Search Results (293)

Marine aerosol particles influence the climate, and interactions between ocean waves and coral reefs may impact aerosol size distributions in remote locations, such as the Great Barrier Reef. However, quantifying these processes has proven to be challenging. We tested whether marine aerosol size distributions and concentrations differ across four zones: background air outside the lagoon, above the reef crest, within the lagoon, and near the beach of Heron Island, approximately 85 km offshore. Using a modified DJI Matrice 600 hexacopter equipped with a miniaturised optical particle counter and custom inline gas dryer, we measured aerosols from 165 to 3000 nm across 64 drone flights during 16 sampling events in November 2024. Aerosol concentrations showed substantial day-to-day temporal variability, while spatial differences among reef zones were generally minor; on certain days, the maximum difference between background and near-island measurements reached approximately 25%. K-means clustering identified four dominant air mass transport patterns, and Hybrid Single-Particle Lagrangian Integrated Trajectory model analysis indicated that upwind conditions had a strong influence on aerosol loading. Vertical profiles revealed limited variability within the lowest 100 m. Mixing layer height, air parcel travel speed, and water depth along the final 12 h of trajectories were key drivers of aerosol variability. These results demonstrate the potential of drone-based measurements for characterising marine aerosols and provide a foundation for improving climate model representations of natural aerosol processes. Full article

With the increasing adoption of advanced drone technologies across diverse fields, the Internet of Drones (IoD) has emerged as a novel mobility paradigm, particularly enhancing Intelligent Transportation Systems (ITS) in urban environments. Despite its significant potential, the IoD faces substantial challenges due to inherent resource constraints such as limited computational power and energy capacity, which hinder the implementation of robust cybersecurity solutions. These limitations expose IoD networks to various security vulnerabilities and privacy threats, necessitating an exhaustive analysis and understanding of these risks. In this paper we introduce SafeIoD, a comprehensive security framework designed to establish standardized procedures for proactive risk identification in Internet of Drones (IoD) devices. It involves sequential steps to determine the trustworthiness of devices subjected to these certification. Therefore, SafeIoD seeks to ensure a basic security level before implementation in a real scenario, where the network devices are evaluated in regards to the specific security requirements. Validation through experimental testing with 15 participants across four IoD deployment scenarios and one military certification case demonstrated the framework’s effectiveness: the tool achieved 73% user satisfaction rating, successfully identified an average of 3.0 security requirements per device, and provided specific lightweight cryptographic algorithm recommendations for 62.2% of elicited requirements. In a tactical military scenario simulation, the framework accurately predicted risk propagation patterns, with COOJA network simulations confirming that implementation of framework-recommended protocols reduced successful attack propagation from 60% to below 5% of the network. Full article

In densely populated and traffic-congested major cities, medical emergency rescue incidents occur frequently, making the use of drones for emergency medical supplies delivery a new emergency distribution method. However, establishing drone transportation networks in urban areas requires balancing spatiotemporal fluctuations in emergency needs, meeting hospitals’ mandatory constraints on response time, and addressing factors like airspace restrictions and weather impacts. By analyzing the spatiotemporal distribution characteristics of medical emergency logistics in large cities, this study constructs a drone base station location optimization model integrating dynamic and static factors. The model combines multi-source data including emergency needs, geographic information, and airspace limitations. It employs kernel density estimation to identify hotspot areas, uses DBSCAN clustering to detect long-term stable demand hotspots, and applies LSTM methods to predict short-term and sudden demand fluctuations. The model optimizes coverage rate, response time, and cost budget control for drone transportation networks through a multi-objective genetic algorithm. Using Guangzhou as a case study, the results demonstrate that through “dynamic-static” collaborative deployment and multi-model drone coordination, the network achieves 96.18% demand coverage with an average response time of 673.38 s, significantly outperforming traditional vehicle transportation. Sensitivity analysis and robustness testing further validate the model’s effectiveness in handling demand fluctuations, weather changes, and airspace restrictions. This research provides theoretical support and decision-making basis for scientific planning of urban medical emergency drone transportation networks, offering practical significance for enhancing urban emergency rescue capabilities. Full article

Open-pit coal mining is characterized by multiple pollution sources, diverse types, and extensive affected areas, leading to complex air pollution with wide diffusion. Traditional fixed monitoring methods cannot address these limitations. Taking a coal mine in Xinjiang as a case study, this study developed a drone-mounted mobile atmospheric monitoring system, focusing on nitrogen dioxide (NO₂) and suspended particulate matter (PM_2.5 and PM₁₀) to explore their distribution, diffusion patterns, and influencing factors. The results show distinct seasonal pollutant characteristics: NO₂ and ozone (O₃) dominate in summer, while particulate matter prevails in winter. The temporal distribution exhibits a bimodal pattern, with high levels in the early morning and evening hours. Spatially, higher pollutant concentrations accumulate vertically below ground level, while lower levels are observed above it. Horizontally, elevated concentrations are found along northern transport corridors; however, these levels become more uniform at greater heights. A spatiotemporal prediction model integrating convolutional neural network (CNN) and long short-term memory (LSTM) network was successfully applied to real-time pollutant prediction in open-pit coal mining areas. This study provides a reliable mobile monitoring solution for open-pit coal mine air pollution and offers valuable insights for targeted pollution control in similar mining areas. Full article

