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Drones
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Advances in Detection, Security, and Communication for UAV
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Advances in Detection, Security, and Communication for UAV

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

Deadline for manuscript submissions: 16 October 2024 | Viewed by 9035

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
Prof. 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,

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
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

Published Papers (5 papers)

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Research

18 pages, 528 KiB  
Article
Dual-driven Learning-Based Multiple-Input Multiple-Output Signal Detection Unmanned Aerial Vehicle Air-to-Ground Communications
by Haihan Li , Yongming He , Shuntian Zheng , Fan Zhou  and Hongwen Yang 
Drones 2024, 8(5), 180; https://doi.org/10.3390/drones8050180 - 02 May 2024
Viewed by 306
Abstract
Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space–air–ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article [...] Read more.
Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space–air–ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article presents a novel dual-driven learning-based network for millimeter-wave (mm-wave) massive MIMO symbol detection of UAV AG communications. Our main contribution is that the proposed approach combines a data-driven symbol-correction network with a model-driven orthogonal approximate message passing network (OAMP-Net). Through joint training, the dual-driven network reduces symbol detection errors propagated through each iteration of the model-driven OAMP-Net. The numerical results demonstrate the superiority of the dual-driven detector over the conventional minimum mean square error (MMSE), orthogonal approximate message passing (OAMP), and OAMP-Net detectors at various noise powers and channel estimation errors. The dual-driven MIMO detector exhibits a 2–3 dB lower signal-to-noise ratio (SNR) requirement compared to the MMSE and OAMP-Net detectors to achieve a bit error rate (BER) of 1×10−2 when the channel estimation error is −30 dB. Moreover, the dual-driven MIMO detector exhibits an increased tolerance to channel estimation errors by 2–3 dB to achieve a BER of 1×10−3. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)
17 pages, 1208 KiB  
Article
The Sound of Surveillance: Enhancing Machine Learning-Driven Drone Detection with Advanced Acoustic Augmentation
by Sebastian Kümmritz
Drones 2024, 8(3), 105; https://doi.org/10.3390/drones8030105 - 19 Mar 2024
Viewed by 838
Abstract
In response to the growing challenges in drone security and airspace management, this study introduces an advanced drone classifier, capable of detecting and categorizing Unmanned Aerial Vehicles (UAVs) based on acoustic signatures. Utilizing a comprehensive database of drone sounds across EU-defined classes (C0 [...] Read more.
In response to the growing challenges in drone security and airspace management, this study introduces an advanced drone classifier, capable of detecting and categorizing Unmanned Aerial Vehicles (UAVs) based on acoustic signatures. Utilizing a comprehensive database of drone sounds across EU-defined classes (C0 to C3), this research leverages machine learning (ML) techniques for effective UAV identification. The study primarily focuses on the impact of data augmentation methods—pitch shifting, time delays, harmonic distortion, and ambient noise integration—on classifier performance. These techniques aim to mimic real-world acoustic variations, thus enhancing the classifier’s robustness and practical applicability. Results indicate that moderate levels of augmentation significantly improve classification accuracy. However, excessive application of these methods can negatively affect performance. The study concludes that sophisticated acoustic data augmentation can substantially enhance ML-driven drone detection, providing a versatile and efficient tool for managing drone-related security risks. This research contributes to UAV detection technology, presenting a model that not only identifies but also categorizes drones, underscoring its potential for diverse operational environments. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)
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29 pages, 10590 KiB  
Article
GA-Net: Accurate and Efficient Object Detection on UAV Images Based on Grid Activations
by Ruiyi Zhang, Bin Luo, Xin Su and Jun Liu
Drones 2024, 8(3), 74; https://doi.org/10.3390/drones8030074 - 21 Feb 2024
Viewed by 1233
Abstract
Object detection plays a crucial role in unmanned aerial vehicle (UAV) missions, where captured objects are often small and require high-resolution processing. However, this requirement is always in conflict with limited computing resources, vast fields of view, and low latency requirements. To tackle [...] Read more.
