Integration Of Unmanned Aerial Vehicles Research Articles

Resource Management in UAV Enabled MEC Networks

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem. According to simulation results, both local and offloading schemes give optimal solution.

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements. These future challenges include physical-layer aspects, non-orthogonal multiple access schemes, radical learning paradigms for swarms of UAVs and unmanned surface and underwater vehicles, as well as UAV-aided edge computing and caching.

Moving target detection based on background modeling and frame difference

The integration of Unmanned Aerial Vehicle (UAV) technology with moving target detection has diverse applications in military reconnaissance, space remote sensing, and smart cities. Traditional motion-based target detection algorithms offer fast processing speeds but lack accuracy. Deep learning-based algorithms, while accurate for specific targets only, are complex and not suitable for resource-limited UAV platforms and lack real-time performance. Therefore, this study proposes a real-time moving target detection algorithm for UAV platforms based on traditional frame difference algorithm. The purpose of this algorithm is to improve detection accuracy, which has been hindered by the limitations of traditional algorithms caused by camera shake, background changes, and fast-moving targets. The algorithm involves rough background modeling, background updating during subsequent video image sequences, image morphology processing, and background compensation. Experimental results from multiple sets of UAV-borne video data demonstrate the algorithm's high target detection rate, low false alarm rate, and ability to detect moving targets stably in complex environments. The proposed algorithm achieves a speed of 25 FPS and a detection accuracy of 91.8%, meeting real-time and accurate detection requirements.

Can iPhone/iPad LiDAR data improve canopy height model derived from UAV?

Aerial images resulting from unmanned aerial vehicle (UAV) are widely used to estimate tree height. The filtering method is required to distinguish between ground and off-ground point clouds to generate a canopy height model. However, the filtering method is not always perfect since UAV data cannot penetrate canopies into the forest floor. The release of iPhone/iPad devices with built-in LiDAR sensors enables the more affordable use of LiDAR for forestry study, including the measurement of local topography below forest stands. This study investigates to what extent iPhone/iPad LiDAR can improve the accuracy of canopy height model from the UAV. The integration of UAV and iPhone/iPad LiDAR data managed to increase the accuracy of tree height model with a mean absolute error (MAE) of 2.188 m, compared to UAV data (MAE = 2.446 m). This preliminary study showed the potential of combining UAV and iPhone/iPad LiDAR data for estimating tree height.

Enhancing Trajectory-Based Operations for UAVs through Hexagonal Grid Indexing: A Step towards 4D Integration of UTM and ATM

Aviation is expected to face a surge in the number of manned aircraft and drones in the coming years, making it necessary to integrate Unmanned Aircraft System Traffic Management (UTM) into Air Traffic Management (ATM) to ensure safe and efficient operations. This research proposes a novel hexagonal grid-based 4D trajectory representation framework for unmanned aerial vehicle (UAV) traffic management that overcomes the limitations of existing square/cubic trajectory representation methods. The proposed model employs a hierarchical indexing structure using hexagonal cells, enabling efficient ground based strategic conflict detection and conflict free 4D trajectory planning. Additionally, the use of Hexagonal Discrete Global Grid Systems provides a more accurate representation of UAV trajectories, improved sampling efficiency and higher angular resolution. The proposed approach can be used for predeparture conflict free 4D trajectory planning, reducing computational complexity and memory requirements while improving the accuracy of strategic trajectory conflict detection. The proposed framework can also be extended for air traffic flow management trajectory planning, Air Traffic Control (ATC) workload measurement, sector capacity estimation, dynamics airspace sectorization using hexagonal sectors and traffic density calculation, contributing to the development of an efficient UTM system, and facilitating the integration of UAVs into the national airspace system with ATM

Prioritization of unmanned aerial vehicles in transportation systems using the integrated stratified fuzzy rough decision-making approach with the hamacher operator

