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Emerging Unmanned Aerial Vehicle Communication Techniques for the Next Generation...
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Emerging Unmanned Aerial Vehicle Communication Techniques for the Next Generation of Wireless Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (16 November 2024) | Viewed by 11920

Special Issue Editors

Dr. Nan Qi

E-Mail Website
Guest Editor
Department of Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: UAV-assisted communications; optimization of wireless communications; opportunistic spectrum access; learning theory; game theory
Prof. Dr. Rugui Yao

E-Mail Website
Guest Editor
School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710072, China
Interests: high-speed communication; AI enabled communication; MIMO communication; array signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiang Su

E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
Interests: Internet of Things; edge and fog computing; networking; distributed systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liang Xiao

E-Mail Website
Guest Editor
Department of Informatics and Communication Engineering, Xiamen University, Fujian 361005, China
Interests: network security; wireless communications; smart grids
Dr. Lin Zhang

E-Mail Website
Guest Editor
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: wireless AI; wireless resource management

Special Issue Information

Dear Colleagues,

For several years, the academic and industrial communities have been sustainably elaborating on in-depth explorations and comprehensive innovations of B5/6G technology. Briefly, B5/6G aims to achieve seamless coverage, ultra-reliability, and ultra-low latency. At the same time, B5/6G is challenged by the explosive growth in the number of communication devices, increasingly severe communication security issues, and the challenge of communication reliability caused by interference. UAVs have been widely used in assisting wireless networks due to their advantages of low cost, flexible deployment, and high mobility, especially in communication scenarios in hard-to-reach areas (e.g., fire and earthquake-affected areas). In particular, with the advancement of artificial intelligence, movement and communication strategies can be guided and flexibly adjusted using learning-based approaches.

This Special Issue aims to outline the recent progress made on the latest techniques in enhancing the performance of UAV-assisted next-generation wireless networks. Specifically, the performance includes, but is not limited to, energy efficiency, high reliability, high security, and low latency:

  • AI for energy/spectrum efficient resource management in UAV networks;
  • Cooperative mobility edge computing in UAV networks;
  • Relaying techniques for UAV networks;
  • Interference mitigation in UAV networks;
  • Intelligent trajectory planning for UAV networks;
  • AI for anti-jamming in UAV networks;
  • Anti-eavesdropping techniques in UAV networks;
  • Beamforming and waveform design for UAV communications nodes;
  • Low-latency UAV swarm networks;
  • Routing protocol for UAV swarm networks;
  • UAV semantic communication;
  • Integrated sensing and communication applications in UAV-assisted networks;
  • Reconfigurable intelligent surface-assisted UAV communications.

Submissions focused on the various advanced analytical algorithms and technological innovations used to enhance the performance of UAV networks are welcome.

Dr. Nan Qi
Prof. Dr. Rugui Yao
Dr. Xiang Su
Prof. Dr. Liang Xiao
Dr. Lin 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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • artificial intelligence
  • unmanned aerial vehicle communications
  • energy efficiency
  • high reliability
  • security and safety
  • low-latency routing protocol
  • reconfigurable intelligent surface

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Published Papers (5 papers)

