Search Results (498)

This study applies a dynamic strategic foresight to examine how Unmanned Aerial Systems (UAS)-based Advanced Air Mobility (AAM), supported by Advanced Communication Systems (ACS), can be integrated into a coherent System of Systems (SoS) for sustainable and effective Disaster Management (DM). These three domains (AAM, ACS, and DM) form a strongly coupled Internet of Things (IoT) triad within an integrated SoS. Using lessons learned from previous or running research projects of the contributing authors, i.e., SUDEM, REGUAS, 5G!Drones, and ETHER, the foresight identifies key enablers—including resilient 5G/6G communication architectures, interoperable data fusion frameworks, and UAS-supported situational awareness. It highlights structural challenges such as fragmented standards, limited cross-agency data integration, and gaps in ACS redundancy for emergency operations. The resulting roadmap outlines development priorities for ACS-enabled AAM, from unified communication protocols and hybrid TN-NTN architectures to education and capacity-building for digital-centric DM. Practically, the findings suggest that policymakers should prioritise harmonised regulatory frameworks for AAM-ACS interoperability and invest in global data exchange standards, while system designers should incorporate redundant communication layers and modular SoS architectures to ensure operational continuity under extreme conditions. Full article

Drone-based Urban Air Mobility (UAM) shows immense potential in urban logistics and emergency response; however, evidence regarding its systemic sustainability remains fragmented. In a systematic review using the PRISMA methodology, this study analyzes 301 core articles to construct an evaluation framework spanning environmental, economic, social, and systemic effectiveness dimensions. Given technical similarities, electric Vertical Take-off and Landing (eVTOL) findings are integrated to anticipate operational challenges. Results highlight a clear consensus: drone delivery is time-efficient in high-sensitivity scenarios, though noise, equity, and safety remain critical bottlenecks. Meanwhile, deep controversies persist across some dimensions. Environmental benefits are highly context-dependent, contingent on operating models, battery life cycles, and clean energy proportions from a Life Cycle Assessment (LCA) perspective. Economically, a mismatch between high costs and low willingness to pay (WTP) necessitates optimized pricing strategies. Socially, public acceptance is sensitive to the balance between perceived benefits and risks. Furthermore, systemic effectiveness depends on the coupling between vertiports and ground infrastructure. Concluding that sustainable drone-based UAM is a multistakeholder systemic endeavor, we urge future research to prioritize LCA, pricing strategies, public acceptance surveys, and integrated air-ground coordination to resolve controversies and foster sustainable systems. Full article

Drone noise-induced human annoyance is emerging as one of the main barriers to socially acceptable large-scale urban air mobility (UAM) operations, which have the potential to revolutionize urban transportation systems in the next few decades. This paper investigates the state-of-the-art technology in the assessment of drone noise and its impact on individuals, focusing on measurement and evaluation methodologies, as well as subjective evaluations. Various acoustic metrics are reviewed to characterize drone noise, including sound pressure levels, spectral analysis, and psychoacoustic parameters such as loudness and annoyance. Preliminary experimental investigations to identify key frequencies and tonal components that significantly contribute to drone noise-induced public annoyance are also discussed. Interdisciplinary approaches integrating pure technical acoustics, human perception, and subjectivity emerge as promising solutions for a comprehensive understanding of drone noise effects. Finally, a preliminary framework for drone noise assessment towards noise-aware UAM operations is proposed. Full article

In today’s densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework’s effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments. Full article

