Adoption Of Unmanned Aerial Vehicles Research Articles

Crowdsourced vehicles and UAVs for last-mile delivery application using blockchain-hosted matching mechanism

The crowdsourcing paradigm has been used for last-mile delivery applications to offer cost-efficient delivery through crowdsourced vehicles. The increasing demand from users for fast delivery led to the adoption of Unmanned Aerial Vehicles (UAVs) due to their speed and cost, for instance, in peak hours. However, existing frameworks do not offer a comprehensive solution that utilizes vehicles and UAVs for transparent, timely, and cost-efficient delivery. This paper proposes a novel supply chain management framework on a consortium blockchain with last-mile delivery through crowdsourced vehicles and UAVs. The blockchain-based framework aims to provide last-mile delivery for time-critical tasks in a transparent and cost-efficient manner. A blockchain-hosted Gale-Shapley Matching mechanism is designed for allocating delivery tasks to UAVs and vehicles that will perform them timely and cost-efficiently. The allocation mechanism is incorporated in the proposed management framework consisting of smart contracts that autonomously respond to members' interactions. In addition, the framework stores the supply chain process data transparently and immutably. The performed experiments explore the feasibility of the proposed solution showing the increase in task allocation percentage (at least 6%) and reward (at least 50%) while reducing the delivery time (at least 23%) compared to the considered benchmarks.

Blockchain and NFT-based traceability and certification for UAV parts in manufacturing

In recent years, the widespread adoption of Unmanned Aerial Vehicles (UAVs) has increased significantly, sparking the need for reliable mechanisms to verify the authenticity, origin, and history of their constituent components. However, the lack of secure and trusted evidence for traceability, attestation, and certification of these components poses alarming challenges in ensuring transparency, data integrity, and authenticity. In this paper, we propose a blockchain and Non-Fungible Token (NFT)-based solution that manages, certifies, and traces the origin, history, and ownership of UAVs and their manufacturing components. The solution ensures decentralization, transparency, audibility, and trustworthiness. We utilize composable NFTs, which capture and store UAV data in a hierarchical way, wherein parent NFTs represent the assembled UAV data, and child NFTs represent individual UAV parts. This approach enables system-wide traceability and facilitates smooth ownership exchanges. We further incorporate Digital Twin (DT) details into the NFT metadata to enhance the representation accuracy of the actual physical asset. In this paper, we present a detailed system architectural design of our solution, along with implementation, testing, and validation of relevant algorithms and smart contracts (SCs). We conduct cost evaluation and security analysis, and we also compare our solution with existing popular solutions in the literature review. The developed smart contracts code has been made publicly available on GitHub.

CHALLENGES AND BARRIERS FOR UNMANNED AERIAL VEHICLE (UAV) IMPLEMENTATION IN MALAYSIAN INFRASTRUCTURE PROJECTS

The usage of Unmanned Aerial Vehicles (UAV) in infrastructure projects has inclined drastic recently due to its benefits. However, there is a slowness in transition from conventional construction approach to new innovation. Besides, there are lots of issues in current practices such as cost overrun, dangerous, delay of time completion and lack of an overall comprehensive scientific system. Furthermore, although there was high level of awareness on the advantages of using UAVs, the Malaysian construction industry are still reluctant for the changes. In response, to overcome issues that the construction industry faces, such as infrastructure projects thus, this study aims to develop a conceptual framework for promoting the applications of UAV in Malaysia infrastructure projects. The methods adopted include explanatory sequential research methods. For instance, questionnaire surveys and interviews are used to collect quantitative and qualitative data, respectively. Besides that, a refined conceptual framework of the challenges and barriers of UAV adoption in Malaysia infrastructure projects were mainly contributed by the restrictive regulatory environment and industry point of view. For example, the privacy issues and guidelines on autonomous UAV and the industry lack of confidence in the adoption of UAV and education on the adoption of the UAV. In addition, it offers some solutions for the issues that have been revealed in the preliminary conceptual framework for the challenges on UAV adoption in Malaysia infrastructure projects. For instance, amaze the contractor with the final data from the UAV. Lastly, the identify solution served as a reference for other Malaysian construction stakeholders that have not used UAVs before. The recommendations for future scholarly research include the roles of UAV technology with other technologies such as Artificial Intelligent (AI) or integration of data with other software.