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Research

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21 pages, 2680 KiB  
Article
TACSan: Enhancing Vulnerability Detection with Graph Neural Network
by Qingyao Zeng, Dapeng Xiong, Zhongwang Wu, Kechang Qian, Yu Wang and Yinghao Su
Electronics 2024, 13(19), 3813; https://doi.org/10.3390/electronics13193813 - 26 Sep 2024
Viewed by 1887
Abstract
With the increasing scale and complexity of software, the advantages of using neural networks for static vulnerability detection are becoming increasingly prominent. Before inputting into a neural network, the source code needs to undergo word embedding, transforming discrete high-dimensional text data into low-dimensional [...] Read more.
With the increasing scale and complexity of software, the advantages of using neural networks for static vulnerability detection are becoming increasingly prominent. Before inputting into a neural network, the source code needs to undergo word embedding, transforming discrete high-dimensional text data into low-dimensional continuous vectors suitable for training in neural networks. However, analysis has revealed that different implementation ideas by code writers for the same functionality can lead to varied code implementation methods. Embedding different code texts into vectors results in distinctions that can reduce the robustness of a model. To address this issue, this paper explores the impact of converting source code into different forms on word embedding and finds that a TAC (Three-Address Code) can significantly eliminate noise caused by different code implementation approaches. Given the excellent capability of a GNN (Graph Neural Network) in handling non-Euclidean space data and complex features, this paper subsequently employs a GNN to learn and classify vulnerabilities by capturing the implicit syntactic structure information in a TAC. Based on this, this paper introduces TACSan, a novel static vulnerability detection system based on a GNN designed to detect vulnerabilities in C/C++ programs. TACSan transforms the preprocessed source code into a TAC representation, adds control and data edges to create a graph structure, and then inputs it into the GNN for training. Comparative testing and evaluation of TACSan against other renowned static analysis tools, such as VulDeePecker and Devign, demonstrate that TACSan’s detection capabilities not only exceed those methods but also achieve substantial enhancements in accuracy and F1 score. Full article
(This article belongs to the Special Issue Emerging Unmanned Aerial Vehicle Communication Techniques for the Next Generation of Wireless Networks)
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13 pages, 2630 KiB  
Article
Jamming Analysis between Non-Cooperative Mega-Constellations Based on Satellite Network Capacity
by Yunfan Zhang, Chi Han, Feihuang Chu, Wei Xiong and Luliang Jia
Electronics 2024, 13(12), 2330; https://doi.org/10.3390/electronics13122330 - 14 Jun 2024
Cited by 1 | Viewed by 892
Abstract
Due to the openness of inter-satellite links (ISLs) in mega-constellations, the threat posed by jamming from non-cooperative constellations is becoming increasingly significant. Most of the existing approaches focus on the up/down link capacity between satellites and ground stations, which differs greatly from the [...] Read more.
Due to the openness of inter-satellite links (ISLs) in mega-constellations, the threat posed by jamming from non-cooperative constellations is becoming increasingly significant. Most of the existing approaches focus on the up/down link capacity between satellites and ground stations, which differs greatly from the situation whereby ISLs are subjected to non-cooperative jamming. Therefore, this work investigates the transmission rates of ISLs under jamming from non-cooperative mega-constellations. Based on this, a novel satellite network capacity calculation method is proposed to evaluate the mega-constellation network capacity when the transmission rates change dynamically. The simulation results show that the satellite number, jamming power and inclination of non-cooperative constellations have a significant influence on the network capacity. The optimal jamming efficiency occurs when the constellation inclinations are close. Full article
(This article belongs to the Special Issue Emerging Unmanned Aerial Vehicle Communication Techniques for the Next Generation of Wireless Networks)
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17 pages, 518 KiB  
Article
WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer
by Qingyao Zeng, Dapeng Xiong, Zhongwang Wu, Kechang Qian, Yu Wang and Yinghao Su
Electronics 2024, 13(11), 2096; https://doi.org/10.3390/electronics13112096 - 28 May 2024
Viewed by 1201
Abstract
As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward [...] Read more.
As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward energy distribution for the seeds. To address these issues, a dynamic distance-weighting-based distance estimation strategy is proposed first, which facilitates strategies for seed distribution that take energy into consideration. Second, to overcome the limitations of current seed energy distribution strategies, the gray wolf optimizer (GWO) is improved by integrating four strategies, leading to the development of the improved gray wolf optimizer (IGWO). Lastly, an adaptive search algorithm is proposed, and the WolfFuzz prototype tool is implemented. In vulnerability recurrence scenarios, WolfFuzz is 3.2× faster on average compared with the baseline and reproduces 76.4% of existing bugs faster. WolfFuzz also discovers nine different types of bugs in seven real-world programs. Full article
(This article belongs to the Special Issue Emerging Unmanned Aerial Vehicle Communication Techniques for the Next Generation of Wireless Networks)
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16 pages, 6318 KiB  
Article
Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach
by Shuqi Wang, Nan Qi, Hua Jiang, Ming Xiao, Haoxuan Liu, Luliang Jia and Dan Zhao
Electronics 2024, 13(8), 1592; https://doi.org/10.3390/electronics13081592 - 22 Apr 2024
Cited by 7 | Viewed by 2018
Abstract
Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, [...] Read more.
Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV’s 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm. Full article
(This article belongs to the Special Issue Emerging Unmanned Aerial Vehicle Communication Techniques for the Next Generation of Wireless Networks)
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Review

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31 pages, 968 KiB  
Review
A Comprehensive Survey of Distributed Denial of Service Detection and Mitigation Technologies in Software-Defined Network
by Yinghao Su, Dapeng Xiong, Kechang Qian and Yu Wang
Electronics 2024, 13(4), 807; https://doi.org/10.3390/electronics13040807 - 19 Feb 2024
Cited by 7 | Viewed by 5267
Abstract
The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible [...] Read more.
The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible one. However, due to its inherent architectural flaws, SDN still faces new security threats. This paper expounds on the architecture and security of SDN, analyzes the vulnerabilities of SDN architecture, and introduces common distributed denial of service (DDoS) attacks within the SDN architecture. This article also provides a review of the relevant literature on DDoS attack detection and mitigation in the current SDN environment based on the technologies used, including statistical analysis, machine learning, policy-based, and moving target defense techniques. The advantages and disadvantages of these technologies, in terms of deployment difficulty, accuracy, and other factors, are analyzed. Finally, this study summarizes the SDN experimental environment and DDoS attack traffic generators and datasets of the reviewed literature and the limitations of current defense methods and suggests potential future research directions. Full article
(This article belongs to the Special Issue Emerging Unmanned Aerial Vehicle Communication Techniques for the Next Generation of Wireless Networks)
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