The Integration of Unmanned Aerial Vehicles (UAVs) into transportation systems has numerous benefits, ranging from the ability to record real-time data to having high mobility and broad vision. Because of the increasing levels of congestion in the cities, many of the transportation system tasks, such as traffic management and data collection, have become complicated to handle. The implementation of UAVs in transportation systems is a feasible solution to overcoming these problems. Therefore, UAVs have become a point of interest for many transportation system authorities. This study aims to introduce an integrated Fuzzy Level Based Weight Assessment (LBWA) in a stratification environment (SF-LBWA) and Fuzzy Rough Hamacher Combined Compromise Solution (CoCoSo) model to resolve the prioritization of Unmanned Aerial Vehicles in transportation system problems. The decision framework for such a problem is defined by establishing the relevant alternatives and criteria according to the specialized literature. Hence, a case study illustrates the formulation and solution of the problem. This study will indicate the most suitable alternative for the problem and provide guidance as authorities look for ways to integrate UAVs and transportation systems.

NATO Corps HQ – Land Component – Integration of Unmanned Aerial Vehicles (UAVs)

Abstract Due to the impact on evolution of future conflicts, Unmanned Aircraft Systems/UAS are registered as influential technology, with unpredictable effects in the doctrinal sphere, namely the integration of Unmanned Aircraft Systems/UAS could alter the space-time-combat correlation, by reducing the time for engagement and increasing precision. The integration of Unmanned Aircraft Systems/UAS may require the synchronization of joint actions in all areas to allow rapid change of effort from one direction to another.

Integrating Multi-Scale Remote-Sensing Data to Monitor Severe Forest Infestation in Response to Pine Wilt Disease

Pine wilt disease (PWD) is one of the most destructive forest diseases that has led to rapid wilting and mortality in susceptible host pine trees. Spatially explicit detection of pine wood nematode (PWN)-induced infestation is important for forest management, policy making, and practices. Previous studies have mapped forest disturbances in response to various forest diseases and/or insects over large areas using remote-sensing techniques, but these efforts were often constrained by the limited availability of ground truth information needed for the calibration and validation of moderate-resolution satellite algorithms in the process of linking plot-scale measurements to satellite data. In this study, we proposed a two-level up-sampling strategy by integrating unmanned aerial vehicle (UAV) surveys and high-resolution Radarsat-2 satellite imagery for expanding the number of training samples at the 30-m resampled Sentinel-1 resolution. Random forest algorithms were separately used in the prediction of the Radarsat-2 and Sentinel-1 infestation map induced by PWN. After data acquisition in Muping District during August and September 2021, we first verified the ability of a deep-learning-based object detection algorithm (i.e., YOLOv5 model) in the detection of infested trees from coregistered UAV-based RGB images (Average Precision (AP) of larger than 70% and R2 of 0.94). A random forest algorithm trained using the up-sampling UAV infestation map reference and corresponding Radarsat-2 pixel values was then used to produce the Radarsat-2 infestation map, resulting in an overall accuracy of 72.57%. Another random forest algorithm trained using the Radarsat-2 infestation pixels with moderate and high severity (i.e., an infestation severity of larger than 0.25, where the value was empirically set based on a trade-off between classification accuracy and infection detectability) and corresponding Sentinel-1 pixel values was subsequently used to predict the Sentinel-1 infestation map, resulting in an overall accuracy of 87.63%, where the validation data are Radarsat-2 references rather than UAV references. The Sentinel-1 map was also validated by independent UAV surveys, with an overall accuracy of 76.30% and a Kappa coefficient of 0.45. We found that the expanded training samples by the integration of UAV and Radarsat-2 strengthened the medium-resolution Sentinel-1-based prediction model of PWD. This study demonstrates that the proposed method enables effective PWN infestation mapping over multiple scales.