Cities are increasingly integrating micromobility, which heightens the need for robust analytical methods to identify high-risk intersections. This study presents a three-stage methodological approach that combines six years of accident data, spatial hotspot analysis, and calibrated floating-car traffic data to estimate exposure and calculate intersection crash rates in Central Vilnius. Testing the proposed approach identified eight high-risk intersections, with intersection crash rates (ICR) ranging from 0.044 to 0.151, indicating substantial differences in exposure-adjusted risk across the network. The validation of floating-car data (FCD) produced a determination coefficient (R²) of 0.87, confirming reliable exposure estimates where traditional traffic counts are not available. One selected intersection was analyzed in greater depth using drone-based observations and conflict assessment, leading to two redesign alternatives. Both reduced conflicts, though the signalized option eliminated uncontrolled conflict points and offered the strongest expected safety improvement. The suggested methodological approach demonstrates how integrating accident data, exposure estimation, and behavioral analysis can support evidence-based scalable interventions to improve micromobility safety. Despite certain limitations, it enables the rapid identification of problematic intersections, provides site-specific safety diagnosis, and facilitates the development of data-driven design improvements to enhance the safety of micromobility users. As the world strives to shift towards greater sustainability, the concept of micromobility, defined as the use of lightweight, short-distance modes of transport, has gained growing attention among users and policymakers. Full article

Hydrogen energy is a pivotal alternative to lithium-ion batteries for low-altitude aircraft, offering a pathway to sustainable aviation with its zero emissions and high energy density. Nevertheless, its broader application is hindered by challenges in storage, safety, and performance under extreme conditions such as low pressure and low temperature at high altitudes. This paper systematically evaluates various hydrogen power technologies—including water-cooled and air-cooled proton exchange membrane fuel cells (PEMFCs) as well as hydrogen turbines—highlighting their respective advantages, limitations, and suitability for different aircraft types. Among these, water-cooled PEMFCs are identified as the most viable option for manned low-altitude aircraft due to their balanced performance in power density and startup capability. In contrast, air-cooled PEMFCs demonstrate distinct cost-effectiveness for lightweight drones, while hydrogen turbines show promise for long-range regional transport. Furthermore, we analyze current progress in integrating PEMFCs into aircraft platforms and discuss persistent challenges in system compatibility and environmental adaptation. Finally, potential future development directions for PEMFC applications in low-altitude aviation are outlined. Full article

With China’s low-altitude economy becoming a strategic emerging industry, the rapid growth of UAV applications demands higher efficiency in low-altitude airspace utilization and safety management. However, existing studies lack unified grid division standards and refined methods to evaluate capacity for complex urban low-altitude airspace. This study is devoted to developing a methodology for determining safe distances and assessing the throughput capacity of transport systems. The work is based on a multi-criteria assessment that takes into account four significant indicators. The application of the Pareto optimization principle made it possible to identify the most effective compromise solutions. A collision probability model with random UAV(Unmanned Aerial Vehicle) headings was proposed to determine safety separations, and a grid capacity simulation model with saturation judgment and convergence verification was established. The optimal grid granularity was identified as 20 m. Safety separations for DJI M300RTK, Mavic 3Pro, and Air 3S were 104 m, 86 m, and 47 m, respectively. Saturated capacity stabilized within 106–116 s, with stable values of 1.022, 0.961, and 1.023 drones/min for the three UAV models. The results of the study contain key conclusions about traffic capacity and suggest ways to optimize it. Conclusions: This study provides a theoretical framework for airspace resource optimization and UAV path planning, offering quantifiable benchmarks to evaluate and manage urban low-altitude airspace. Full article