TD3-Based Optimization Framework for RSMA-Enhanced UAV-Aided Downlink Communications in Remote Areas

The need for reliable wireless communication in remote areas has led to the adoption of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs). FlyBSs hover over a designated area to ensure continuous communication coverage for mobile users on the ground. Moreover, rate-splitting multiple access (RSMA) has emerged as a promising interference management scheme in multi-user communication systems. In this paper, we investigate an RSMA-enhanced FlyBS downlink communication system and formulate an optimization problem to maximize the sum-rate of users, taking into account the three-dimensional FlyBS trajectory and RSMA parameters. To address this continuous non-convex optimization problem, we propose a TD3-RFBS optimization framework based on the twin-delayed deep deterministic policy gradient (TD3). This framework overcomes the limitations associated with the overestimation issue encountered in the deep deterministic policy gradient (DDPG), a well-known deep reinforcement learning method. Our simulation results demonstrate that TD3-RFBS outperforms existing solutions for FlyBS downlink communication systems, indicating its potential as a solution for future wireless networks.

On the use of drones for vision-based digitalization, diagnostics and energy efficiency assessment of buildings

The adoption of Unmanned Aerial Vehicles for energy efficiency assessment is a promising technique. Although there are some interesting ideas to automate the process, most of the operations are still done manually. An integrated methodology is presented to identify maintenance needs from both RGB and infrared images collected with a commercial drone. The real building selected as case study is reconstructed in a 3D environment through Structure-from-Motion Photogrammetry, while the infrared information is integrated and properly scaled on it to obtain a 3D point cloud model that provides, for each point: (i) information about its position in the space, and (ii) external surface temperature measured during data gathering phase. The point cloud is then segmented into the different sides of the envelope to identify each part of the façade and compared with a model that reconstructs the expected conditions of the building. This procedure enables the development of an automated pipeline to identify defects and failures in a building envelope and to suggest corrective actions

The Adoption of Unmanned Aerial Vehicles (UAV) Technology in the Construction Industry: Construction Stakeholders’ Perception

In parallel to the Industry Revolution 4.0, the introduction of Unmanned Aerial Vehicles (UAV) in construction is expected to improve the construction project management efficiency. Although much research works has been done by the academics in uncovering the advantageous potentials of using UAV in construction, the barriers and drivers that influence the construction companies towards adopting UAV have received scant or very limited attention from the academics. Even though the UAV technology infrastructure has progressively been developed and improved by the government and the UAV manufacturers with some construction companies successfully got on board the platform to tap the potentials for gains, however there are construction companies have yet to act on it. This paper aims to identify the main drivers of the construction companies in adopting and integrating the UAV into their construction activities or business. The semi-structured interviews were designed using theory of interpersonal communication in achieving an honest exchange of information on the perception of the participants, their encounters, experiences, views and expectation with respect to the use of UAV in their business activities. This study was conducted with eight respondents from Johor Bharu and Kuala Lumpur of diverse background and experience in the construction and UAV. The findings show that relative advantage, education, knowledge of technology, positive attitude of the decision makers, compatibility of technology, simplicity of technology, and observability are the six drivers to the adoption of UAV in the construction industry. Therefore, this study may serve as a conceptual framework for the future research works on the adoption of UAV in the Malaysian construction industry as the industry is increasingly digitally driven with the emergence of new technology such as the UAV application technology.