Multi-Agent Path Finding in Unmanned Aircraft System Traffic Management With Scheduling and Speed Variation

The development of an unmanned aircraft system (UAS) traffic management (UTM) system for the safe integration of unmanned aerial vehicles (UAVs) requires pre-flight conflict detection and resolution (CDR) methods to provide collision-free flight paths for all UAVs before takeoff. A popular solution consists in adapting multi-agent path finding (MAPF) techniques. However, standard MAPF solvers consider only a fixed takeoff time and fixed uniform speed for each UAV flight path, which can lead to inefficiencies in the resolution of instances. Therefore, in this article, we propose incorporating scheduling elements into MAPF solvers, which allows us to adjust the takeoff times and speeds of each UAV to solve conflicts. We introduce two time-related resolution techniques: 1) takeoff scheduling, whereby the start time of a UAV agent is delayed, and 2) speed adjustment, wherein the speed of a UAV agent is decreased over a segment on its flight path. Importantly, we present a distinction of conflict types, which enables us to combine replanning resolution to the aforementioned temporal resolution techniques. We evaluate our proposed approaches on a realistic, high-density UAV delivery scenario in Tokyo, Japan. We show that the combination of takeoff scheduling, replanning, and speed-adjustment resolution techniques improves the efficiency of route planning by reducing the average delay per flight path and the number of rejected flight paths.

UAV and TLS point cloud integration for the surface plant infrastructure of underground coal mines

The surface plant infrastructure (SPI) of underground coal mines is one of important sets of underground mines as it includes essential objects, such as office buildings, structures and equipment used to load, receive, sort or process minerals; receive and discharge waste rocks; provide ventilation for tunnels and energy for mining operations. The measurement and collection of spatial data of SPI are important to ensure the safe and effective management and operation of mining activities in underground mines. A rapid development in geospatial technologies has facilitated the acquisition of geospatial data in the mining industry. Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are two of the typical geospatial technologies, which have made significant contributions to the field of geospatial data collection. While UAV photogrammetry allows to create dense point clouds with centimeter - level accuracy in a short time and large areas, TLS technology can produce dense point clouds with millimeter - level accuracy. However, the latter is time - consuming and expensive while performing on a large area. The integration of UAV and TLS data can be seen as a reasonable solution to gain the advantages of both and avoid the disadvantages of each technology. This paper presents the results of an integrated study of point cloud data generated by UAV and TLS for the plant infrastructure of the underground coal mine. Featuring structures in the study area include mineshaft tower, office and factory buildings. The results show that the UAV and TLS integrated point cloud data has millimeter - level accuracy for important objects such as mineshaft towers, while ancillary structures in the study area have centimeter - level accuracy.

Integration of unmanned aerial vehicle (UAV)-based photogrammetry and InSAR for mining subsidence and parameters inversion: a case study of the Wangjiata Mine, China

Integration of unmanned aerial vehicle (UAV)-based photogrammetry and InSAR for mining subsidence and parameters inversion: a case study of the Wangjiata Mine, China

Internet of Flying Things security: A systematic review

SummaryToday, with the growth of the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs) can play a significant role in terms of stability, reliability, connectivity, and coverage. The integration of UAVs and IoT led to the Internet of Flying Things (IoFT). Internet of drones (IoD) is an infrastructure that provides access and control over the Internet between users and drones. One of the most critical challenges in IoFT and IoD is security. In this article, a systematic review is proposed to analyze the existing literature in the field of IoFT and IoD security. We explain the research methodology including the article selection process and the search queries. Thirteen articles were selected out of 166 articles that were published between 2019 and September 2021. Furthermore, the security approaches in the selected articles are classified into five main categories: security vulnerability and eavesdropping, security realization by Blockchain, secure trajectory and secure communication link, authentication and privacy approaches, and security‐enhanced resource allocation. The main ideas, advantages, disadvantages, the employed tools, and evaluation parameters of each selected article are also discussed in detail. Finally, we point out the open issues and orientations of future researches.

