A novel framework to intercept GPS-denied,bomb-carrying,nonmilitary,kamikaze drones:Towards protecting critical infrastructures

Protection of urban critical infrastructures(CIs)from GPS-denied,bomb-carrying kamikaze drones(G-BKDs)is very challenging.Previous approaches based on drone jamming,spoofing,communication interruption and hijacking cannot be applied in the case under examination,since G-B-KDs are uncontrolled.On the other hand,drone capturing schemes and electromagnetic pulse(EMP)weapons seem to be effective.However,again,existing approaches present various limitations,while most of them do not examine the case of G-B-KDs.This paper,focuses on the aforementioned under-researched field,where the G-B-KD is confronted by two defensive drones.The first neutralizes and captures the kamikaze drone,while the second captures the bomb.Both defensive drones are equipped with a net-gun and an innovative algorithm,which,among others,estimates the locations of interception,using a real-world trajectory model.Additionally,one of the defensive drones is also equipped with an EMP weapon to damage the electronics equipment of the kamikaze drone and reduce the capturing time and the overall risk.Extensive simulated experiments and comparisons to state-of-art methods,reveal the advantages and limitations of the proposed approach.More specifically,compared to state-of-art,the proposed approach improves:(a)time to neutralize the target by at least 6.89%,(b)maximum number of missions by at least 1.27%and(c)total cost by at least 5.15%.

A Shuffling-Steganography Algorithm to Protect Data of Drone Applications

In Saudi Arabia,drones are increasingly used in different sensitive domains like military,health,and agriculture to name a few.Typically,drone cameras capture aerial images of objects and convert them into crucial data,alongside collecting data from distributed sensors supplemented by location data.The interception of the data sent from the drone to the station can lead to substantial threats.To address this issue,highly confidential protection methods must be employed.This paper introduces a novel steganography approach called the Shuffling Steganography Approach(SSA).SSA encompasses five fundamental stages and three proposed algorithms,designed to enhance security through strategic encryption and data hiding techniques.Notably,this method introduces advanced resistance to brute force attacks by employing predefined patterns across a wide array of images,complicating unauthorized access.The initial stage involves encryption,dividing,and disassembling the encrypted data.A small portion of the encrypted data is concealed within the text(Algorithm 1)in the third stage.Subsequently,the parts are merged and mixed(Algorithm 2),and finally,the composed text is hidden within an image(Algorithm 3).Through meticulous investigation and comparative analysis with existing methodologies,the proposed approach demonstrates superiority across various pertinent criteria,including robustness,secret message size capacity,resistance to multiple attacks,and multilingual support.

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

基于Raspberry Pi及Drone Kit的无人机飞行控制教学实践应用

基于开源硬件Raspberry Pi,Pixhawk及开源软件Drone Kit,设计了高校无人机飞行控制教学实践课程。课程中使用Raspberry Pi作为无人机的机载计算机与Pixhawk飞控芯片协同工作,通过使用MAVLink协议通信。学生在实践操作中需要掌握硬件各接口的功能属性进行无人机组装,在调测环境下,基于Drone Kit开发无人机飞行控制程序。所提的实践课程可以提高学生的动手实践能力,新环境下的创新应用能力能加深对飞行控制技术的理论理解。

Drone Usage in Civil Engineering—A Case Study of the Pristina-Gjilan Highway

The use of drones in construction engineering has gained increasing attention in recent years due to its potential to revolutionize the industry. Drones, offer the ability to capture high-resolution aerial imagery and collect data that was previously difficult or impossible to obtain. The integration drones in construction engineering presents opportunities for accurate data collection, analysis and visualization, which can improve decision-making processes and improve project outcomes. For example, drones equipped with GIS technology can be used to capture high-resolution aerial images of construction sites, allowing engineers to monitor progress, identify potential issues, and make informed adjustments as needed. By harnessing drones, civil engineers in the civil engineering field can potentially optimize project planning, design and execution while minimizing risks and costs. The work of this topic examines the case of the use of Drones combined with GIS in construction engineering. During this study, aerial photography of a certain segment of the Pristina-Gjilan Highway was taken. The results generated by the processing of aerial photos have been compared with the project. However, further research is needed to fully understand the capabilities and limitations of these technologies in this specific context, as well as to explore any potential challenges and barriers to their widespread adoption.