The sixth-generation (6G) mobile networks are envisioned to deliver seamless three-dimensional(3D) coverage from ground to sky and vice versa. In parallel, aerial corridors are emerging to elevate ground-based transportation into the air, enabling smart air mobility for unmanned aerial vehicles (UAVs). The convergence of this intelligent transportation system (ITS) with 6G introduces new challenges: how to ensure reliable, efficient connectivity within aerial corridors, and how these corridors can serve as foundational sky infrastructure to advance the 6G ecosystem. This paper presents a comprehensive survey that systematically presents aerial corridors as integrated 6G sky infrastructure, unifying corridor geometry, network architecture, channel modeling, and key enabling technologies within a single framework. It conceptualizes the aerial corridor as a tube-shaped, multi-lane, bidirectional structure to manage drone-based roles, including user equipment (UE), base stations (BS), and communication relays. To support this vision, key enablers such as air-to-ground channel modeling and integrated sensing and communication (ISAC) are investigated. The proposed infrastructure aligns with the IMT-2030 vision, supporting machine-type communication, ubiquitous connectivity, and immersive services in regulated aerial space. Full article

Uncrewed aerial systems (UASs) have diverse applications in natural environments, yet their deployment can inadvertently disturb wildlife. This PRISMA-guided systematic review synthesised 39 studies (2015–2025) encompassing birds, mammals, and marine taxa to identify UAS operational drivers of disturbance. We applied a Factors–Responses–Consequences (F–R–C) framework linking UAS operational characteristics, observed wildlife responses, and ecological consequences. Three patterns emerged: (i) Factors: Contextual and operational conditions such as flight altitude, noise, and approach direction interact with species-specific sensitivities to shape disturbance potential. (ii) Responses: Physiological measures (e.g., elevated heart rates) often reveal hidden stress not evident from behaviour alone. (iii) Consequences: Short-term effects may accumulate into long-term impacts on health, reproduction, and habitat use. These findings highlight the need for species- and context-specific flight envelopes rather than solely uniform altitude limits. By structuring existing evidence within the F–R–C framework, this synthesis provides a transferable foundation for UAS mission planning, drone development, operational decision-making, ethical practice, and environmental impact assessment in conservation and wildlife-management contexts. As all screening and data extraction were conducted by a single reviewer, the findings should be interpreted with appropriate caution pending independent validation. Full article

The accelerated growth of e-commerce has intensified the dual challenges of weak infrastructure and carbon emission pressures in last-mile delivery for rural and mountainous regions. As the World Bank calls for integrating carbon market development into national strategies, Truck-Drone Collaborative Delivery (TDCD) has emerged as a critical sustainable solution. However, existing research often overlooks the strict airspace regulations in sensitive border areas. Therefore, this paper proposes a Vehicle Routing Problem with Drones and Mobile Base Stations (VRPDBS) model that explicitly incorporates airspace constraints and mobile hub deployment. We introduce a quantified “Regional Flyability Factor” ($f_k$) to measure the impact of airspace restrictions on routing decisions and solve the problem using a hybrid metaheuristic algorithm. A case study based on real-world data from the Yanbian Korean Autonomous Prefecture reveals that strict airspace compliance imposes an absolute delivery delay of 4–5 h and an operational cost premium of up to 15%, an impact that can be effectively mitigated through a mobile base station mediation strategy. More importantly, multi-scenario sensitivity analysis under carbon price uncertainty indicates that although truck-dominant modes are cost-effective at current low carbon prices, drone-intensive configurations demonstrate superior economic robustness and environmental performance under high carbon price scenarios. This study not only provides a technical framework for green logistics planning in complex airspace but also offers strategic decision support for logistics enterprises to navigate long-term climate policy risks. Full article

Unmanned Aerial Vehicles (UAVs/drones) have emerged as transformative tools for coastal environmental monitoring, yet the field’s intellectual evolution and operational maturity remain incompletely characterized. This study employs citation network analysis via Litmaps to map the structure, consolidation, and knowledge diffusion patterns of coastal drone research from 2013 to 2024. A corpus of 47 influential articles was identified through systematic citation connectivity criteria, revealing three distinct phases: Seminal (≤2016), Consolidation (2017–2022), and Innovation (≥2023). Results demonstrate that foundational RGB photogrammetry protocols established in 2013–2016 remain standard references in 2024, indicating methodological maturity rather than obsolescence. However, substantial geographic concentration exists (Mediterranean institutions dominate early development), with application imbalances: temporal monitoring (46.8%) dominates while policy-relevant erosion/risk assessment comprises only 8.5%. Despite documented technical adequacy (sub-centimeter accuracy, 70–80% cost reduction vs. alternatives), the transition to operational coastal programs faces institutional rather than technological barriers. The analysis concludes that realizing UAV operational potential requires coordinated institutional development across management agencies, research institutions, capacity-building programs, and equitable data governance frameworks. Full article

