2区地球科学

Drones Pub Date : 2023-10-09 DOI: 10.3390/drones7100628

Yueling Che, Zeyu Zhao, Sheng Luo, Kaishun Wu, Lingjie Duan, Victor C. M. Leung

{"title":"UAV-Aided Wireless Energy Transfer for Sustaining Internet of Everything in 6G","authors":"Yueling Che, Zeyu Zhao, Sheng Luo, Kaishun Wu, Lingjie Duan, Victor C. M. Leung","doi":"10.3390/drones7100628","DOIUrl":"https://doi.org/10.3390/drones7100628","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Use of UAVs for Morphological Coastal Change Monitoring—A Bibliometric Analysis 利用无人机监测海岸形态变化——文献计量学分析

2区地球科学

Drones Pub Date : 2023-10-09 DOI: 10.3390/drones7100629

Jorge Novais, António Vieira, António Bento-Gonçalves, Sara Silva, Saulo Folharini, Tiago Marques

引用次数: 0

A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes 基于分层区块链的无人机集群节点信任度量方法

2区地球科学

Drones Pub Date : 2023-10-08 DOI: 10.3390/drones7100627

Jinxin Zuo, Ruohan Cao, Jiahao Qi, Peng Gao, Ziping Wang, Jin Li, Long Zhang, Yueming Lu

{"title":"A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes","authors":"Jinxin Zuo, Ruohan Cao, Jiahao Qi, Peng Gao, Ziping Wang, Jin Li, Long Zhang, Yueming Lu","doi":"10.3390/drones7100627","DOIUrl":"https://doi.org/10.3390/drones7100627","url":null,"abstract":"In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135251023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Constraint Guidance and Maneuvering Penetration Strategy via Meta Deep Reinforcement Learning 基于元深度强化学习的多约束制导机动突防策略

2区地球科学

Drones Pub Date : 2023-10-08 DOI: 10.3390/drones7100626

Sibo Zhao, Jianwen Zhu, Weimin Bao, Xiaoping Li, Haifeng Sun

引用次数: 1

Integrating a UAV System Based on Pixhawk with a Laser Methane Mini Detector to Study Methane Emissions 基于Pixhawk的无人机系统与激光甲烷微型探测器集成研究甲烷排放

2区地球科学

Drones Pub Date : 2023-10-07 DOI: 10.3390/drones7100625

Timofey Filkin, Iliya Lipin, Natalia Sliusar

{"title":"Integrating a UAV System Based on Pixhawk with a Laser Methane Mini Detector to Study Methane Emissions","authors":"Timofey Filkin, Iliya Lipin, Natalia Sliusar","doi":"10.3390/drones7100625","DOIUrl":"https://doi.org/10.3390/drones7100625","url":null,"abstract":"This article describes the process of integrating one of the most commonly used laser methane detectors, the Laser Methane mini (LMm), and a multi-rotor unmanned aerial vehicle (UAV) based on the Pixhawk flight controller to create an unmanned aerial system designed to detect methane leakages from the air. The integration is performed via the LaserHub+, a newly developed device which receives data from the laser methane detector, decodes it and transmits it to the flight controller with the protocol used by the ArduPilot platform for laser rangefinders. The user receives a single data array from the UAV flight controller that contains both the values of the methane concentrations measured by the detector, and the co-ordinates of the corresponding measurement points in three-dimensional space. The transmission of data from the UAV is carried out in real time. It is shown in this project that the proposed technical solution (the LaserHub+) has clear advantages over not only similar serial commercial solutions (e.g., the SkyHub complex by SPH Engineering) but also experimental developments described in the scientific literature. The main reason is that LaserHub+ does not require a deep customization of the methane detector or the placement of additional complex devices on board the UAV. Tests using it were carried out in aerial gas surveys of a number of municipal solid waste disposal sites in Russia. The device has a low cost and is easy for the end user to assemble, connect to the UAV and set up. The authors believe that LaserHub+ can be recommended as a mass solution for aerial surveys of methane sources. Information is provided on the approval of LaserHub+ for aerial gas surveys of a number of Russian municipal waste disposal facilities.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135301588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study 基于深度学习的无人机图像杂草检测:比较研究

2区地球科学

Drones Pub Date : 2023-10-07 DOI: 10.3390/drones7100624

Tej Bahadur Shahi, Sweekar Dahal, Chiranjibi Sitaula, Arjun Neupane, William Guo

{"title":"Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study","authors":"Tej Bahadur Shahi, Sweekar Dahal, Chiranjibi Sitaula, Arjun Neupane, William Guo","doi":"10.3390/drones7100624","DOIUrl":"https://doi.org/10.3390/drones7100624","url":null,"abstract":"Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information about their comparative study for optimal model selection in terms of performance in this field. Identifying such a model helps the agricultural community make the best use of technology. As such, we perform a comparative study of cutting-edge AI deep learning-based segmentation models for weed detection using an RGB image dataset acquired with UAV, called CoFly-WeedDB. For this, we leverage AI segmentation models, ranging from SegNet to DeepLabV3+, combined with five backbone convolutional neural networks (VGG16, ResNet50, DenseNet121, EfficientNetB0 and MobileNetV2). The results show that UNet with EfficientNetB0 as a backbone CNN is the best-performing model compared with the other candidate models used in this study on the CoFly-WeedDB dataset, imparting Precision (88.20%), Recall (88.97%), F1-score (88.24%) and mean Intersection of Union (56.21%). From this study, we suppose that the UNet model combined with EfficientNetB0 could potentially be used by the concerned stakeholders (e.g., farmers, the agricultural industry) to detect weeds more accurately in the field, thereby removing them at the earliest point and increasing crop yields.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135300610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Real-Time On-Drone Pedestrian Tracking in 4K Inputs 在4K输入中实现实时无人机行人跟踪