Impediments to Construction Site Digitalisation Using Unmanned Aerial Vehicles (UAVs)

Utilising emerging innovative technologies and systems to improve construction processes in an effort towards digitalisation has been earmarked as critical to delivering resilience and responsive infrastructure. However, successful implementation is hindered by several challenges. Hence, this study evaluates the challenges facing the adoption of unmanned aerial vehicles towards the digitalisation of the built environment. The study adopted a quantitative survey of built environment stakeholders in developed and developing economies. A total of 161 completely filled forms were received after the survey, and the data were analysed using descriptive analysis and inferential statistics. The study’s findings show that there are different barriers experienced between developed and developing countries in the adoption of drones towards digitalising construction processes in the built environment. Moreover, economic/cost-related factors were identified as the most critical barriers to the adoption of drones, followed by technical/regulatory factors and education/organisation-related factors. The findings can assist the built environment in reducing the impact of these barriers and could serve as a policy instrument and helpful guidelines for governmental organisations, stakeholders, and others.

ОСОБЛИВОСТІ ВИКОРИСТАННЯ ДАНИХ ДИСТАНЦІЙНОГО ЗОНДУВАННЯ ЗЕМЛІ В СИСТЕМІ ТОПОГЕОДЕЗИЧНОГО ЗАБЕЗПЕЧЕННЯ ЗБРОЙНИХ СИЛ УКРАЇНИ

The widespread use of remote sensing data (RSD) in solving the tasks of topogodetic support of the troops and during the conduct of hostilities is one of the modern development trends. Earth remote sensing data provide accurate, reliable, reliable, operational and up-to-date information about terrain, areas, and observation objects. Its modern means allow for round-the-clock continuous observation in almost any weather conditions and for any region of the globe, to determine the coordinates of the objects of observation with an accuracy from 0.03-0.5 m to several meters. Remote sensing methods allow continuous topographic reconnaissance, gathering information in hard-to-reach areas, in temporarily inaccessible territories. RSD materials are used for compiling, updating and correcting topographic, digital, electronic maps, for compiling special maps, photo documents, descriptions and information about the area, etc. A complete transition to remote methods of terrain research is especially appropriate during hostilities, primarily due to its remoteness, economy of expenditure of funds, workers and time for cartographic work. The research and appropriate combination of various methods of land surveying allows to obtain accurate, high-quality data and to solve the tasks of topographic support faster, more quickly and more efficiently. Improving the use of remote sensing data when performing such tasks of topogodetic support as creating photoplans, photoschemes, photomaps, maps of terrain changes, updating, operational correction of maps, etc. allows you to improve the process of cartographic activities, save money and time for work. RSD is the basis, and sometimes the only source document for creating maps. Since there are no operational satellites in Ukraine, and aerial photography is carried out with limited capabilities, unmanned aerial vehicles (UAVs), due to their advantages, are a promising method of obtaining data about the terrain. Data obtained from UAVs are relevant, timely, accurate, and relatively easy to process. The adoption of UAVs in the Topographic Service of the Armed Forces of Ukraine will allow to increase the efficiency and accuracy of the tasks assigned, to improve the performance of a number of cartographic works.

Distributed Communication and Sensing System Co-Design for Improved UAV Network Resilience

The progress in wireless technology over the past decade led to the rapid adoption of unmanned aerial vehicles (UAVs) for various applications. As the interest in UAVs is accelerating, increased attention is paid to the reliability and resilience of UAV-based systems with respect to the collision avoidance. One of the ways to improve this aspect is to utilize the RADAR functionality. In this work, we consider a cellular network employed for communication jointly with RADAR operation. The critical parameter that affects the RADAR algorithm is the radar cross-section (RCS). Since the task of obtaining the RCS of a complex-shaped object is extremely challenging, we first propose a novel, accurate, and fast method of scattered field assessment. We further perform radio network planning for the cellular deployment, as well as link budget estimations for the RADAR system that co-exists with it. Under this system model, we carry out detailed Monte-Carlo simulations of the RADAR detection process to obtain reliable statistical results and answer the question of how the actual bistatic RCS model affects the detection algorithm. We then apply mathematical modeling based on stochastic geometry to estimate the collision probability without the need to simulate an extensive number of flight-hours. Our numerical results confirm the robustness to RCS pattern nulls, which is crucial for safety-centric applications such as collision avoidance.