Drone logistics is emerging as a key trend in future delivery systems due to its efficiency. However, current benefit assessments are often one-dimensional, focusing on single-node modes and overlooking load variations and charging processes in continuous multi-node delivery. To address this gap, this paper develops an integrated assessment framework across three dimensions: environment, economy, and time. Based on lifecycle emissions and total cost of ownership, a structured time-performance indicator, time value, is introduced. By incorporating an energy consumption model that accounts for dynamic loads and a charging model that considers charging behavior, an improved genetic algorithm is designed to optimize large-scale urban drone dispatch. Furthermore, a comparative sensitivity analysis with electric trucks quantifies the effects of market demand, charging strategy and technological progress. Results show that, under the modeled scenarios and parameter assumptions, electric trucks remain preferable in the short term, while drones demonstrate stronger long-term potential. Enterprises should align drone and truck deployment with demand and manage charging dynamically, while governments should combine initial subsidies with long-term guidance and systemic support to enable large-scale drone logistics adoption. Full article

The emergence of Gaussian Splatting has ushered in a new phase in large-scale scene geometry reconstruction. Existing research in this field primarily focuses on computational efficiency and often neglects the effective utilization of inherent structural geometric priors. This paper introduces a shape-optimized Gaussian Splatting method based on the Manhattan World Assumption for precise geometric reconstruction from drone imagery. By leveraging the constraints provided by this assumption, a geometry-driven optimization mechanism is developed to guide the deformation and distribution of each Gaussian splat. This ensures improved alignment with the dominant structural directions of the scene. Additionally, dense point clouds are generated from the optimized Gaussian representation, making them more suitable for downstream tasks. The proposed approach maintains high visual fidelity while significantly improving geometric accuracy. Extensive experiments on multiple large-scale scene datasets, including a new benchmark for Jinan City, demonstrate that the method surpasses current state-of-the-art approaches in geometry-oriented evaluation metrics by a considerable margin. Full article

The rapid proliferation of low-altitude drones has led to increasingly congested and heterogeneous electromagnetic environments, posing significant challenges to fine-grained spectrum awareness and reliable drone management. Specific emitter identification (SEI), which exploits inherent hardware-dependent radio frequency fingerprints, provides an effective physical-layer solution for emitter-level discrimination. However, practical SEI systems often suffer from two critical issues: extremely limited labeled samples for newly emerging emitters and heterogeneous data distributions collected by geographically distributed receivers with mismatched label spaces. To address these challenges, this paper proposes a heterogeneous federated learning (HFL)-based framework for few-shot specific emitter identification (FS-SEI). The proposed framework decouples feature embedding learning from task-specific classification and enables collaborative representation learning across distributed receivers without sharing raw signal data. A metric learning-based training strategy is adopted, where only the feature embedding models are aggregated in the federated process, effectively alleviating the impact of label space mismatch by utilizing center loss and an improved triplet loss. Moreover, two federated optimization schemes, namely gradient averaging (GA) and model averaging (MA), are systematically investigated to analyze their effectiveness under fully heterogeneous settings. Extensive experiments conducted on a real-world dataset demonstrate that the proposed HFL framework significantly outperforms isolated local training. In particular, the GA-based scheme achieves a few-shot identification performance that closely approaches centralized learning while preserving data privacy and robustness against data heterogeneity. The results validate the effectiveness of the proposed approach for practical FS-SEI in low-altitude drone management scenarios. Full article

