Drones for Flood Monitoring, Mapping and Detection: A Bibliometric Review

Drones, Journal Year: 2023, Volume and Issue: 7(1), P. 32 - 32

Published: Jan. 1, 2023

Floods are one of the most often occurring and damaging natural hazards. They impact society on a massive scale result in significant damages. To reduce floods, needs to keep benefiting from latest technological innovations. Drones equipped with sensors algorithms (e.g., computer vision deep learning) have emerged as potential platform which may be useful for flood monitoring, mapping detection activities more efficient way than current practice. better understand scope recent trends domain drones management, we performed detailed bibliometric analysis. The intent performing analysis waws highlight important research trends, co-occurrence relationships patterns inform new researchers this domain. was terms performance (i.e., publication statistics, citations top publishing countries, journals, institutions, publishers Web Science (WoS) categories) science by country, keyword co-occurrences, co-authorship, co-citations bibliographic coupling) total 569 records extracted WoS duration 2000–2022. VOSviewer open source tool has been used generating network maps. Subjective discussions results explain obtained In end, review 28 publications subjected process-driven context management. active areas were also identified future regard use activities.

Language: Английский

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Elliptic Curve Cryptography-Based Scheme for Secure Signaling and Data Exchanges in Precision Agriculture

Sustainability, Journal Year: 2023, Volume and Issue: 15(13), P. 10264 - 10264

Published: June 28, 2023

Precision agriculture encompasses automation and application of a wide range information technology devices to improve farm output. In this environment, smart collect exchange massive number messages with other servers over public channels. Consequently, farming is exposed diverse attacks, which can have serious consequences since the sensed data are normally processed help determine agricultural field status facilitate decision-making. Although myriad security schemes has been presented in literature curb these challenges, they either poor performance or susceptible attacks. paper, an elliptic curve cryptography-based scheme presented, shown be formally secure under Burrows–Abadi–Needham (BAN) logic. addition, it semantically demonstrated offer user privacy, anonymity, unlinkability, untraceability, robust authentication, session key agreement, secrecy does not require deployment verifier tables. withstand side-channeling, physical capture, eavesdropping, password guessing, spoofing, forgery, replay, hijacking, impersonation, de-synchronization, man-in-the-middle, privileged insider, denial service, stolen device, known session-specific temporary terms performance, proposed protocol results 14.67% 18% reductions computation communication costs, respectively, 35.29% improvement supported features.

Language: Английский

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Global Models of Smart Cities and Potential IoT Applications: A Review

IoT, Journal Year: 2023, Volume and Issue: 4(3), P. 366 - 411

Published: Aug. 31, 2023

As the world becomes increasingly urbanized, development of smart cities and deployment IoT applications will play an essential role in addressing urban challenges shaping sustainable resilient environments. However, there are also to overcome, including privacy security concerns, interoperability issues. Addressing these requires collaboration between governments, industry stakeholders, citizens ensure responsible equitable implementation technologies cities. The offers a vast array possibilities for city applications, enabling integration various devices, sensors, networks collect analyze data real time. These span across different sectors, transportation, energy management, waste public safety, healthcare, more. By leveraging technologies, can optimize their infrastructure, enhance resource allocation, improve quality life citizens. In this paper, eight global models have been proposed guide provide frameworks standards planners stakeholders design deploy solutions effectively. We detailed evaluation based on nine metrics. implement mentioned, recommendations stated overcome challenges.

Language: Английский

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Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 19941 - 19968

Published: Jan. 1, 2023

Non terrestrial networks (NTN) involving 'in the sky' objects such as low-earth orbit satellites, high altitude platform systems (HAPs) and Unmanned Aerial Vehicles (UAVs) are expected to be integral components of next generation cellular systems. With deployment 5G services beyond, NTNs leveraged assist aerial base stations in providing ubiquitous network connectivity service ground users or deployed connected network. NTN-aided wireless communication offers multiple benefits mobility, flexibility, resistance physical attacks wide coverage. However, due their limited resources current design that do not account for users, other restrictions requirements, available power storage on high-throughput resource allocation, location station flight trajectory UAVs need intelligently controlled satisfy various objectives both from an overall perspectives. To achieve this, many works have explored Reinforcement Learning (RL) techniques allow platforms non-terrestrial learn past observations some optimal control policy. In this paper differently prior surveys, we contribute a comprehensive review required by been solved using RL formulations. We provide up-to-date overview latest applications different aspects. The survey focuses Markov Decision Process (MDP) formulations terms states, actions, rewards. synthesize taxonomy surveyed literature representation usages communications. A qualitative analysis level realism achieved presented is provided based several factors pertain simulation environment, setting, channel assumption, energy considerations. also curate list challenges remain considered research community order more efficient deployments close simulation-to-reality gap.