2区地球科学

Drones Pub Date : 2023-10-06 DOI: 10.3390/drones7100623

Chanyoung Oh, Moonsoo Lee, Chaedeok Lim

引用次数: 0

Dynamic Offloading in Flying Fog Computing: Optimizing IoT Network Performance with Mobile Drones 飞行雾计算中的动态卸载:利用移动无人机优化物联网网络性能

2区地球科学

Drones Pub Date : 2023-10-05 DOI: 10.3390/drones7100622

Wei Min, Abdukodir Khakimov, Abdelhamied A. Ateya, Mohammed ElAffendi, Ammar Muthanna, Ahmed A. Abd El-Latif, Mohammed Saleh Ali Muthanna

{"title":"Dynamic Offloading in Flying Fog Computing: Optimizing IoT Network Performance with Mobile Drones","authors":"Wei Min, Abdukodir Khakimov, Abdelhamied A. Ateya, Mohammed ElAffendi, Ammar Muthanna, Ahmed A. Abd El-Latif, Mohammed Saleh Ali Muthanna","doi":"10.3390/drones7100622","DOIUrl":"https://doi.org/10.3390/drones7100622","url":null,"abstract":"The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It leverages drones with computing capabilities (e.g., fog nodes), enabling data processing and storage closer to the network edge. This introduces various benefits to IoT networks compared to deploying traditional static edge computing paradigms, including coverage improvement, enabling dense deployment, and increasing availability and reliability. However, drones’ dynamic and mobile nature poses significant challenges in task offloading decisions to optimize resource utilization and overall network performance. This work presents a novel offloading model based on dynamic programming explicitly tailored for flying fog-based IoT networks. The proposed algorithm aims to intelligently determine the optimal task assignment strategy by considering the mobility patterns of drones, the computational capacity of fog nodes, the communication constraints of the IoT devices, and the latency requirements. Extensive simulations and experiments were conducted to test the proposed approach. Our results revealed significant improvements in latency, availability, and the cost of resources.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135484123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Intelligent Resource Allocation Using an Artificial Ecosystem Optimizer with Deep Learning on UAV Networks 基于深度学习的无人机网络人工生态优化器的智能资源分配

2区地球科学

Drones Pub Date : 2023-10-03 DOI: 10.3390/drones7100619

Ahsan Rafiq, Reem Alkanhel, Mohammed Saleh Ali Muthanna, Evgeny Mokrov, Ahmed Aziz, Ammar Muthanna

{"title":"Intelligent Resource Allocation Using an Artificial Ecosystem Optimizer with Deep Learning on UAV Networks","authors":"Ahsan Rafiq, Reem Alkanhel, Mohammed Saleh Ali Muthanna, Evgeny Mokrov, Ahmed Aziz, Ammar Muthanna","doi":"10.3390/drones7100619","DOIUrl":"https://doi.org/10.3390/drones7100619","url":null,"abstract":"An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Condition Monitoring of the Torque Imbalance in a Dual-Stator Permanent Magnet Synchronous Motor for the Propulsion of a Lightweight Fixed-Wing UAV 轻型固定翼无人机双定子永磁同步电机转矩不平衡状态监测

2区地球科学

Drones Pub Date : 2023-10-03 DOI: 10.3390/drones7100618

Aleksander Suti, Gianpietro Di Rito, Giuseppe Mattei

{"title":"Condition Monitoring of the Torque Imbalance in a Dual-Stator Permanent Magnet Synchronous Motor for the Propulsion of a Lightweight Fixed-Wing UAV","authors":"Aleksander Suti, Gianpietro Di Rito, Giuseppe Mattei","doi":"10.3390/drones7100618","DOIUrl":"https://doi.org/10.3390/drones7100618","url":null,"abstract":"This paper deals with the development of a model-based technique to monitor the condition of torque imbalances in a dual-stator permanent magnet synchronous motor for UAV full-electric propulsion. Due to imperfections, degradations or uncertainties, the torque split between power lines can deviate from the design, causing internal force-fighting and reduced efficiency. This study demonstrates that, by only elaborating the measurements of speed and direct/quadrature currents of the stators during motor acceleration/deceleration, online estimations of demagnetization and electrical angle misalignment can be obtained, thus permitting the evaluation of the imbalance and total torque of the system. A relevant outcome is that the technique can be used for developing both signal-based and model-based monitoring schemes. Starting from physical first-principles, a nonlinear model of the propulsion system, including demagnetization and electrical angle misalignment, is developed in order to analytically derive the relationships between monitoring inputs (currents and speed) and outputs (degradations). The model is experimentally validated using a system prototype characterized by asymmetrical demagnetization and electrical angle misalignment. Finally, the monitoring effectiveness is assessed by simulating UAV flight manoeuvres with the experimentally validated model: injecting different levels of degradations and evaluating the torque imbalance.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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