FFP: A Force Field Protocol for the tactical management of UAV conflicts

In recent years, we have seen a tremendous growth in the adoption of Unmanned Aerial Vehicles (UAVs). Nowadays, UAVs are used in many different industries such as agriculture, inspection (bridges, pipelines, etc.), parcel delivery, etc. In the near future, this will lead to a substantial increase of aircraft in our airspace, especially in urban areas. Many existing collision avoidance approaches rely on heavy and/or expensive sensors, which limits its use for real UAVs due to increased costs, weight and complexity. Hence, to address this problem, in this paper we present a solution for the tactical management (i.e. in-flight) of UAV conflicts outdoors that introduces minimal requirements: a wireless interface and a GPS module. Specifically, we provide a collision avoidance algorithm based on artificial potential fields to provide flight safety. Our solution, called Force Field Protocol (FFP), allows the UAVs to autonomously detect each other using wireless communications, and to maintain a safe distance between them without the intervention of any central service. Experiments performed in our multi-UAV simulator ArduSim show that, with our approach, collisions between two UAVs are completely avoided in a wide set of scenarios, while introducing low disturbances to the original flight plans. Specifically, in the scenarios that we tested, the additional flight time introduced will be only 7 s longer in the worst case; in addition, it is able to improve upon previous approaches by reducing flight time by up to 54 s. We have shown experimentally that our approach can be scaled easily up to 100 UAVs, and that the probability of a collision is very low (< 0.06) despite flying in a small area (2.5 km × 2.5 km).

Unmanned aerial vehicles usage on south african construction projects: perceived benefits

In recent years, unmanned aerial vehicles (UAVs) are being employed in various parts of the engineering industries for project development, project management, surveying, among others. UAVs can also be adopted in construction for pre-planning, proper surveying of the given area, checking or inspecting site safety and quality monitoring. Based on these envisaged uses, this study is set to assess the benefits of UAVs usage in the construction industry. This was achieved through a detailed literature review combined with empirical data analysis. Data was retrieved through questionnaire survey distributed to professionals randomly in the South African construction industry. The retrieved data was analysed using descriptive and inferential data analysis methods. Findings revealed that UAVs adoption in the construction industry will lead to reduction in worker’s injury as it will be implemented for monitoring of workers activities on site. It was also revealed that UAVs are useful in on-site asset tracking which allows stakeholders to have real-time information on the construction project from anywhere. The study concluded that the efficiency in the performance of the construction industry can be achieved through the adoption of UAVs in the different stages of construction projects.

A review of remote-sensing unmanned aerial vehicles in the mining industry

The increased adoption of unmanned aerial vehicles (UAVs) may improve the productivity and cost-effectiveness of remote sensing in the mining industry. This review's objective is to enable stakeholders to identify possible application adoption, improvement, and innovation opportunities. The review merges a building block strategy and practical screening criteria to identify possible avenues of research to answer the review questions. After the screening process, 72 documents were included in the review. Papers were classified into four categories: exploration, development, exploitation, and reclamation. Fifteen applications were identified, the majority of which were in the exploration phase. The most often researched applications were topographic surveys, reclamation monitoring, and slope management. From the two UAV types identified, multi-rotor vehicles were the most favoured for all applications. From the eight remote sensing techniques identified, photogrammetry was the one most often used. Other techniques were limited because of complexity, cost, or the incompatibility of sensors and UAVs. The review was limited to published papers in academic journals. Future studies could aim to include empirical data on the latest UAV applications used in the mining industry.

