parameters on the Pareto front is analyzed under various scenarios. Simulation results validate the benefits of leveraging social attributes for resource allocation and demonstrate the effectiveness and convergence of the proposed algorithm for the multi-UAV caching strategy. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)
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20 pages, 589 KiB  
Article
Intelligent Queue Scheduling Method for SPMA-Based UAV Networks
by Kui Yang, Chenyang Xu, Guanhua Qiao, Jinke Zhong and Xiaoning Zhang
Drones 2025, 9(8), 552; https://doi.org/10.3390/drones9080552 - 6 Aug 2025
Viewed by 288
Abstract
Static Priority-based Multiple Access (SPMA) is an emerging and promising wireless MAC protocol which is widely used in Unmanned Aerial Vehicle (UAV) networks. UAV (Unmanned Aerial Vehicle) networks, also known as drone networks, refer to a system of interconnected UAVs that communicate and [...] Read more.
Static Priority-based Multiple Access (SPMA) is an emerging and promising wireless MAC protocol which is widely used in Unmanned Aerial Vehicle (UAV) networks. UAV (Unmanned Aerial Vehicle) networks, also known as drone networks, refer to a system of interconnected UAVs that communicate and collaborate to perform tasks autonomously or semi-autonomously. These networks leverage wireless communication technologies to share data, coordinate movements, and optimize mission execution. In SPMA, traffic arriving at the UAV network node can be divided into multiple priorities according to the information timeliness, and the packets of each priority are stored in the corresponding queues with different thresholds to transmit packet, thus guaranteeing the high success rate and low latency for the highest-priority traffic. Unfortunately, the multi-priority queue scheduling of SPMA deprives the packet transmitting opportunity of low-priority traffic, which results in unfair conditions among different-priority traffic. To address this problem, in this paper we propose the method of Adaptive Credit-Based Shaper with Reinforcement Learning (abbreviated as ACBS-RL) to balance the performance of all-priority traffic. In ACBS-RL, the Credit-Based Shaper (CBS) is introduced to SPMA to provide relatively fair packet transmission opportunity among multiple traffic queues by limiting the transmission rate. Due to the dynamic situations of the wireless environment, the Q-learning-based reinforcement learning method is leveraged to adaptively adjust the parameters of CBS (i.e., idleslope and sendslope) to achieve better performance among all priority queues. The extensive simulation results show that compared with traditional SPMA protocol, the proposed ACBS-RL can increase UAV network throughput while guaranteeing Quality of Service (QoS) requirements of all priority traffic. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)
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19 pages, 3297 KiB  
Article
Secrecy Rate Maximization via Joint Robust Beamforming and Trajectory Optimization for Mobile User in ISAC-UAV System
by Lvxin Xu, Zhi Zhang and Liuguo Yin
Drones 2025, 9(8), 536; https://doi.org/10.3390/drones9080536 - 30 Jul 2025
Viewed by 216
Abstract
Unmanned aerial vehicles (UAVs) have emerged as a promising platform for integrated sensing and communication (ISAC) due to their mobility and deployment flexibility. By adaptively adjusting their flight trajectories, UAVs can maintain favorable line-of-sight (LoS) communication links and sensing angles, thus enhancing overall [...] Read more.
Unmanned aerial vehicles (UAVs) have emerged as a promising platform for integrated sensing and communication (ISAC) due to their mobility and deployment flexibility. By adaptively adjusting their flight trajectories, UAVs can maintain favorable line-of-sight (LoS) communication links and sensing angles, thus enhancing overall system performance in dynamic and complex environments. However, ensuring physical layer security (PLS) in such UAV-assisted ISAC systems remains a significant challenge, particularly in the presence of mobile users and potential eavesdroppers. This manuscript proposes a joint optimization framework that simultaneously designs robust transmit beamforming and UAV trajectories to secure downlink communication for multiple ground users. At each time slot, the UAV predicts user positions and maximizes the secrecy sum-rate, subject to constraints on total transmit power, multi-target sensing quality, and UAV mobility. To tackle this non-convex problem, we develop an efficient optimization algorithm based on successive convex approximation (SCA) and constrained optimization by linear approximations (COBYLA). Numerical simulations validate that the proposed framework effectively enhances the secrecy performance while maintaining high-quality sensing, achieving near-optimal performance under realistic system constraints. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)
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25 pages, 5051 KiB  
Article
Unmanned Aerial Vehicle Anomaly Detection Based on Causality-Enhanced Graph Neural Networks
by Chen Feng, Jun Fan, Zhiliang Liu, Guang Jin and Siya Chen
Drones 2025, 9(6), 408; https://doi.org/10.3390/drones9060408 - 3 Jun 2025
Cited by 1 | Viewed by 976
Abstract
With the widespread application of unmanned aerial vehicles (UAVs), the safety detection system of UAVs has created an urgent need for anomaly detection technology. As a direct representation of system health status, flight data contain critical status information, driving data-driven methods to gradually [...] Read more.