Object detection plays a crucial role in unmanned aerial vehicle (UAV) missions, where captured objects are often small and require high-resolution processing. However, this requirement is always in conflict with limited computing resources, vast fields of view, and low latency requirements. To tackle these issues, we propose GA-Net, a novel approach tailored for UAV images. The key innovation includes the Grid Activation Module (GAM), which efficiently calculates grid activations, the probability of foreground presence at grid scale. With grid activations, the GAM helps filter out patches without objects, minimize redundant computations, and improve inference speeds. Additionally, the Grid-based Dynamic Sample Selection (GDSS) focuses the model on discriminating positive samples and hard negatives, addressing background bias during training. Further enhancements involve GhostFPN, which refines Feature Pyramid Network (FPN) using Ghost module and depth-wise separable convolution. This not only expands the receptive field for improved accuracy, but also reduces computational complexity. We conducted comprehensive evaluations on DGTA-Cattle-v2, a synthetic dataset with added background images, and three public datasets (VisDrone, SeaDronesSee, DOTA) from diverse domains. The results prove the effectiveness and practical applicability of GA-Net. Despite the common accuracy and speed trade-off challenge, our GA-Net successfully achieves a mutually beneficial scenario through the strategic use of grid activations. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)
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20 pages, 6066 KiB  
Article
Smart Drone Surveillance System Based on AI and on IoT Communication in Case of Intrusion and Fire Accident
by Minh Long Hoang
Drones 2023, 7(12), 694; https://doi.org/10.3390/drones7120694 - 02 Dec 2023
Cited by 3 | Viewed by 4475
Abstract
Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things [...] Read more.
Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things (IoT) network. The system includes a Passive Pyroelectric Infrared Detector for human detection and an analog flame sensor to sense the appearance of the concerned objects and then transmit the signal to the workstation via Wi-Fi based on the microcontroller Espressif32 (Esp32). The computer vision models YOLOv8 (You Only Look Once version 8) and Cascade Classifier are trained and implemented into the workstation, which is able to identify people, some potentially dangerous objects, and fire. The drone is also controlled by three algorithms—distance maintenance, automatic yaw rotation, and potentially dangerous object avoidance—with the support of a proportional–integral–derivative (PID) controller. The Smart Drone Surveillance System has good commands for automatic tracking and streaming of the video of these specific circumstances and then transferring the data to the involved parties such as security or staff. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)
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18 pages, 6706 KiB  
Article
Detection of Volatile Organic Compounds (VOCs) in Indoor Environments Using Nano Quadcopter
by Aline Mara Oliveira, Aniel Silva Morais, Gabriela Vieira Lima, Rafael Monteiro Jorge Alves Souza and Luis Cláudio Oliveira-Lopes
Drones 2023, 7(11), 660; https://doi.org/10.3390/drones7110660 - 06 Nov 2023
Viewed by 1472
Abstract
The dispersion of chemical gases poses a threat to human health, animals, and the environment. Leaks or accidents during the handling of samples and laboratory materials can result in the uncontrolled release of hazardous or explosive substances. Therefore, it is crucial to monitor [...] Read more.
The dispersion of chemical gases poses a threat to human health, animals, and the environment. Leaks or accidents during the handling of samples and laboratory materials can result in the uncontrolled release of hazardous or explosive substances. Therefore, it is crucial to monitor gas concentrations in environments where these substances are manipulated. Gas sensor technology has evolved rapidly in recent years, offering increasingly precise and reliable solutions. However, there are still challenges to be overcome, especially when sensors are deployed on unmanned aerial vehicles (UAVs). This article discusses the use of UAVs to locate gas sources and presents real test results using the SGP40 metal oxide semiconductor gas sensor onboard the Crazyflie 2.1 nano quadcopter. The solution proposed in this article uses an odor source identification strategy, employing a gas distribution mapping approach in a three-dimensional environment. The aim of the study was to investigate the feasibility and effectiveness of this approach for detecting gases in areas that are difficult to access or dangerous for humans. The results obtained show that the use of drones equipped with gas sensors is a promising alternative for the detection and monitoring of gas leaks in closed environments. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)
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