Physical Layer Security of 6G Vehicular Networks with UAV Systems:First Order Secrecy Metrics,Optimization,and Bounds

The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.

Sensing-Communication-Computing-Control Closed Loop for NOMA-UAV Systems

In the areas without terrestrial communication infrastructures,unmanned aerial vehicles(UAVs)can be utilized to serve field robots for mission-critical tasks.For this purpose,UAVs can be equipped with sensing,communication,and computing modules to support various requirements of robots.In the task process,different modules assist the robots to perform tasks in a closed-loop way,which is referred to as a sensing-communication-computing-control(SC3)loop.In this work,we investigate a UAV-aided system containing multiple SC^(3)loops,which leverages non-orthogonal multiple access(NOMA)for efficient resource sharing.We describe and compare three different modelling levels for the SC^(3)loop.Based on the entropy SC^(3)loop model,a sum linear quadratic regulator(LQR)control cost minimization problem is formulated by optimizing the communication power.Further for the assure-to-be-stable case,we show that the original problem can be approximated by a modified user fairness problem,and accordingly gain more insights into the optimal solutions.Simulation results demonstrate the performance gain of using NOMA in such task-oriented systems,as well as the superiority of our proposed closed-loop-oriented design.

Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle(UAV)in the presence of parameter uncertainties,unmodeled uncertainties and wind disturbances.First,a fixed-time disturbance observer(FXDO)based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model.Then,a fixed-time integral sliding mode control(FXISMC)is combined with the FXDO to achieve strong robustness and chattering reduction.Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability.Finally,numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances.In addition,the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

UAV-Assisted Multi-Object Computing Offloading for Blockchain-Enabled Vehicle-to-Everything Systems

This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)systems.Due to the presence of an eavesdropper(Eve),the system’s com-munication links may be insecure.This paper proposes deploying an intelligent reflecting surface(IRS)on the UAV to enhance the communication performance of mobile vehicles,improve system flexibility,and alleviate eavesdropping on communication links.The links for uploading task data from vehicles to a base station(BS)are protected by IRS-assisted physical layer security(PLS).Upon receiving task data,the computing resources provided by the edge computing servers(MEC)are allocated to vehicles for task execution.Existing blockchain-based computation offloading schemes typically focus on improving network performance,such as minimizing energy consumption or latency,while neglecting the Gas fees for computation offloading and the costs required for MEC computation,leading to an imbalance between service fees and resource allocation.This paper uses a utility-oriented computation offloading scheme to balance costs and resources.This paper proposes alternating phase optimization and power optimization to optimize the energy consumption,latency,and communication secrecy rate,thereby maximizing the weighted total utility of the system.Simulation results demonstrate a notable enhancement in the weighted total system utility and resource utilization,thereby corroborating the viability of our approach for practical applications.

Performance analysis of UAV-based mixed underwater PLC-RF systems

In this study,we analyzed the performance of an Unmanned Aerial Vehicle(UAV)-based mixed Underwater Power Line Communication-Radio Frequency(UPLC-RF)network.In this network,a buoy located at the sea is used as a relay to transmit signals from the underwater signal source to the UAV through the PLC link.We assume that the UPLC channel obeys a log-normal distribution and that the RF link follows the Rician distribution.Using this model,we obtained the closed-form expressions for the Outage Probability(OP),Average Bit-error-rate(ABER),and Average Channel Capacity(ACC).In addition,the asymptotic analysis of the OP and ABER was performed,and an upper bound for the average capacity was obtained.Finally,the analytical results were verified by Monte Carlo simulation thereby demonstrating the effect of impulse noise and the altitude of the UAV on network performance.

Evaluating Global Navigation Satellite System (GNSS) Constellation Performance for Unmanned Aerial Vehicle (UAV) Navigation Precision

Accurate localization is paramount for unmanned aerial vehicles (UAVs) spanning various technical and industrial domains, necessitating a comprehensive assessment of global navigation satellite system (GNSS) precision. This study investigates the performance of distinct GNSS constellations in determining the precise location of a building utilizing a high-precision GNSS receiver. The receiver, incorporating advanced multi-frequency and full-constellation positioning capabilities, was integrated with a smartphone via Bluetooth to enable the UAV’s acquisition of centimeter-level positioning data. Sequential utilization of single satellite systems—such as GPS-only, GLONASS-only, Galileo-only, SBAS-only, and BeiDou-only—facilitated the documentation of latitude and longitude coordinates for the designated building. Subsequent comparison of these coordinates with a specialized Geographic Information System (GIS) was conducted to evaluate their positional accuracy. The comparative analysis underscores significant variability in the precision offered by each satellite constellation, providing valuable insights for optimizing UAV navigation across GIS, IoT, construction, and other sectors requiring high-precision localization. This research underscores the significance of high-precision GNSS receivers in enhancing UAV-based geospatial assessments, emphasizing the critical selection of appropriate satellite systems for tailored localization tasks. The study contributes to advancing UAV navigation strategies, ensuring robust and accurate geospatial data collection within diverse operational frameworks.