DroneRFa:用于侦测低空无人机的大规模无人机射频信号数据集

为研究与发展反无人机检测识别技术,该文公开了一个名为DroneRFa的大规模无人机射频信号数据集。该数据集使用软件无线电设备探测无人机与遥控器相互通信的射频信号,包含城市户外场景下运动无人机信号9类、城市室内场景下信号15类以及背景参照信号1类。每类数据有不少于12个片段,每个片段包含1亿个以上的采样点。数据采集覆盖了3个ISM无线电频段,记录无人机多频通信的真实活动。该数据集具有详细的无人机户外飞行距离和工作频段标注,以前缀字符结合二进制编码的形式方便用户灵活访问所需数据。此外,该文提供了基于频谱可视统计特征和基于深度学习表征的两种无人机识别方案,以验证数据集的可靠和有效性。

Efficient Remote Identification for Drone Swarms

With the advancement of unmanned aerial vehicle(UAV)technology,the market for drones and the cooperation of many drones are expanding.Drone swarms move together in multiple regions to perform their tasks.A Ground Control Server(GCS)located in each region identifies drone swarmmembers to prevent unauthorized drones from trespassing.Studies on drone identification have been actively conducted,but existing studies did not consider multiple drone identification environments.Thus,developing a secure and effective identification mechanism for drone swarms is necessary.We suggested a novel approach for the remote identification of drone swarms.For an efficient identification process between the drone swarm and the GCS,each Reader drone in the region collects the identification information of the drone swarmand submits it to the GCS for verification.The proposed identification protocol reduces the verification time for a drone swarm by utilizing batch verification to verify numerous drones in a drone swarmsimultaneously.To prove the security and correctness of the proposed protocol,we conducted a formal security verification using ProVerif,an automatic cryptographic protocol verifier.We also implemented a non-flying drone swarmprototype usingmultiple Raspberry Pis to evaluate the proposed protocol’s computational overhead and effectiveness.We showed simulation results regarding various drone simulation scenarios.

基于YOLOX-drone的反无人机系统抗遮挡目标检测算法

为解决现实场景下无人机目标被部分遮挡,导致不易检测问题,本文提出了基于YOLOX-S改进的反无人机系统目标检测算法YOLOX-drone.首先,建立无人机图像数据集;其次,搭建YOLOX-S目标检测网络,在此基础上引入坐标注意力机制,来增强无人机的目标图像显著度,突出有用特征抑制无用特征;然后,再去除特征融合层中自下而上的路径增强结构,减少网络复杂度,并设计了自适应特征融合网络结构,增强有用特征的表达能力,抑制干扰,提升检测精度.在DUT-AntiUAV数据集上的测试结果表明:YOLOX-drone与YOLOX-S、YOLOv5-S和YOLOX-tiny相比,平均准确率(IoU=0.5)提升了3.2%、4.7%和10.1%;在自建的无人机图像数据集上的测试结果表明:YOLOX-drone与原YOLOX-S目标检测模型相比,在无遮挡、一般遮挡、严重遮挡情况下,平均准确率(IoU=0.5)分别提高了2.4%、2.1%和6.4%,验证了改进的算法具有良好的抗遮挡检测能力.