Multi-objective Optimization of Subsonic Glider Wing Using Genetic Algorithm

The widespread adoption of Unmanned Aerial Vehicles (UAVs) can be traced to its flexibility and wide adaptability to various operating conditions and applications, comparably low cost of construction and maintenance and environmental friendliness as they can be easily configured for electric power. The use of electric power also favours its low noise applications such as surveillance. A major issue associated with surveillance, as addressed in this study is the compromise between Range and Endurance operation modes. The Range mode relates to being able to cover longer distances while the Endurance mode relates to spending longer times in the atmosphere for a fixed charge. Trying to balance the interplay of these parameters gave rise to a multi-objective optimization where the objectives are somewhat conflicting. This resulted in a set of Pareto solutions which are a set of design parameters (primarily angle of attack) that satisfy the joint requirements of the performance parameters of Range and Endurance. This study first considered a baseline aerodynamic design using traditional design methods. Design of Experiment techniques were then used to select the most favourable design points. This model was then used to build an input framework for Genetic Optimization algorithm deployed in the Global Optimization Toolbox of MATLAB. The result of this research shows that most of the region associated with medium angle of attack (AOA) setting (7 degrees) jointly satisfies good Range and Endurance performances with an average lift-to-drag ratio of 20 in the flight configuration considered. The implication of this result is that low velocity drag encountered in surveillance that requires a high AOA is largely reduced with the medium setting, albeit stabilized with other structural and aerodynamic settings, namely an aspect ratio of 13 and a taper ratio of 0.6.

ECU-IoFT: A Dataset for Analysing Cyber-Attacks on Internet of Flying Things

There has been a significant increase in the adoption of unmanned aerial vehicles (UAV) within science, technology, engineering, and mathematics project-based learning. However, the risks that education providers place their student and staff under is often unknown or undocumented. Low-end consumer drones used within the education sector are vulnerable to state-of-the-art cyberattacks. Therefore, datasets are required to conduct further research to establish cyber defenses for UAVs used within the education sector. This paper showcases the development of the ECU-IoFT dataset, documenting three known cyber-attacks targeting Wi-Fi communications and the lack of security in an affordable off-the-shelf drone. At present, there are no publicly available labeled datasets that reflect cyberattacks on the Internet of Flying Things (IoFT). The majority of the publicly available network traffic datasets are emulated and do not reflect the scenarios/attacks from a real test setup. This dataset will be beneficial for both cybersecurity researchers to develop defense strategies and UAV manufacturers to design more secure products. In the future, endeavors will be taken to incorporate newer attacks and create datasets appropriate for big data analysis.

Multi-Agent Reinforcement Learning Aided Intelligent UAV Swarm for Target Tracking

Past few years have witnessed the widespread adoption of unmanned aerial vehicles (UAVs) in target tracking for regional monitor and strike. Most existing target tracking approaches rely on the target motion frames obtained by the camera equipped, or on ideally assuming a pre-set target trajectory. However, in practice, the real trajectory of the target cannot be perfectly known to the UAVs in advance, and also the target may intelligently adjust its flying strategy according to the environment. Besides, the limited flight performance, as well as information capture and processing capability, of a single UAV can hardly fulfill high tracking success rate requirements. To address aforementioned issues, this paper proposes an end-to-end cooperative multi-agent reinforcement learning (MARL) scheme, where UAVs are enabled to make intelligent flight decisions for cooperative target tracking, on the basis of the past and current states of the target. In order to reduce power consumption and prolong the lifetime of the UAV tracking system, the propulsion power consumption model and energy saving strategy are introduced. Moreover, to further increase the detection coverage, spatial information entropy is introduced in the tracking algorithm. Simulation results show that our proposed algorithm outperfoms the deep reinforcement learning baselines in terms of the mean episode rewards, while also yields high performances with respect to tracking success rates, power saving efficiency and detection coverage.