Significant Advancements in UAV Technology for Reliable Oil and Gas Pipeline Monitoring

Unmanned aerial vehicles(UAVs)technology is rapidly advancing,offering innovative solutions for various industries,including the critical task of oil and gas pipeline surveillance.However,the limited flight time of conventional UAVs presents a significant challenge to comprehensive and continuous monitoring,which is crucial for maintaining the integrity of pipeline infrastructure.This review paper evaluates methods for extending UAV flight endurance,focusing on their potential application in pipeline inspection.Through an extensive literature review,this study identifies the latest advancements in UAV technology,evaluates their effectiveness,and highlights the existing gaps in achieving prolonged flight operations.Advanced techniques,including artificial intelligence(AI),machine learning(ML),and deep learning(DL),are reviewed for their roles in pipeline monitoring.Notably,DL algorithms like You Only Look Once(YOLO)are explored for autonomous flight in UAV-based inspections,real-time defect detection,such as cracks,corrosion,and leaks,enhancing reliability and accuracy.A vital aspect of this research is the proposed deployment of a hybrid drone design combining lighter-than-air(LTA)and heavier-than-air(HTA)principles,achieving a balance of endurance and maneuverability.LTA vehicles utilize buoyancy to reduce energy consumption,thereby extending flight durations.The paper details the methodology for designing LTA vehicles,presenting an analysis of design parameters that align with the requirements for effective pipeline surveillance.The ongoing work is currently at Technology Readiness Level(TRL)4,where key components have been validated in laboratory conditions,with fabrication and flight testing planned for the next phase.Initial design analysis indicates that LTA configurations could offer significant advantages in flight endurance compared to traditional UAV designs.These findings lay the groundwork for future fabrication and testing phases,which will be critical in validating and assessing the proposed approach’s real-world applicability.By outlining the technical complexities and proposing specialized techniques tailored for pipeline monitoring,this paper provides a foundational framework for advancing UAV capabilities in the oil and gas sector.Researchers and industry practitioners can use this roadmap to further develop UAV-enabled surveillance solutions,aiming to improve the reliability,efficiency,and safety of pipeline monitoring.

Low-profile,low sidelobe array antenna with ultrawide beam coverage for UAV communication

This paper presents a design method to implement an antenna array characterized by ultra-wide beam coverage,low profile,and low Sidelobe Level(SLL)for the application of Unmanned Aerial Vehicle(UAV)air-to-ground communication.The array consists of ten broadside-radiating,ultrawide-beamwidth elements that are cascaded by a central-symmetry series-fed network with tapered currents following Dolph-Chebyshev distribution to provide low SLL.First,an innovative design of end-fire Huygens source antenna that is compatible with metal ground is presented.A low-profile,half-mode Microstrip Patch Antenna(MPA)is utilized to serve as the magnetic dipole and a monopole is utilized to serves as the electric dipole,constructing the compact,end-fire,grounded Huygens source antenna.Then,two opposite-oriented end-fire Huygens source antennas are seamlessly integrated into a single antenna element in the form of monopole-loaded MPA to accomplish the ultrawide,broadside-radiating beam.Particular consideration has been applied into the design of series-fed network as well as antenna element to compensate the adverse coupling effects between elements on the radiation performance.Experiment indicates an ultrawide Half-Power Beamwidth(HPBW)of 161°and a low SLL of-25 dB with a high gain of 12 d Bi under a single-layer configuration.The concurrent ultrawide beamwidth and low SLL make it particularly attractive for applications of UAV air-to-ground communication.

基于UAV激光雷达+InSAR技术+关键区域GNSS连续观测的矿地表沉陷监测方案研究

煤炭开采势必会造成地表沉陷,而对地表沉陷规律进行研究,可以对地表建筑物的保护做到有的放矢。以往传统的沉陷规律研究通常采取布设常规沉陷观测站,借助全站仪进行数据采集。针对常规观测站观测存在的观测周期长、外业劳动强度大、固定测点不易保护等不足,各个矿区均在寻求更合适的监测手段。随着测绘新技术的不断出现,一些新的测量方法也被应用于矿山监测,如InSAR技术、无人机激光雷达技术等。基于此,本文结合传统地表移动观测站,利用无人机激光雷达技术和InSAR技术对某矿煤矿3112工作面开采地表沉陷进行全面监测。