Various intersection structures are utilized in city-wide traffic network infrastructure by local transportation authorities to handle the exponentially increasing traffic loads in developing countries. In this regard, numerous studies have considered the notable positive contribution of the modern roundabouts in intersection performance as a prominent method utilized widely in our contemporary world. Properly designed roundabouts are vital components of sustainable transportation planning, as they significantly influence traffic efficiency, safety, and environmental performance. Accurate estimation of roundabout capacity is essential to ensure that they can accommodate anticipated traffic volumes without causing congestion, thereby contributing to energy efficiency and reducing emissions. Moreover, sustainable roundabout design supports the development of safer and more inclusive transportation networks by improving accessibility for all road users, thus strengthening the overall sustainability of urban mobility. The SIDRA (version 8.0), a traffic simulation software, is frequently employed in performance analysis and determining the effects of possible outcomes of different scenarios of roundabouts in today’s world. On the other hand, driver behaviors are found to play a significant role in software performance during the analysis process of roundabout capacity and performance. Therefore, in order to optimize the environmental factor (EF) representing driver behaviors in the SIDRA software, a Differential Evolution Algorithm-Based Bi-Level Calibration Model (DEBCAM) was introduced. Observation data collected from eight different modern-structured roundabouts through drones were run into the SIDRA simulation software; the average delays obtained were employed to estimate optimum EF values through DEBCAM. Observed average delay values were taken into consideration with respect to the delay values obtained as a result of the SIDRA calibration by using the GEH statistics. GEH values indicate the consistency of vehicle delay data obtained via the DEBCAM with observed data. Acquired results clearly suggest that the SIDRA software needs to be calibrated so that it can represent drivers’ behaviors. After determination of the optimum values of the EF parameter for calibration of the SIDRA software, the regression analysis was conducted through the Partial Least Squares (PLS) method. As a result of the analysis, a capacity estimation model was developed, which displayed a significant conformity with the SIDRA capacity estimation results. Our findings suggested that the parameter requirement for the roundabout capacity estimation can be decreased by employing the appropriate EF value for the roundabout that needs to be analyzed. Full article

The proliferation of drones across precision agriculture, disaster response operations, and delivery services has accentuated the critical need for secure communication frameworks. Due to the limited computational capabilities of drones and the fragility of real-time wireless communication networks, the cellular connected drones confront mounting cybersecurity threats. Traditional authentication mechanisms, such as public-key infrastructure-based authentication, and identity-based authentication, are centralized and have high computational costs, which may result in single point of failure. To address these issues, this paper proposes a blockchain-enabled authentication and key agreement scheme for cellular-connected drones. Leveraging identity-based cryptography (IBC) and the Message Queuing Telemetry Transport (MQTT), the scheme flow is optimized to reduce the communication rounds in the authentication. By integrating MQTT brokers with the blockchain, it enables drones to authenticate through any network node, thereby enhancing system scalability and availability. Additionally, cryptographic performance is optimized via precompiled smart contracts, enabling efficient execution of complex operations. Comprehensive experimental evaluations validate the performance, scalability, robustness, and resource efficiency of the proposed scheme, and show that the system delivers near-linear scalability and accelerated on-chain verification. Full article

A well-performing road network is essential for modern society. But any road is nothing without its users—cyclists, drivers, pedestrians. Road network cannot be managed without knowing who the roads serve. The gaps in this knowledge lead to decisions that hinder efficiency, equality, and sustainability. This is why monitoring traffic is imperative for road management. However, traditional short-term traffic counting methods fail to provide full coverage at a reasonable cost. This study assessed the economic feasibility of drone-enabled traffic monitoring systems across Estonian urban environments through comparative spatial and economic analysis. Hexagonal tessellation was applied to 255 urban locations, identifying 47,530 monitoring points across 4077 grid cells. Economic modeling compared traditional counting costs with drone-based systems utilizing ultralight drones and nomadic 5G infrastructure. Monte Carlo simulation evaluated robustness under varying operational intensities from 30 to 180 days annually. Analysis identified an 8-point density threshold for economic viability, substantially lower than previously reported requirements. Operational intensity emerged as the critical determinant: minimal operations (30 days) proved viable for 9.0% of locations, while semi-continuous deployment (180 days) expanded viability to 81.6%. The findings demonstrate that drone-based monitoring achieves 60–80% cost reductions compared to traditional methods while maintaining equivalent accuracy (95–100% detection rates for vehicles, cyclists, and pedestrians), presenting an economically superior alternative for 67% of Estonian urban areas, with viability extending to lower-density locations through increased operational utilization. Full article

The growing demand for fast and sustainable urban deliveries has accelerated exploration of the use of Unmanned Aerial Vehicles as viable logistics solutions for the last mile. This study investigates the integration of a distributed multi-agent system with a structured three-dimensional Urban Aerial Network (3D-UAN) for drone delivery operations. The proposed architecture models each drone as an autonomous agent operating within predefined air corridors and communication protocols. Unlike traditional approaches, which rely on simplified 2D models or centralized control systems, this research exploits a multi-layered 3D network structure combined with decentralized decision-making for improving scalability, safety, and responsiveness in complex environments. Through agent-based simulations, this study evaluates the operational performance of the proposed system under varying fleet size conditions, focusing on travel times and system scalability. Preliminary results demonstrate that the potential of this approach in supporting efficient, adaptive, resilient logistics within Urban Air Mobility frameworks depends on both the size of the fleet operating in the 3D-UAN and constraints linked to the current regulations and technological properties, such as the maximum allowed operational height. These findings contribute to ongoing efforts to define robust operational architectures and simulation methodologies for next-generation urban freight transport systems. Full article

