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 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.

The Use of Drones in Forestry

Recently, drones have found applicability in a variety of study fields, one of these being forestry, where an increasing interest is given to this segment of technology, especially due to the high-resolution data that can be collected flexibly in a short time and at a relatively low price. Also, drones have an important role in filling the gaps of common data collected using manned aircraft or satellite remote sensing, while having many advantages both in research and in various practical applications particularly in forestry as well as in land use in general. This paper aims to briefly describe the different approaches of applications of UAVs （Unmanned Aircraft Vehicles） in forestry, such as forest mapping, forest management planning, canopy height model creation or mapping forest gaps. These approaches have great potential in the near future applications and their quick implementation in a variety of situations is desirable for the sustainable management of forests.

A mobility compensation method for drones in SG-eloT

In order to achieve the specific goal of a smart grid,the concept of electricity Internet of Things(eloT)has been proposed to assist the monitoring and inspection of power transmission line state and optimize the asset utilization.The long power transmission line and the complex field operation environment urge the introduction of drones into the eloT for fast power transmission line inspection,data collection from sensors for further big data analysis,adaptive control of power line voltage,etc.Additionally,drones can also act as a central communication control or relay point to serve the data exchange among sensors,drones and power transmission line maintenance personnel in the scenario where the conventional mobile communication service is not available.However,the fast mobility of drones may affect the signal transmission and position estimation performance,which may further deteriorate the networking performance.In order to solve this problem,a mobility compensation method is proposed,which includes the steps of frequency offset estimation and relative velocity calculation.Through the Monte Carlo simulations,the proposed algorithm shows favorable gains compared with the conventional ones.

Intelligent Optimization-Based Clustering with Encryption Technique for Internet of Drones Environment

The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst drones.While drones are naturally mobile,it undergoes frequent topological changes.Such alterations in the topology cause route election,stability,and scalability problems in IoD.Encryption is considered as an effective method to transmit the images in IoD environment.The current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)environment.The key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by drones.The presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction technique.In addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD environment.This process enables the secure transmission of images to the ground station.In order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different aspects.The comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.

Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time applications.As several applications comprise IoD in real time environment,significant interest has been received by research communications.Since IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD environment.This article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD environment.The major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD environment.To do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible format.In addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate features.Moreover,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of FFA.The performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

Swiss Innovation Week 2019:Home of Drones,the New High Flyer of Swiss Industry

Following the successful Swiss Innovation Week(SIW)held in July 2018,the Embassy of Switzerland in China launches its 2nd edition from 12 to 14of June 2019.With Swiss drones as the brand-new theme,Switzerland’s drone ecosystem and innovation in the field of flying robots were present.According to various rankings,Switzerland is one of the most innovative countrie s in the world and also one of the most competitive co untries.

Objects Detection and Recognition System Using Artificial Neural Networks and Drones

The paper presents the digital image objects detection and recognition system using artificial neural networks and drones. It contains description based on the example of person identification system where face is the key of object processing. It describes the structure of this system and components of the learning sub-system as well as the processing sub-system （detection, recognition）. It consists of the description and examples of learning and processing algorithms and applied technologies. The results of calculations of efficiency and speed of each algorithm are presented in the table and appropriate characteristics. The article also describes the possibilities of further system developments.

Detection and Classification on Amateur Drones Based on Cepstrum of Radio Frequency Signal

As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.

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.

Loader and Tester Swarming Drones for Cellular PhoneNetwork Loading and Field Test: Non-stochasticParticle Swarm Optimization

Cellular network operators have problems to test their network without affecting their user experience. Testingnetwork performance in a loaded situation is a challenge for the network operator because network performance differswhen it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to loadthe cellular network and scan/test the network performance more realistically. Besides, manual swarming dronenavigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to bedeployed on swarming drone to find the regions where there are performance issues. Swarming drone communicationshelps to deploy the particle swarm optimization (PSO) method on them. Loading and testing swarm separation help tohave almost non-stochastic received signal level as an objective function. Moreover, there are some situations that morethan one network parameter should be used to find a problematic region in the cellular network. It is also proposed toapply multi-objective PSO to find more multi-parameter network optimization at the same time.