Mobile Networks’ Support for Large-Scale UAV Services

The services based on Unmanned Aerial Vehicles (UAVs) have started to be used in many countries but not on a large scale yet. The paper describes the present status of UAV services and a concept of a solution for large-scale deployment of safe and reliable UAV services that use the 5G network for communication with UAVs. Based on the Standards Developing Organizations and related industry fora activities, unmanned traffic and airspace regulations, especially the commonly supported concept of Unmanned Aircraft Systems Traffic Management (UTM), UAV-related communication requirements and 5G System (5GS) features, we have developed an alternative approach to the integration of UAV and 5GS. The proposed concept includes a set of enablers that can be provided by Mobile Network Operators in order to not only support but also leverage UAV services. As work on many items is still in progress, we identify a list of open issues and challenges and present them at the end of the paper with the main focus on the MNO–UTM provider relationship and safe flights.

Real-time 3D UAV Path Planning in Dynamic Environments with Uncertainty

The integration of Unmanned Aerial Vehicles (UAVs) is being proposed in a spectrum of applications varying from military to civil. In these applications, UAVs are required to safely navigate in real-time in dynamic and uncertain environments. Uncertainty can be present in both the UAV itself and the environment. Through a literature study, this paper first identifies, quantifies and models different uncertainty sources using bounding shapes. Then, the UAV model, path planner parameters and four scenarios of different complexity are defined. To investigate the effect of uncertainty on path planning performance, uncertainty in obstacle position and orientation and UAV position is varied between 2% and 20% for each uncertainty source first separately and then concurrently. Results show a deterioration in path planning performance with the inclusion of both uncertainty types for all scenarios for both A* and the Rapidly-Exploring Random Tree (RRT) algorithms, especially for RRT. Faster and shorter paths with similar same success rates (>95%) result for the RRT algorithm with respect to the A* algorithm only for simple scenarios. The A* algorithm performs better than the RRT algorithm in complex scenarios.

Unmanned Aerial Vehicles (UAV) Integration with Digital Technologies toward Construction 4.0: A Systematic Literature Review

Unmanned Aerial Vehicles (UAVs) have been employed in the construction industry in the last decade for various purposes such as progress monitoring and building inspection. Recently, there has been a rising trend of employing UAVs with other digital technologies (DTs), such as Building Information Modeling and Extended Reality. The integration of these technologies encourages automation and digitization toward better project performance. However, little is known about the implementation of UAVs in conjunction with other DTs. Therefore, this study performs a systematic literature review to determine application areas and technology trends regarding UAVs’ integration with other DTs. The search yielded 287 articles, of which 36 satisfied the established inclusion criteria and formed the foundation of this systematic review. Seven application areas of UAV integration with other DTs were identified: progress monitoring, historic building conservation, information management, construction safety, construction education, structural and infrastructure inspection, and transportation. This study also revealed UAV technology trends encouraging automation and digitization: automated progress monitoring, automated UAV inspection planning, real-time video streaming, and parametric model development of historicbuildings. This study is expected to be a starting point of future in-depth research by providing a general understanding of the current applications of UAVs integration with other DTs.

THz-Empowered UAVs in 6G: Opportunities, Challenges, and Trade-offs

Envisioned use cases of unmanned aerial vehicles (UAVs) impose new service requirements in terms of data rate, latency, and sensing accuracy, to name a few. Meeting such requirements enable highly reliable and harmonized integration of UAVs in the 6G ecosystem. Toward this, terahertz (THz) bands are perceived as a prospective technological enabler for various improved functionalities, such as ultra-high throughput communication, accurate localization, and enhanced sensing capabilities. In this article, we provide the potential opportunities and use cases of THz-empowered UAVs, corresponding novel design challenges, and resulting trade-offs. Furthermore, we overview recent advances in UAV deployment regulations, THz standardization, and health aspects related to THz bands. Finally, we take UAV-to-UAV (U2U) communication as a case study to provide numerical insights into the impact of various system design parameters and environment factors.