Unmanned aircraft are increasingly recognized for their potential to enhance healthcare logistics, offering rapid and reliable transport solutions. Among the many envisioned use cases, emergency medical deliveries stand out as particularly promising due to their immediate societal value. This study investigates the potential of drones operating under U-space to support hospital-to-hospital emergency deliveries in Madrid. Using the GEMMA tool, we modeled and simulated operations with two drone types along direct routes between four hospitals, resulting in six hospital pairs. Drone travel times were estimated and compared against road transport times obtained from the Google Routes API, incorporating one week of traffic data to capture daily and weekend variability. The results show substantial advantages of aerial transport, with time savings ranging from 2 to 26 min, equivalent to 35–58% compared to road transport. Drones consistently ensured deliveries within 15 min, outperforming regular cars (39%) and ambulances or motorcycles in highly congested periods. Sensitivity analysis confirms their reliability in scenarios with strict time constraints, especially under 15 min. These findings demonstrate that drones reduce travel times and improve predictability, providing a robust evidence base for policymakers and regulators to advance U-space integration in healthcare logistics. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as promising solutions to overcome the shortcomings of traditional highway-monitoring approaches. UAVs have been used extensively for highway traffic monitoring, infrastructure inspection, safety analysis, and environmental management. This review summarizes the latest applications, contributions, and challenges of UAV technology in highway systems, highlighting their transformative impacts on traffic monitoring, infrastructure inspection, and safety assessment. Several UAV-based highway traffic datasets significantly improve research in traffic behavior analysis and automated driving system validation. The integration of UAVs with advanced technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and 5G, further enhances their capabilities, enabling enhanced real-time analytics and better decision-making support. Addressing ethical, regulatory, and social implications through transparent governance and privacy-preserving technologies is essential for sustainable deployment. Full article

Unmanned aerial vehicle (UAV)-based object detection shows promising potential in intelligent transportation and disaster response. However, detecting small targets remains challenging due to inherent limitations (long-distance and low-resolution imaging) and environmental interference (complex backgrounds and occlusions). To address these issues, this paper proposes an enhanced small target detection model, DDF-YOLO, which achieves higher detection performance. First, a dynamic feature extraction module (C2f-DCNv4) employs deformable convolutions to effectively capture features from irregularly shaped objects. In addition, a dynamic upsampling module (DySample) optimizes multi-scale feature fusion by combining shallow spatial details with deep semantic features, preserving critical low-level information while enhancing generalization across scales. Finally, to balance rapid convergence with precise localization, an adaptive Focaler-ECIoU loss function dynamically adjusts training weights based on sample quality during bounding box regression. Extensive experiments on VisDrone2019 and UAVDT benchmarks demonstrate DDF-YOLO’s superiority. Compared to YOLOv8n, our model achieves gains of 8.6% and 4.8% in mAP50, along with improvements of 5.0% and 3.3% in mAP50-95, respectively. Furthermore, it exhibits superior efficiency, requiring only 7.3 GFLOPs and attaining an inference speed of 179 FPS. These results validate the model’s robustness for UAV-based detection, particularly in small-object scenarios. Full article

Urban Air Mobility (UAM) has the potential to revolutionize urban transportation, largely with the deployment of Unmanned Aerial Vehicles (UAVs), commonly known as drones. After an initial stage focused on technology requirements, research is now shifting toward investigating operational requirements, which are unavoidably affected by urban characteristics. This study aims to explore the implementation of UAM services within urban environments by mapping the current scientific landscape from a city-focused perspective. Following a systematic search procedure, a bibliometric analysis was conducted on studies published between 2010 and 2024, examining over 350 articles that address UAM and urban-related topics. Trends in publication volume and scientific impact were analysed, along with influential manuscripts, collaborations, and leading countries in the field. Through a keyword co-occurrence analysis, five main research themes were identified: air traffic management, risk assessment, environmental factors (wind and noise), and vertiport location. These themes were further explored through a scoping review to assess current research and emerging directions. The findings highlight that urban characteristics are not just operational constraints but also fundamental elements that shape UAM strategies, influencing UAV path planning, safety, environmental constraints, and infrastructure design. Future research directions include the development of urban digital twins, comprehensive urban spatial databases, and multi-objective optimization frameworks to support the effective implementation of UAM into cities. Full article