Evaluating the Use of Drones in the Area of Transportation/Construction

Drones are proving out as a valuable tool and growing quickly in the world of technological advances.The applications of these vehicles are spreading widely in the areas of remote sensing,real time monitoring,goods delivery,security,defense,surveillance,infrastructure inspection.Although,the intent behind creating this tool was remote sensing.Smart drones will be the next big innovation and modification,which would have much wider applications especially in the field of infrastructure where it can reduce risks and lower costs.Current direct evaluation techniques are tedious,and the information caught is frequently not led in a precise manner with the areas tested not being geographically correct and the resulting reports being delivered past the point of no return.These were the reasons,which have increased the demand and usage of unmanned vehicles.In this research paper,we present critical review of main advancements of Drones in the area of transportation and agriculture.We present all the research related to civil applications in those areas and challenges including traffic monitoring,Bridge condition assessment,Roadway asset detection and many other applications related to infrastructure inspection enhancement.The paper also contributes with a discussion on the opportunities,which are opened,and the challenges that need to be addressed.Findings from the case studies,it is reported that around 25%of the bridges in united states are deficient and need continuous monitoring for enhancements to prevent any hazard.Unmanned vehicles could be a great help in monitoring these bridges and other important components of transportation,which can efficiently minimize the cost as well as the time spent on inspection for each of this component,as manual inspection requires labor and time which would be subsequently reduced by incorporating the usage of drones in the area of transportation.

Particle Swarm Optimization Bat Algorithm Path Automatically Planning Research for Police Drones in Hilly Cities

Mountain cities are complex asymmetric dynamic network architectures,and the flight of UAVs in this environment is subject to various constraints,while efficiency is a crucial factor in the trajectory planning of police UAVs,which need to maintain high efficiency and safe flight paths between their starting and ending points,but the traditional trajectory planning method cannot meet the requirements of rapid maneuvering of police UAVs.To achieve this,a 3D terrain map is built,an objective function is established for the flight cost in the UAV trajectory planning process,and a planning algorithm called particle swarm optimization bat algorithm(PSOBA)is proposed.PSOBA combines the characteristics of the bat algorithm(BA)and the particle swarm optimization algorithm(PSO)to improve population diversity and resolve the delayed convergence issue in the last phases of BA.Simulation results show that PSOBA is more effective than BA,with a search time for the best solution that is approximately 20.43%shorter and a convergence value of the objective function that is approximately 38%smaller.PSOBA is also able to plan a quicker,shorter,and safer flight path compared to other trail planning algorithms that enhance the bat algorithm.These findings suggest that PSOBA is a powerful algorithm with potential application value in UAV trajectory planning control in the mobile intelligence era.Contribute to the service of public social security.

Optimization and analysis of composite sandwich box beam for solar drones

Solar drones have garnered considerably research attention in recent years due to their continuous cruising capability,and the feasibility of design schemes is sensitive to the weight of structure.Sandwich box beam composed of carbon fiber and polymethacrylimide(PMI)foam is conducive to realize the lightweight of structure.In this study,a two-stage optimization design methodology for sandwich box beam is proposed.This methodology is primarily based on a low-order analytical method for evaluating stress/deflection and the linear buckling analysis method combined with experimental correction factor for predicting the buckling eigenvalues.Subsequently,a case study was conducted using an 18-m wingspan solar drone,where the results of mechanical test verified the optimization results.For validating the use of sandwich box beam in solar drones of other scales,additional analysis was conducted based on three aspects:(A)effects of stiffness and stability constraints on the design of sandwich box beam;(B)crucial role of the weight of foam inter layer and application scope of sandwich box beam;(C)best method to improve the buckling eigenvalue of sandwich box beam.Overall,the methodology and general rules presented in this paper can support the design of light wing beam for solar drones.

Landing route planning method for micro drones based on hybrid optimization algorithm

Aiming at the obstacle avoidance trajectory planning problem in the landing process of the micro drone,this paper proposes a swarm optimization algorithm that combines the dragonfly optimization method and the differential evolution method.The orthogonal learning mechanism is adopted to realize the adaptive switch between the two algorithms.In the process of landing route planning,the planning plane is first obtained by making the gliding plane tangent to the obstacle.In the planning plane,the projection of obstacle is transformed into multiple unreachable line segments.By designing an optimization model,the 3D landing route planning problem is transformed into a 2D obstacle avoidance route optimization problem.Taking the shortest route as the optimization objective,the penalty factor is introduced into the cost function to avoid the intersection of the landing route and obstacle.During the optimization process,through the orthogonal learning of the intermediate iterative results,the hybrid algorithm can adaptively select the next iterative algorithm,so it can give full play to the respective advantages of the two algorithms.The optimization results show that,compared with the single optimization algorithm,the hybrid optimization algorithm proposed in this paper can better solve the problem of landing route planning for micro-small UAVs.