Drones have proven effective for acquiring aerial imagery, and when equipped with onboard analysis tools, they can automatically identify objects of interest. Neural-network methods for image analysis typically require large training datasets and substantial computational resources. By contrast, algorithmic techniques can detect objects using simple features, such as pixel colors, thereby reducing the need for extensive training and computational resources. Once trained, both types of system can analyze images in a short time. In our experiments, each approach has distinct strengths. The YOLO-based detector is more accurate for complex-shaped objects, such as trees, whereas the pixel-color approach performs better on sparser objects. This paper proposes YOLO-C3, a hybrid system designed for onboard drone image processing. By leveraging the strengths of both YOLO-based and pixel-based approaches, YOLO-C3 balances detection accuracy with estimation confidence. Trained on Mediterranean imagery dataset, the system is optimized for identifying natural objects, including citrus groves and trees. To assess the robustness of the image classifier, a K-fold cross-validation is performed. Compared to existing models, YOLO-C3 detects a wider range of natural objects with high accuracy and minimal latency, achieving a processing speed of 0.01 s per image. By performing object detection locally, drones can adapt their trajectories to support emergency response, helping to map safe corridors and locate buildings where people may be awaiting rescue after a natural disaster. Full article

In recent decades, the issue of marine litter has emerged as a major environmental concern, particularly with regard to plastic litter. The European Marine Strategy Framework Directive (MSFD, 2008/56/EC) requires member states to monitor marine litter along the coast, in the water, and on the seabed. Since 2014, beach litter monitoring has been carried out in Italy’s coastal regions, an activity entrusted to the Regional Environmental Agencies System (ARPA). ARPA Puglia is responsible for monitoring the Apulian coastline, and this paper summarizes the main results obtained from 2014 to 2023. The monitoring, which was repeated twice a year, consists of a visual census of litter items along a 100-meter stretch of beach in six different locations across the Puglia region. During this period, an average of 506 litter items per 100 m were observed on the six target beaches in Puglia, 90% of which were plastic ones. Among these, single-use plastic items (SUPs) accounted for 37%. A trend analysis reveals a decline in the aggregate quantity of marine litter on Apulian beaches over the past decade, a phenomenon that is particularly evident when considering the SUP subcategory in isolation. This decreasing trend is consistent with the overall pattern observed along the Italian coastline and the coastlines of European seas. Consequently, it can be hypothesized that an increase in awareness of the issue, in conjunction with the implementation of European Directive 2019/904 for the reduction in single-use plastics, has resulted in more responsible practices. However, further efforts are needed to achieve the goal of 20 litter items per 100 m of beach to attain the Good Environmental Status under the Marine Strategy Framework Directive. The findings emphasize the importance of constant monitoring of litter items along the shoreline, as well as the integration of new and alternative methodologies (e.g., drone surveys) to evaluate the efficacy of European regulatory implementation. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

Nano-aerial vehicles have emerged as pivotal tools in modern robotics research, offering a safe and scalable means to validate complex algorithms in resource-constrained environments. This scoping review synthesizes the extensive body of work on the Crazyflie nano-quadcopter and evaluates its potential for drone application development in research and academia. The Crazyflie quadcopter has emerged as a leading open-source platform for education and research in aerial robotics due to its modularity and low cost. Despite its rapid evolution, there is currently no comprehensive synthesis mapping its diverse applications across hardware configurations and research domains. This evaluation systematically charts existing research on the Crazyflie platform, outlining its development, identifying relevant hardware and software configurations, categorizing major research topics, and identifying knowledge gaps. A systematic search was performed on three major databases, Scopus, Web of Science and Google Scholar, for studies published between 2015 and 2025. The results indicate a rapid growth in scientific production, an involved research community and very diverse thematic approaches. Expansion decks for the Crazyflie have been analyzed together with their relation to specific fields of research. While control systems remain the primary research theme, there is a significant shift toward artificial intelligence and swarm robotics. Full article