Multitask Learning of Alfalfa Nutritive Value From UAV-Based Hyperspectral Images

Alfalfa is a valuable and widely adapted forage crop, and its nutritive value directly affects animal performance and ultimately affects the profitability of livestock production. Traditional nutritive value measurement method is labor-intensive and time-consuming and thus hinders the determination of alfalfa nutritive values over large fields. The adoption of unmanned aerial vehicles (UAVs) facilitates the generation of images with high spatial and temporal resolutions for field-level agricultural research. Additionally, compared with other imaging modalities, hyperspectral data usually consist of hundreds of narrow spectral bands and allow the accurate detection, identification, and quantification of crop quality. Although various machine-learning methods have been developed for alfalfa quality prediction, they were all single-task models that learned independently for each quality trait and failed to utilize the underlying relatedness between each task. Inspired by the idea of multitask learning (MTL), this study aims to develop an approach that simultaneously predicts multiple quality traits. The algorithm first extracts shared information through a long short-term memory (LSTM)-based common hidden layer. To enhance the model flexibility, it is then divided into multiple branches, each containing the same or different number of task-specific fully connected hidden layers. Through comparison with multiple mainstream single-task machine-learning models, the effectiveness of the model is illustrated based on the measured alfalfa quality data and multitemporal UAV-based hyperspectral imagery.

The role of peer effects on farmers’ decision to adopt unmanned aerial vehicles: evidence from Missouri

ABSTRACT This paper examines the role of peer effects on farmers’ decision to adopt Unmanned Aerial Vehicles (UAVs). This relationship is analysed by means of a Bayesian spatial autoregressive probit model applied to spatially explicit data describing the awareness, attitudes, and adoption of UAVs by 809 Missouri farmers. Results show that it is not only the farmers’ characteristics, awareness, and attitudes towards UAVs that affect adoption, but also the adoption behaviour and traits of neighbouring peers. Peer effects arise both from UAV adoption of nearby farmers and from spatial spillovers of farmers’ characteristics, awareness of UAV agricultural applications, expectations of economic and environmental benefits from UAV use and perceived neighbour privacy concerns in the use of UAVs.

Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities

The adoption of Unmanned Aerial Vehicles (UAVs) in numerous sectors is projected to grow exponentially in the future as technology advances and regulation evolves. One of the promising applications of UAVs is in transportation systems. As the current transportation system is moving towards Intelligent Transportation Systems (ITS), UAVs will play a significant role in the functioning of ITS. This paper presents a survey on the recent advances of UAVs and their roles in current and future transportation systems. Moreover, the emerging technologies of UAVs in the transportation section and the current research areas are summarized. From the discussion, the challenges and opportunities of integrating UAVs towards future ITS are highlighted. In addition, some of the potential research areas involving UAVs in future ITS are also identified. This study aims to lay a foundation for the development of future intelligent and resilient transportation systems.

Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks

The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.

Interlocking Safety Cases for Unmanned Autonomous Systems in Shared Airspaces

The growing adoption of unmanned aerial vehicles (UAVs) for tasks such as eCommerce, aerial surveillance, and environmental monitoring introduces the need for new safety mechanisms in an increasingly cluttered airspace. In our work we thus emphasize safety issues that emerge at the intersection of infrastructures responsible for controlling the airspace, and the diverse UAVs operating in their space. We build on safety assurance cases (SAC) - a state-of-the-art solution for reasoning about safety - and propose a novel approach based on interlocking SACs. The infrastructure safety case (ISAC) specifies assumptions upon UAV behavior, while each UAV demonstrates compliance to the ISAC by presenting its own (pluggable) safety case (pSAC) which connects to the ISAC through a set of interlock points. To collect information on each UAV we enforce a “trust but monitor” policy, supported by runtime monitoring and an underlying reputation model. We evaluate our approach in three ways: first by developing ISACs for two UAV infrastructures, second by running simulations to evaluate end-to-end effectiveness, and finally via an outdoor field-study with physical UAVs. The results show that interlocking SACs can be effective for identifying, specifying, and monitoring safety-related constraints upon UAVs flying in a controlled airspace.