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.

Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

基于UAV勘察与层次分析法的安阳许家沟露天矿山地质风险评价

矿产资源是社会经济发展的重要基础,在社会发展与生态建设统筹的背景下,矿山地质风险防治与生态环境修复成为矿业研究的焦点。矿山地质风险的分类和量化是矿山地质风险防治和修复的基础,有利于因地制宜制定治理措施。本文基于UAV勘察和层次分析法对安阳许家沟露天矿山群地质风险进行了评价,根据勘察结果总结出边坡崩塌、矿坑坍塌和水土流失3个主要地质风险,各风险层选取4个因素,共12个评价指标,并利用ArcGIS栅格计算模块对选取的12个评价因素进行叠加分析。研究结果显示:高度风险区、显著风险区、一般风险区和稍有风险区的面积分别为13500 m^(2)、39000 m^(2)、26750 m^(2)、45750 m^(2),分别占研究区总面积的10.8%、31.2%、21.4%、36.6%;综合矿山地质风险排序为河西胜利II区<豫隆I区<豫安III区;从豫隆I区到豫安III区,随着研究子区面积的减少,边坡崩塌风险贡献率逐渐降低,矿坑坍塌风险和水土流失风险逐渐升高。研究结果表明,边坡崩塌评分与矿山地质评分呈现双峰态势,而矿坑坍塌风险和水土流失风险呈单峰态势。本文提出危岩清除、矿渣回填、边坡修整、客土恢复林地和耕地、养护治理等治理措施,从而降低矿山地质风险,修复矿山生态。

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

基于UAV成像技术的‘高原红’海棠花期物候监测研究

以‘高原红’海棠(Malus‘Prairifire’)为试材,基于UAV成像技术采集物候期内图像样本并提取色彩参数值(CIE L^(*)a^(*)b^(*)),计算不同采样点及不同物候期的色差值(△E),通过对比△E的变化情况实现海棠物候的动态监测,为实时掌握海棠物候动态变化情况,适时作出调整策略提供技术参考,同时也为开展花期物候调控相关研究提供参考依据。结果表明:1)利用Photoshop提取航拍图像色彩参数,并基于物候色彩参数进行聚类分析,将其物候划分为萌芽期、展叶期、蕾期、初花期、盛花期、末花期、落叶期、结果期、休眠期,建立了‘高原红’海棠物候色彩参数库;2)计算‘高原红’海棠各物候期内色差△E_(1)及不同物候期间色差△E_(2)范围,并基于各物候期内最大色差△E_(1max)′及不同物候期间最小色差△E_(2min)′,建立了基于色差参数的物候判别方法;3)随机提取了2个日期(3月22日、4月10日)的‘高原红’海棠物候图像色彩参数,通过聚类分析判定2个日期物候图分别处于蕾期和盛花期,结果与实际相符;2个日期物候图间色差(△E_(2)′=21.8)大于蕾期至初花期最小色差(△E_(2min)=9.6),表明此时物候期跨度较大,结果与实际相符。综上,该方法可便捷高效判断海棠花期等物候期情况。

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

UAV倾斜摄影在革命旧址保护中的应用

针对革命旧址保护工作的严峻性,以及在获取革命旧址测区数据中传统测绘方法工作效率低、难以全面获取数据和无法获得高精度三维立体数据等问题,基于UAV倾斜摄影测量技术,以红军长征旧址为研究对象,通过外业数据采集,利用SIFT算法提取匹配影像特征点,再利用SfM-MVS算法构建三维立体场景,采集三维立体场景中的检查点数据。结果表明,三维立体场景满足大比例尺数字地形图成图规范要求;分析线元素精度可知,三维立体场景数据测量结果可达厘米级,满足革命旧址保护中对数据精度的要求;在革命旧址保护工作中还可提供多种基础数据,为今后革命旧址保护工作提供了一种高效获取高精度数据的参考方法。