基于OFDM信号的UAV测控数据链通信稳定性研究

无人机数据链通信的稳定性成为制约无人机性能的重要因素之一,若能够在测控数据采集处理过程中对数据精度进行优化处理,将极大地提高数据通信稳定性。为此,本研究结合正交频分复用信号的基本原理和特点,提出了一种新的测角算法。该算法通过设置对应的列阵雷达、差波生成方法对无人机角度估计。然后在测角结果的基础上,利用卷积神经网络和应答机制对测控数据进行优化处理,进一步校正数据误差。实验结果表明:本文的设计方法在无人机测控数据链通信稳定性方面具有显著优势,能够提供更加准确有效的测控数据,为无人机操作提供有效依据。研究结果也可以为其他无线通信系统的稳定性和可靠性问题提供有益的参考和借鉴。

Computing Challenges of UAV Networks: A Comprehensive Survey

Devices and networks constantly upgrade,leading to rapid technological evolution.Three-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information exchange.Various network types,including sensor networks and 5G mobile networks,support this transmission.Notably,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/transmission.Key topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future research.Additionally,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility models.It also explores the prospects of UAV systems and presents state-of-the-art solutions.

Channel Modeling and Performance Analysis for UAV Relay Systems

In this paper, a multi-hop relay channel model based on unmanned aerial vehicles(UAVs) is established by taking into account of the propagation loss, shadowing, and multi-path fading. Based on the proposed channel model, the cascaded propagation loss of relay link and the cascaded probability density function(PDF) of channel fading are derived. Moreover, the theoretical performance of the UAV-based relay system, i.e., the outage probability, bit error rate(BER), and channel capacity, is also analysed and derived. Simulation results show agreement with theoretical results for the hill, mountain, and sea scenarios, indicating the accuracy of both the simulations and derivations.

Control system design of flying-wing UAV based on nonlinear methodology

In this paper, A fluid vector rudder flying-wing UAV is employed as the design object, so as to study the nonlinear design method and flight validation. For the maneuvering flight control, this paper presents a control structure. This control structure included the inner loop linearization decoupling methods to eliminate the known negative coupling and the outer loop backstepping methods for trajectory tracking control. The stability of the control structure has been proved in this paper. Compared with the traditional backstepping control method, this controller increases the inner loop decoupling structure and retains the aerodynamic damping term which makes the linearized system a weak nonlinear system.This structure can not only reduce the conservatism of the outer loop controller design, but also is convenient for engineering implementation. Simulation and flight validation results show that the proposed control scheme is effective.

Secrecy Capacity Maximization for a UAV-Assisted MEC System

Unmanned aerial vehicle(UAV)communication has attracted wide attentions in the mobile edge computing(MEC)system owing to its high-flexibility and simple operation auxiliary communication mode.Users can offload computing tasks to UAVs,which serves as edge nodes.Meanwhile,UAVs forward the tasks onto a cloud center or base station for processing,thereby shortening the implementation time of tasks.Nevertheless,the offloading links of an UAV-assisted MEC system adopt a radio broadcasting mode.Several eavesdroppers might be present in the environment to eavesdrop the data sent by users and UAVs,thereby causing significant effects on the secrecy performance.An optimized iterative algorithm is proposed in this paper to realize the maximum secrecy capacity of the MEC system and further improve the secrecy performance of an UAV-assisted MEC system and assure secrecy transmit.By doing so,the secrecy transmit problems of the two-staged offloading model of the UAV-assisted MEC system are solved.The maximum secrecy capacity of the system is obtained through joint optimization of the UAV positions,transmit power of the UAV,task offloading ratio,and allocation of offloading users considering the limited time and energy of an UAV.Simulation results demonstrate that the proposed iterative algorithm can effectively improve the secrecy capacity of the system.

Security Enhancement of UAV Swarm Enabled Relaying Systems with Joint Beamforming and Resource Allocation

The high mobility of unmanned aerial vehicles(UAVs)could bring abundant degrees of freedom for the design of wireless communication systems,which results in that UAVs,especially UAV swarm,have attracted considerable attention.This paper considers a UAV Swarm enabled relaying communication system,where multiple UAV relays are organized via coordinated multiple points(CoMP)as a UAV swarm to enhance physical layer security of the system in the presence of an eavesdropper.In order to maximize achievable secrecy rate of downlink,we jointly optimize the beamforming vector of the virtual array shaped by the UAV swarm and bandwidth allocation on it for receiving and forwarding,and both amplify-and-forward(AF)and decode-andforward(DF)protocols are considered on the UAV swarm.Due to the non-convexity of the joint optimization problem,we propose an alternating optimization(AO)algorithm to decompose it into two subproblems utilizing block coordinate descent technique,then each subproblem is solved by successive convex optimization method.Simulation results demonstrate that DF has competitive performance advantage compared with AF and the superiority of the proposed secure transmission strategy with optimal beamforming and bandwidth allocation compared with benchmark strategies.