Application of combined terrestrial laser scanning and unmanned aerial vehicle digital photogrammetry method in high rock slope stability analysis: A case study

Recent advancements in remote sensing techniques have made it possible to overcome the risky issues using conventional methods and have opened up new opportunities for collecting data on discontinuity characteristics. This research compares the application of a Terrestrial Laser Scanner (TLS) and Unmanned Aerial Vehicle (UAV) in rock slope stability analysis. Two case studies, Ulu Choh Quarry and Jelapang Rock Slope are analysed using Kinematic Analysis and Slope Mass Rating (SMR) to depict rock through the 3D point cloud. The techniques are compared with the hand mapping method concerning the accuracy of the data acquisition in assessing the rock slope. The standard deviation for the dip and dip direction between digital capture and hand mapping is 2.97° and 2.86°, which falls under the acceptable tolerance limit. The integration of UAV and TLS also generates excellent clear point cloud data from the top to the toe of the slope.

Developing Enhanced Unmanned Aerial Vehicle Sensing System for Practical Bridge Inspections Using Field Experiments

Unmanned aerial vehicles (UAVs) have transitioned from a futuristic research concept to becoming a reality for practical, safe, cost-effective bridge inspections. Several studies have used UAVs to capture images from bridges and infrastructure to assess their condition. However, measuring the dynamic responses of bridges using a UAV involves the integration of UAV, sensors, and the use of dynamic equations. Measurement of dynamic transverse displacement especially is a difficult task in the field given the actual constraints of bridges, flights, and sensing under loading events. If transverse displacements could be measured easily, bridge owners could prioritize maintenance operations more cost-effectively by selecting to repair those bridges that move the most under trains. This paper discusses new requirements and solutions for fabricating an enhanced UAV to obtain dynamic transverse displacement benefiting from experiences gained from several field tests. This work follows the regulations of railroad bridge inspection guidelines and considers the aspects for an implementable system in its development. The paper first introduces the preliminary system that has been developed to this end and discusses potential improvements to this system that are identified through multiple field tests. The preliminary UAV system was developed using an algorithm combining the signals from sensors mounted on the UAV to measure dynamic displacements. This paper explains the step-by-step improvement of the existing system which resulted in a successful field test on a real bridge. Subsequently, some modifications and enhancements for the algorithm are proposed which are compatible with the new system data.

Sampling-based unmanned aerial vehicle air traffic integration, path planning, and collision avoidance

Unmanned aircraft or drones as they are sometimes called are continuing to become part of more real-life applications. The integration of unmanned aerial vehicles in public airspace is becoming an important issue that should be addressed. As the number of unmanned aerial vehicles and their applications are largely increasing, air traffic with more unmanned aircraft has to be given more attention to prevent collisions and maintain safe skies. Unmanned aerial vehicle air traffic integration and regulation has become a priority to different regulatory agencies and has become of greater interest for many researchers all around the world. In this research, a sampling-based air traffic integration, path planning, and collision avoidance approach is presented. The proposed algorithm expands an existing 2D sampling-based approach. The original 2D approach deals with only two unmanned aircraft. Each of the two aircraft shares location information with a ground-based path planner computer, which would send back the avoidance waypoints after performing the 2D sampling. The algorithm proposed in this article can handle any number of drones in the 3D space by performing either 2D or 3D sampling. The proposed work shows a 10-fold enhancement in terms of the number of unmanned aerial vehicle collisions. The presented results also contribute to enabling a better understanding of what is expected of integrating more drones in dense skies.

Synergetic UAV-RIS Communication With Highly Directional Transmission

The effective integration of unmanned aerial vehicles (UAVs) in future wireless communication systems depends on the conscious use of their limited energy, which constrains their flight time. Reconfigurable intelligent surfaces (RISs) can be used in combination with UAVs with the aim to improve the communication performance without increasing complexity at the UAVs’ side. In this letter, we propose a synergetic UAV-RIS communication system, utilizing a UAV with a highly directional antenna aiming to the RIS. The proposed scenario can be applied in all air-to-ground RIS-assisted networks and numerical results illustrate that it is superior from the cases where the UAV utilizes either an omnidirectional antenna or a highly directional antenna aiming towards the ground node.