With the widespread application of unmanned aerial vehicles (UAVs), the safety detection system of UAVs has created an urgent need for anomaly detection technology. As a direct representation of system health status, flight data contain critical status information, driving data-driven methods to gradually replace traditional dynamic modeling as the mainstream paradigm. The former effectively circumvent the problems of nonlinear coupling and parameter uncertainty in complex dynamic modeling. However, data-driven methods still face two major challenges: the scarcity of anomalous flight data and the difficulty in extracting strong spatio-temporal coupling among flight parameters. To address these challenges, we propose an unsupervised anomaly detection method based on the causality-enhanced graph neural network (CEG). CEG innovatively introduces a causality model among flight parameters, achieving targeted extraction of spatial features through a causality-enhanced graph attention mechanism. Furthermore, CEG incorporates a trend-decomposed temporal feature extraction module to capture temporal dependencies in high-dimensional flight data. A low-rank regularization training paradigm is designed for CEG, and a residual adaptive bidirectional smoothing strategy is employed to eliminate the influence of noise. Experimental results on the ALFA dataset demonstrate that CEG outperforms state-of-the-art methods in terms of Precision, Recall, and F1 score. The proposed method enables accurate and robust anomaly detection on a wide range of anomaly types such as engines, rudders, and ailerons, validating its effectiveness in handling the unique challenges of UAV anomaly detection. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)
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19 pages, 7134 KiB  
Article
Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication
by Donghao Li, Binfang Du and Zhiquan Bai
Drones 2025, 9(3), 160; https://doi.org/10.3390/drones9030160 - 21 Feb 2025
Cited by 1 | Viewed by 1620
Abstract
The growing interest in integrated sensing and communication (ISAC) has accelerated the development of unmanned aerial vehicles (UAVs) and drones for secure data transmission. In this study, the optimization of UAV trajectory and bandwidth allocation within the ISAC framework is investigated, with a [...] Read more.
The growing interest in integrated sensing and communication (ISAC) has accelerated the development of unmanned aerial vehicles (UAVs) and drones for secure data transmission. In this study, the optimization of UAV trajectory and bandwidth allocation within the ISAC framework is investigated, with a focus on covert communication under energy constraints. We propose a novel deep reinforcement learning (DRL) algorithm, Soft Actor-Critic for Covert Communication and Charging (SAC-CC), to address this problem. The SAC-CC algorithm maximizes the CCTR by dynamically allocating bandwidth for sensing and communication tasks while adjusting the UAV’s trajectory to manage energy consumption. This approach ensures accurate tracking of the adversarial UAV to maintain effective covert communication. Experimental results show that SAC-CC significantly outperforms existing DRL algorithms in CCTR and improves UAV endurance. Also, its robustness under different adversarial trajectories, covert communication requirements, and charging conditions is validated. Furthermore, the UAV’s flight altitude, along with the number and distribution pattern of adversarial UAVs, directly affect covert communication performance. Finally, the study emphasizes the trade-offs among bandwidth allocation, sensing accuracy, and the balance between power spectral density and UAV energy capacity, providing key insights for the practical configuration of bandwidth and energy parameters in UAV-assisted ISAC systems. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)
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21 pages, 463 KiB  
Article
DEGNN: A Deep Learning-Based Method for Unmanned Aerial Vehicle Software Security Analysis
by Jiang Du, Qiang Wei, Yisen Wang and Xingyu Bai
Drones 2025, 9(2), 110; https://doi.org/10.3390/drones9020110 - 2 Feb 2025
Viewed by 1134
Abstract
With the increasing utilization of drones, the cyber security threats they face have become more prominent. Code reuse in the software development of drone systems has led to vulnerabilities in drones. The binary code similarity analysis method offers a way to analyze drone [...] Read more.
With the increasing utilization of drones, the cyber security threats they face have become more prominent. Code reuse in the software development of drone systems has led to vulnerabilities in drones. The binary code similarity analysis method offers a way to analyze drone firmware lacking source code. This paper proposes DEGNN, a novel graph neural network for binary code similarity analysis. It uses call-enhanced control graphs and attention mechanisms to generate dual embeddings of functions and predict similarity based on graph structures and node features. DEGNN is effective in cross-architecture tasks. Experimental results show that in the cross-architecture binary function search, DEGNN’s mean reciprocal rank and recall@1 surpass the state of the art by 12% and 28.6%, respectively. In the cross-architecture real-world vulnerability search, specifically targeting drone systems, it has a 33.3% performance improvement over the SOTA model, indicating its great potential in enhancing drone cyber security. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)
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