A tree detection method based on trunk point cloud section in dense plantation forest using drone Li DAR data

Single-tree detection is one of the main research topics in quantifying the structural properties of forests. Drone Li DAR systems and terrestrial laser scanning systems produce high-density point clouds that offer great promise for forest inventories in limited areas. However, most studies have focused on the upper canopy layer and neglected the lower forest structure. This paper describes an innovative tree detection method using drone Li DAR data from a new perspective of the under-canopy structure. This method relies on trunk point clouds, with undercanopy sections split into heights ranging from 1 to 7 m, which were processed and compared, to determine a suitable height threshold to detect trees. The method was tested in a dense cedar plantation forest in the Aichi Prefecture, Japan, which has a stem density of 1140 stems·ha^(-1) and an average tree age of 42 years. Dense point cloud data were generated from the drone Li DAR system and terrestrial laser scanning with an average point density of 5000 and 6500 points·m^(-2), respectively. Tree detection was achieved by drawing point-cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk-section-based method significantly reduces the difficulty of tree detection in dense plantation forests with high accuracy(F1-Score=0.9395). This method can be extended to different forest scenarios or conditions by changing section parameters.

Classification of birds and drones by exploiting periodical motions in Doppler spectrum series

With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from highresolution Doppler spectrum sequences(DSSs) for classification.This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory(LSTM) is used to solve the time series classification.Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.

A Drone-Based Blood Donation Approach Using an Ant Colony Optimization Algorithm

This article presents an optimized approach of mathematical techniques in themedical domain by manoeuvring the phenomenon of ant colony optimization algorithm(also known as ACO).A complete graph of blood banks and a path that covers all the blood banks without repeating any link is required by applying the Travelling Salesman Problem(often TSP).The wide use promises to accelerate and offers the opportunity to cultivate health care,particularly in remote or unmerited environments by shrinking lab testing reversal times,empowering just-in-time lifesaving medical supply.

MF2-DMTD: A Formalism and Game-Based Reasoning Framework for Optimized Drone-Type Moving Target Defense

Moving-target-defense(MTD)fundamentally avoids an illegal initial compromise by asymmetrically increasing the uncertainty as the attack surface of the observable defender changes depending on spatial-temporal mutations.However,the existing naive MTD studies were conducted focusing only on wired network mutations.And these cases have also been no formal research on wireless aircraft domains with attributes that are extremely unfavorable to embedded system operations,such as hostility,mobility,and dependency.Therefore,to solve these conceptual limitations,this study proposes normalized drone-type MTD that maximizes defender superiority by mutating the unique fingerprints of wireless drones and that optimizes the period-based mutation principle to adaptively secure the sustainability of drone operations.In addition,this study also specifies MF2-DMTD(model-checkingbased formal framework for drone-type MTD),a formal framework that adopts model-checking and zero-sum game,for attack-defense simulation and performance evaluation of drone-type MTD.Subsequently,by applying the proposed models,the optimization of deceptive defense performance of drone-type MTD for each mutation period also additionally achieves through mixed-integer quadratic constrained programming(MIQCP)and multiobjective optimization-based Pareto frontier.As a result,the optimal mutation cycles in drone-type MTD were derived as(65,120,85)for each control-mobility,telecommunication,and payload component configured inside the drone.And the optimal MTD cycles for each swarming cluster,ground control station(GCS),and zone service provider(ZSP)deployed outside the drone were also additionally calculated as(70,60,85),respectively.To the best of these authors’knowledge,this study is the first to calculate the deceptive efficiency and functional continuity of the MTD against drones and to normalize the trade-off according to a sensitivity analysis with the optimum.

Drone for Dynamic Monitoring and Tracking with Intelligent Image Analysis

Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the entire environ-ment.Whenfinding a suspicious person,the tracked object cannot be locked in time for tracking.This research replaces the traditionalfixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person.This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.In this article,we proposed a TIMT(The Intel-ligent Monitoring and Tracking)algorithm which can make the drone have smart surveillance and tracking capabilities.It combined with Artificial Intelligent(AI)face recognition technology and the OpenPose which is able to monitor the phy-sical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote con-trol interface of the drone.This system is highly agile and could be adjusted immediately to any angle and screen that we monitor.Therefore,the system couldfind abnormal conditions immediately and track and monitor them automatically.That is the system can immediately detect when someone invades the home or community,and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved.Experimental results show that the intelligent monitoring and tracking drone sys-tem has an excellent performance,which not only dramatically reduces the num-ber of monitors and the required equipment but also achieves perfect monitoring and tracking.