Language: Английский

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Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 130860 - 130887

Published: Jan. 1, 2023

The combination of drones and Intelligent Reflecting Surfaces (IRS) have emerged as potential technologies for improving the performance six Generation (6G) communication networks by proactively modifying wireless through smart signal reflection manoeuvre control. By deploying IRS on drones, it becomes possible to improve coverage reliability network while reducing energy consumption costs. Furthermore, integrating with Federated Learning (FL) can further boost drone enabling collaborative learning among multiple leading better more efficient decision-making holding great promise 6G networks. Therefore, we present a novel framework FL meets in 6G. In this framework, IRS-equipped swarm are deployed form distributed network, where techniques used collaborate process optimize coefficients each drone-IRS. This allows adapt changing environments quality services. Integrating into offers several advantages over traditional networks, including rapid deployment emergencies or disasters, improved services, increased accessibility remote areas. Finally, highlight challenges opportunities researchers interested We also help drive innovation developing

Language: Английский

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An Improved Artificial Potential Field UAV Path Planning Algorithm Guided by RRT Under Environment-aware Modeling: Theory and Simulation

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 12080 - 12097

Published: Jan. 1, 2024

Unmanned Aerial Vehicles (UAVs) have been extensively researched and used in civil military applications due to their effectiveness flexibility. However, when identifying obstacles avoiding them, most of the existing path planning methods fail accurately perceive environment, such as without considering differences between obstacles, which leads low timeliness easy fall into a local minimum. In this work, an improved artificial potential field UAV algorithm (G-APF) guided by rapidly-exploring random tree (RRT) based on environment-aware model is designed overcome limitations traditional methods. The can different objects environment through addition supervised modeling unsupervised planning. Specifically, YOLOv8 establish flight model, adaptive optimal threat distance calculation module construct repulsive field. Secondly, improve global awareness we first use G-APF plan rough environment. Then, initially generated trajectory replanned building attractive combining it with Finally, problems minimum target unreachability oscillation (APF) are solved G-APF. Experiments regions performed demonstrate efficiency proposed approach.

Language: Английский

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Multi-UAV networks for disaster monitoring: challenges and opportunities from a network perspective

Drone Systems and Applications, Journal Year: 2024, Volume and Issue: 12, P. 1 - 28

Published: Jan. 1, 2024

Disasters, whether natural or man-made, demand rapid and comprehensive responses. Unmanned aerial vehicles (UAVs), drones, have become essential in disaster scenarios, serving as crucial communication relays areas with compromised infrastructure. They establish temporary networks, aiding coordination among emergency responders facilitating timely assistance to survivors. Recent advancements sensing technology transformed response by combining the collaborative power of these networks real-time data processing. However, challenges remain consider for monitoring applications, particularly deployment strategies, processing, routing, security. Extensive research is refine ad hoc networking solutions, enhancing agility effectiveness systems. This article explores various aspects, including network architecture, formation protocols, security concerns multi-UAV monitoring. It also examines potential enabling technologies like edge computing artificial intelligence bolster performance Further, provides a detailed overview key open issues, outlining prospects evolving field response.

Language: Английский

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AI-Based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities

Pattern Recognition and Image Analysis, Journal Year: 2024, Volume and Issue: 34(1), P. 169 - 186

Published: March 1, 2024

Language: Английский

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Drone-DETR: Efficient Small Object Detection for Remote Sensing Image Using Enhanced RT-DETR Model

Sensors, Journal Year: 2024, Volume and Issue: 24(17), P. 5496 - 5496

Published: Aug. 24, 2024

Performing low-latency, high-precision object detection on unmanned aerial vehicles (UAVs) equipped with vision sensors holds significant importance. However, the current limitations of embedded UAV devices present challenges in balancing accuracy and speed, particularly analysis remote sensing images. This challenge is pronounced scenarios involving numerous small objects, intricate backgrounds, occluded overlaps. To address these issues, we introduce Drone-DETR model, which based RT-DETR. overcome difficulties associated detecting objects reducing redundant computations arising from complex backgrounds ultra-wide-angle images, propose Effective Small Object Detection Network (ESDNet). network preserves detailed information about reduces computations, adopts a lightweight architecture. Furthermore, Enhanced Dual-Path Feature Fusion Attention Module (EDF-FAM) within neck network. module specifically designed to enhance network's ability handle multi-scale objects. We employ dynamic competitive learning strategy model's capability efficiently fuse features. Additionally, incorporate P2 shallow feature layer ESDNet into small-object features, thereby enhancing detection. Experimental results indicate that model achieves an mAP

Language: Английский

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SOD-YOLOv8—Enhancing YOLOv8 for Small Object Detection in Aerial Imagery and Traffic Scenes

Sensors, Journal Year: 2024, Volume and Issue: 24(19), P. 6209 - 6209

Published: Sept. 25, 2024

Object detection, as a crucial aspect of computer vision, plays vital role in traffic management, emergency response, autonomous vehicles, and smart cities. Despite the significant advancements object detecting small objects images captured by high-altitude cameras remains challenging, due to factors such size, distance from camera, varied shapes, cluttered backgrounds. To address these challenges, we propose detection YOLOv8 (SOD-YOLOv8), novel model specifically designed for scenarios involving numerous objects. Inspired efficient generalized feature pyramid networks (GFPNs), enhance multi-path fusion within integrate features across different levels, preserving details shallower layers improving accuracy. Additionally, introduce fourth layer effectively utilize high-resolution spatial information. The multi-scale attention module (EMA) C2f-EMA further enhances extraction redistributing weights prioritizing relevant features. We powerful-IoU (PIoU) replacement CIoU, focusing on moderate quality anchor boxes adding penalty based differences between predicted ground truth bounding box corners. This approach simplifies calculations, speeds up convergence, SOD-YOLOv8 significantly improves surpassing widely used models various metrics, without substantially increasing computational cost or latency compared YOLOv8s. Specifically, it increased recall 40.1% 43.9%, precision 51.2% 53.9%, mAP0.5 40.6% 45.1%, mAP0.5:0.95 24% 26.6%. Furthermore, experiments conducted dynamic real-world scenes illustrated SOD-YOLOv8’s enhancements diverse environmental conditions, highlighting its reliability effective capabilities challenging scenarios.

Language: Английский

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