Periodic event-triggered formation control for multi-UAV systems with collision avoidance

In this paper,periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles(UAVs).Firstly,based on the Artificial Potential Field(APF)method,a novel sliding manifold is proposed for controller design,which can solve the problem of collision avoidance.Then,the event-triggered strategy is applied to the distributed formation control of multi-UAV systems,where the evaluation of the event condition is continuous.In addition,the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound.Next,a periodic event-triggered mechanism is developed for formation control based on the continuous eventtriggered mechanism.The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors,which can reduce the control cost.The stability of the control system is proved by the Lyapunov function method.Finally,some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol.

An Edge-Boxes-Based Intruder Detection Algorithm for UAV Sense and Avoid System

With the great development of unmanned aircraft system(UAS)over the last decade,sense and avoid(SAA)system has been a crucial technology for integrating unmanned aircraft vehicle(UAV)into national airspace with reliable and safe operations.This paper mainly focuses on intruder detection for SAA system.A robust algorithm based on the combination of edge-boxes and spatial pyramid matching using sparse coding(sc-SPM)is presented.The algorithm is composed of three stages.First,edge-boxes method is adopted to obtain a large number of proposals;Second,the optimization program is presented to obtain intruder area-of-interest(ROI)regions;Third,sc-SPM is employed for feature representation of ROI regions and support vector machines(SVM)is adopted to detect the intruder.The algorithm is evaluated under different weather conditions.The recall reaches to 0.95 in dawn and sunny weather and 0.9 in cloudy weather.The experimental results indicate that the intruder detection algorithm is effective and robust with various weather under complex background.

CNN Based Automated Weed Detection System Using UAV Imagery

The problem of weeds in crops is a natural problem for farmers.Machine Learning(ML),Deep Learning(DL),and Unmanned Aerial Vehicles(UAV)are among the advanced technologies that should be used in order to reduce the use of pesticides while also protecting the environment and ensuring the safety of crops.Deep Learning-based crop and weed identification systems have the potential to save money while also reducing environmental stress.The accuracy of ML/DL models has been proven to be restricted in the past due to a variety of factors,including the selection of an efficient wavelength,spatial resolution,and the selection and tuning of hyperparameters.The purpose of the current research is to develop a new automated weed detecting system that uses Convolution Neural Network(CNN)classification for a real dataset of 4400 UAV pictures with 15336 segments.Snapshots were used to choose the optimal parameters for the proposed CNN LVQ model.The soil class achieved the user accuracy of 100%with the proposed CNN LVQ model,followed by soybean(99.79%),grass(98.58%),and broadleaf(98.32%).The developed CNN LVQ model showed an overall accuracy of 99.44%after rigorous hyperparameter tuning for weed detection,significantly higher than previously reported studies.

Robust Tracking Control of Uncertain MIMO Nonlinear Systems with Application to UAVs

In this paper, we consider the robust adaptive tracking control of uncertain multi-input and multi-output (MIMO) nonlinear systems with input saturation and unknown external disturbance. The nonlinear disturbance observer (NDO) is employed to tackle the system uncertainty as well as the external disturbance. To handle the input saturation, an auxiliary system is constructed as a saturation compensator. By using the backstepping technique and the dynamic surface method, a robust adaptive tracking control scheme is developed. The closed-loop system is proved to be uniformly ultimately bounded thorough Lyapunov stability analysis. Simulation results with application to an unmanned aerial vehicle (UAV) demonstrate the effectiveness of the proposed robust control scheme. © 2014 Chinese Association of Automation.

Pigeon-Inspired Circular Formation Control for Multi-UAV System with Limited Target Information

The problem of cooperative circular formation with limited target information for multiple Unmanned Aerial Vehicle(UAV)system is addressed in this paper.A pigeon-inspired circular formation control method is proposed to form the desired circular distribution in a plane based on the intelligent pigeon behavior during hovering.To reach the goal of prescribed radius and angular distribution,the controller is designed consisting of a circular movement part and a formation distribution part.Therein,the circular movement part is designed to make each UAV rotate around the speci-ed circle at the same angular speed only using the relative position between the UAV and the target.The formation distribution part could adjust the angular distance between each UAV and its neighbors with the jointly connected network to reduce communication cost.To smooth the speed variation,nonlinear PID-type method is delivered throughout the evolution of the system.The convergence analysis of the proposed control protocol is presented using Lyapunov theory and graph tools.The e®ectiveness of the proposed control strategies is demonstrated through numerical simulations.