Geographic Drone-based Route Optimization Approach for Emergency Area Ad-Hoc Network

Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.

Platform Strategies of the Chinese Commercial Drone Manufacturer:A Theoretical and Empirical Study of Ecosystem Development

From the perspective of the business ecosystem,this paper analyzes the competitive advantage and platform strategy of Da-Jiang Innovations Science and Technology Co.,Ltd.(DJI),a Chinese commercial drone manufacturer that is currently leading the global commercial drone industry.DJI was established in 2006 and developed the industry’s first core components such as drone control system.DJI released its“Phantom”in the United States in 2013 and occupied the global commercial drone market accounting for 70%in a short period of time.Its market share has maintained its superiority till present.During the inflection transition from the formation of a new ecosystem to expansion,DJI has defended and strengthened its core technology through a strong containment strategic action of competing with GoPro;therefore,DJI has obtained its hub position of multiple markets with bargaining power.In addition,DJI has entered the surrounding markets of corporate market from the general consumer market,and instilled its own product standards&design standards(reference design).Furthermore,it has stimulated and revitalized coexisting companies,individual&corporate customers for expanding the ecosystem of drone industry.

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and surveillance.For improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with others.In addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV networks.Besides,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)process.The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

Drone remote sensing for forestry research and practices

Drones of various shapes, sizes, and functionalities have emerged over the past few decades, and their civilian applications are becoming increasingly appealing. Flexible, low-cost, and high-resolution remote sensing systems that use drones as platforms are important for filling data gaps and supplementing the capabilities of crewed/manned aircraft and satellite remote sensing systems. Here, we refer to this growing remote sensing ini- tiative as drone remote sensing and explain its unique advantages in forestry research and practices. Furthermore, we summarize the various approaches of drone remote sensing to surveying forests, mapping canopy gaps, mea- suring forest canopy height, tracking forest wildfires, and supporting intensive forest management. The benefits of drone remote sensing include low material and operational costs, flexible control of spatial and temporal resolution, high-intensity data collection, and the absence of risk to crews. The current forestry applications of drone remote sensing are still at an experimental stage, but they are expected to expand rapidly. To better guide the development of drone remote sensing for sustainable forestry, it isimportant to systematically and continuously conduct comparative studies to determine the appropriate drone remote sensing technologies for various forest conditions and/or forestry applications.

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Pine wilt disease(PWD)has recently caused substantial pine tree losses in Republic of Korea.PWD is considered a severe problem due to the importance of pine trees to Korean people,so this problem must be handled appropriately.Previously,we examined the history of PWD and found that it had already spread to some regions of Republic of Korea;these became our study area.Early detection of PWD is required.We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD.Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees.To differentiate healthy pine trees from those with PWD,we produced a land cover(LC)map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods,i.e.,artificial neural network(ANN)and support vector machine(SVM).Furthermore,compared the accuracy of two types of Global Positioning System(GPS)data,collected using drone and hand-held devices,for identifying the locations of trees with PWD.We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD.In Anbi,the SVM had an overall accuracy of 94.13%,which is 6.7%higher than the overall accuracy of the ANN,which was 87.43%.We obtained similar results in Wonchang,for which the accuracy of the SVM and ANN was 86.59%and 79.33%,respectively.In terms of the GPS data,we used two type of hand-held GPS device.GPS device 1 is corrected by referring to the benchmarks sited on both locations,while the GPS device 2 is uncorrected device which used the default setting of the GPS only.The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang.However,in Anbi,we obtained better results from GPS device 2 than from GPS device 1.In Anbi,the error in the data from GPS device 1 was 7.08 m,while that of the GPS device 2 data was 0.14 m.In conclusion,both classifiers can distinguish between healthy trees and those with PWD based on LC data.LC data can also be used for other types of classification.There were some differences between the hand-held and drone GPS datasets from both areas.

Collaborative Spectrum Sensing for Illegal Drone Detection: A Deep Learning-Based Image Classification Perspective

Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.