基于UAV-SfM数字模型的滇中环状构造地表与地形特征分析

地表特征与自然灾害密切相关,对维护生态环境和深入了解地表演化过程及地质构造特征具有重要作用。通过无人机(Unmanned aerial vehicle,UAV)航测和运动恢复结构(Structure from motion,SfM)技术构建的高空间分辨率数字模型,在滇中环状构造地貌开展地表覆盖信息和地形特征的分布关系分析。结果表明:在裸岩、裸土和植被混合区域,从定性和定量分析中发现DeepLabv3+算法相比于RF算法在试验区地表覆盖信息提取中有较好的提取效果。点云经滤波得到地面点,选择交叉验证中均值误差和均方根误差最小的Kriging算法构建分辨率0.1 m的数字高程模型(Digital elevation model,DEM),解译一阶坡面、二阶坡面和复合坡面的多种地形因子,根据相关性分析选取6种地形因子构建了综合地形分析模型(Comprehensive terrain analysis model,CTAM)。经过分析地表覆盖信息中覆盖面积最大裸土、植被与地形的联系,CTAM中每个等级像元数量与总像元数量百分比中,Ⅱ级占比最高,为28.87%,Ⅰ、Ⅳ、Ⅴ占比分别为18.39%、13.82%和17.29%。UAV-SfM技术能有效捕捉环状构造表面特征,可为该地区地质研究与资源管理提供技术手段和科学依据。

RIS辅助的UAV与用户协同缓存策略

研究了智能反射面(RIS)和缓存辅助的无人机(UAV)中继通信系统方案,通过在UAV与用户之间搭建RIS反射信号,改善信道环境;在UAV与用户设备上部署缓存设备,预先存储热点内容,减轻无线回程链路的压力;以最大化用户服务成功概率为优化目标,建立缓存容量受限约束下的UAV与用户协同缓存放置策略优化模型,针对该非线性连续非凸约束优化问题,提出基于鲸鱼优化算法(WOA)的求解方法.仿真实验结果表明,使用RIS可以有效降低UAV通信中断概率,基于WOA的UAV与用户协同缓存最优放置策略优于现有其他两种缓存策略,能有效提高缓存命中概率,从而提高用户服务成功概率.

OFDM-UAV中继广播通信系统航迹优化方法

为了提高无人机中继广播通信系统的性能,针对频率选择性衰落信道,考虑基站到无人机以及无人机到用户的全通信链路,提出了基于正交频分复用(OFDM)技术的无人机中继广播通信系统航迹优化方法。首先提出了基于OFDM的无人机中继广播通信传输模型,对单用户节点中断概率和系统平均中断概率进行理论分析并给出近似计算公式;然后以平均中断概率最小化和最大用户中断概率最小化为优化准则,提出无人机的航迹优化方法并分析了各因素对系统中断性能的影响;最后通过计算机仿真,证明了所提航迹优化方法正确有效。研究表明:在多径信道下,基于OFDM的无人机中继广播通信系统能有效克服频率选择性衰落,且译码转发方式下的连通性优于放大转发方式;基于平均中断概率最小化准则得到的系统中断性能优于最大用户中断概率最小化准则。

基于DPBBO算法的智慧云仓UAV盘库作业优化

针对智慧云仓货物信息量大、易出现账物不符等库存管理问题,迫切需要将无人机(unmanned aerial vehicle, UAV)和工业物联网(industrial Internet of things, IIoT)集成起来,为仓储精细化管理提供解决方案。首先,分析盘库作业数据采集与信息交互运行机制,以危险避障和数据采集为约束函数,考虑了UAV在加速、减速、匀速、转角等飞行条件下的能耗差异,并以能耗最低和时间最短为目标函数构造UAV盘库作业数学模型;然后,设计了差分迁移-分段变异生物地理学优化(differential migration-piecewise mutation-biogeography-based optimization, DPBBO)算法对上述模型进行优化解算;最后,进行了仿真实验验证。结果表明:DPBBO算法对解决该盘库作业问题的效果较优,可以提升库存抽检任务的时效性和库存管理的准确性。

基于多密度流聚类的UAV-NOMA用户分簇与功率分配算法

针对无人机(Unmanned Aerial Vehicle,UAV)辅助非正交多址(Non-Orthogonal Multiple Access,NOMA)下行通信系统,提出了最大化和速率的用户动态分簇与功率分配方案.考虑用户服务质量与UAV位置约束,建立了和速率最大化的优化问题.由于目标函数的非凸性,将原问题解耦为三个子问题,分别优化UAV位置部署与用户连接、用户动态分簇、功率分配以提高系统性能.首先,基于K-means算法设计了UAV位置部署与用户连接方案,以减小路损为目的确定UAV最佳部署位置,同时选择其服务的最优用户群;其次,改进多密度流聚类(Multi-Density Stream Clustering, MDSC)算法,提出了单UAV下用户静态与动态分簇方案,静态分簇方案可自适应平衡簇数与簇用户数,并获得较大的簇内用户信道增益差异,动态分簇方案则针对用户移动属性,制定了即时更新策略;最后,使用分式规划(Fractional Programming,FP)二次变换的方法,引入辅助变量将原非凸问题变换为凸问题,交替优化辅助变量与功率分配因子,获得原非凸问题的次优解.仿真结果表明,与其他算法相比,本文分簇方案能获得更大的簇内信道差异与更小的簇内用户数标准差,同时用户系统性能也获得了显著提升.