DRONES FOR YOUR WECHAT

Continuing its bid to occupy every inch of Chinese internet life with its messaging app,We Chat,Tencent released a consumer quadcopter drone,called“Ying”,at the end of October.Made in partnership with Qualcomm and Chinese drone manufacturer Zero Tech,Ying allows its users to capture videos at 4K resolution and can be synchronized to your phone to be broadcast live to your social network through We Chat.It also

Automated optical inspection of FAST’s reflector surface using drones and computer vision

The Five-hundred-meter Aperture Spherical radio Telescope(FAST)is the world’s largest single-dish radio telescope.Its large reflecting surface achieves unprecedented sensitivity but is prone to damage,such as dents and holes,caused by naturally-occurring falling objects.Hence,the timely and accurate detection of surface defects is crucial for FAST’s stable operation.Conventional manual inspection involves human inspectors climbing up and examining the large surface visually,a time-consuming and potentially unreliable process.To accelerate the inspection process and increase its accuracy,this work makes the first step towards automating the inspection of FAST by integrating deep-learning techniques with drone technology.First,a drone flies over the surface along a predetermined route.Since surface defects significantly vary in scale and show high inter-class similarity,directly applying existing deep detectors to detect defects on the drone imagery is highly prone to missing and misidentifying defects.As a remedy,we introduce cross-fusion,a dedicated plug-in operation for deep detectors that enables the adaptive fusion of multi-level features in a point-wise selective fashion,depending on local defect patterns.Consequently,strong semantics and fine-grained details are dynamically fused at different positions to support the accurate detection of defects of various scales and types.Our AI-powered drone-based automated inspection is time-efficient,reliable,and has good accessibility,which guarantees the long-term and stable operation of FAST.

A practical approach with drones,smartphones,and tracking tags for potential real-time animal tracking

Drones are increasingly used for fauna monitoring and wildlife tracking;however,their application for wildlife tracking is restricted by developing such systems.Here we explore the potential of drones for wildlife tracking using an off-the-shelf system that is easy to use by non-specialists consisting of a multirotor drone,smartphones,and commercial tracking devices via Bluetooth and Ultra-Wide Band(UWB).We present the system configuration,explore the operational parameters that can affect detection capabilities,and test the effectiveness of the system for locating targets by simulating target animals in savanna and forest environments.The self-contained tracking system was built without hardware or software customization.In 40 tracking flights carried out in the Brazilian Cerrado,we obtained a detection rate of 90%in savanna and 40%in forest areas.Tests for targets in movement(N=20),the detection rates were 90%in the savanna and 30%in the forest areas.The spatial accuracy obtained by the system was 14.61 m,being significantly more accurate in savanna(x=10.53)than in forest areas(x=13.06).This approach to wildlife tracking facilitates the use of drones by non-specialists at an affordable cost for conservation projects with limited resources.The reduced size of the tags,the long battery life,and the iower cost compared to GPS-tags open up a range of opportunities for animal tracking.

A Review on Cooperative Robotic Arms with Mobile or Drones Bases

This review paper focuses on cooperative robotic arms with mobile or drone bases performing cooperative tasks. This is because cooperative robots are often used as risk-reduction tools to human life. For example, they are used to explore dangerous places such as minefields and disarm explosives. Drones can be used to perform tasks such as aerial photography, military and defense missions,agricultural surveys, etc. The bases of the cooperative robotic arms can be stationary, mobile(ground), or drones. Cooperative manipulators allow faster performance of assigned tasks because of the available "extra hand". Furthermore, a mobile base increases the reachable ground workspace of cooperative manipulators while a drone base drastically increases this workspace to include the aerial space.The papers in this review are chosen to extensively cover a wide variety of cooperative manipulation tasks and industries that use them.In cooperative manipulation, avoiding self-collision is one of the most important tasks to be performed. In addition, path planning and formation control can be challenging because of the increased number of components to be coordinated.