Detection and analysis of landslide geomorphology based on UAV vertical photogrammetry

High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide distribution.This study proposed a vertical route planning method for unmanned aerial vehicles(UAVs),which could achieve rapid image collection based on strictly calculated route parameters.The effectiveness of this method was verified using a DJI Mavic 2 Pro,obtaining high-resolution landslide images within the Dongchuan debris flow gully,in the Xiaojiang River Basin,Dongchuan District,Yunnan,China.A three-dimensional(3D)model was constructed by the structure-from-motion and multi-view stereo(SfM-MVS).Micro-geomorphic features were analyzed through visual interpretation,geographic information system(GIS),spatial analysis,and mathematical statistics methods.The results demonstrated that the proposed method could obtain comprehensive vertical information on landslides while improving measurement accuracy.The 3D model was constructed using the vertically oriented flight route to achieve centimeter-level accuracy(horizontal accuracy better than 6 cm,elevation accuracy better than 3 cm,and relative accuracy better than 3.5 cm).The UAV technology could further help understand the micro internal spatial and structural characteristics of landslides,facilitating intuitive acquisition of surface details.The slope of landslide clusters ranged from 36°to 72°,with the majority of the slope facing east and southeast.Upper elevation levels were relatively consistent while middle to lower elevation levels gradually decreased from left to right with significant variations in lower elevation levels.During the rainy season,surface runoff was abundant,and steep topography exacerbated changes in surface features.This route method is suitable for unmanned aerial vehicle(UAV)landslide surveys in complex mountainous environments.The geomorphological analysis methods used will provide references for identifying and describing topographic features.

Vulnerability assessment of UAV engine to laser based on improved shotline method

Laser anti-drone technology is entering the sequence of actual combat,and it is necessary to consider the vulnerability of typical functional parts of UAVs.Since the concept of"vulnerability"was proposed,a variety of analysis programs for battlefield targets to traditional weapons have been developed,but a comprehensive assessment methodology for targets'vulnerability to laser is still missing.Based on the shotline method,this paper proposes a method that equates laser beam to shotline array,an efficient vulnerability analysis program of target to laser is established by this method,and the program includes the circuit board and the wire into the vulnerability analysis category,which improves the precision of the vulnerability analysis.Taking the UAV engine part as the target of vulnerability analysis,combine with the"life-death unit method"to calculate the laser penetration rate of various materials of the UAV,and the influence of laser weapon system parameters and striking orientation on the killing probability is quantified after introducing the penetration rate into the vulnerability analysis program.The quantitative analysis method proposed in this paper has certain general expansibility,which can provide a fresh idea for the vulnerability analysis of other targets to laser.

Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data

Glaciers in the Pamir region are experiencing rapid melting and receding due to climate change,which has a significant implication for the Amu Darya river basin.Predominantly,surging glaciers,which undergo unpredictable advances,are potentially leading to the obstruction of high-altitude river channels and also glacial lake outburst floods.decrease of-703.5±30.0 m.There is a substantial increase in the number(from 19 to 75)and area(from 4889.7±0.6 m2 to 15345.5±0.6 m2)of RGS lakes along with supra-glacier ponds based on a comparison of ArcGIS base map in 2011 and high-resolution UAV data in 2023.For M glacier,number of lakes increased from 4 to 22 but the lake area declined from 10715.2±0.6 to 365.6±0.6 m2.It was noted that the largest lake in 2011 with an area of 10406.4±0.6 m2 at the southeastern portion of the glacier was not observed in 2023 due to outburst.Both the glaciers have substantially impacted the river flow(Abdukahor river)by obstructing a significant proportion of river channel in recent years and might cause outburst floods.These findings enhance the understanding of glacier dynamics and their impacts on the surrounding areas,emphasizing the urgent need for continued monitoring and appropriate management strategies,with a specific focus on surging glaciers and the associated risks.

Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.