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Editorial: Special Issue "Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking"
2022
Sensors
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In the last decade, the behavior of mobile data users has completely changed [...] ...
The transmit power at the base station and the UAV was determined in advanced based on the availability of channel state information (CSI). ...
Adding mobile infrastructure or UABSs (Unmanned Aerial Base Stations)-i.e., a base station mounted on a UAV (Unmanned Aerial Vehicle) or drone-would provide a huge added value to the network. ...
doi:10.3390/s22124458
pmid:35746240
pmcid:PMC9228075
fatcat:mgzmdsgcd5dlzlgd3vkui7mvme
Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity
2021
Sensors
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Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced ...
Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. ...
Several machine learning approaches are tested to determine the users' positions. ...
doi:10.3390/s21134618
fatcat:yo4rytvhbnce7k433gl5gzbifu
Special Issue on Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
2019
Applied Sciences
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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have beenmaturing rapidly[ ...
Acknowledgments: This special issue would not be possible without the contributions of professional authors and reviewers, and the excellent editorial team of Applied Sciences. ...
to remote sensing; (3) Application of machine learning techniques to Global Positioning System (GPS); (4) Spatial analysis and geocomputation based on machine learning techniques; (5) Spatial prediction ...
doi:10.3390/app9122446
fatcat:3zbjs2lkhzbjplifdkwswyhsk4
Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms
[article]
2020
arXiv
pre-print
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To this end, this paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. ...
For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference. ...
The models of 3D CNN and 2D CNN are widely-adopted image-based models that have good performance for AQI scale inference tasks, and SVM is a popular machine learning model for general prediction applications ...
arXiv:2007.12004v1
fatcat:c2dgwdpncvfbxibjohrquhzhc4
Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models
2021
Sensors
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The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). ...
The second step is to generate the landslide-risk model based on machine learning approaches. ...
Determining the Optimal Parameters of Machine Learning Methods Experiments were established to identify the optimal batch size, epochs, and the number of nodes for individual machine learning model i.e ...
doi:10.3390/s21134620
fatcat:75iudbyhdrd4rcoy5nuwgzieue
Detection and Analysis of Oil Spill using Image Processing
2022
International Journal of Advanced Computer Science and Applications
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In the training model part, a machine learning model is applied, which is one of the fastest and most accurate methods, integrated inside PipelineMLML. ...
In order to mitigate and manage oil spill impacts, an unmanned aerial vehicle has proven to be a valuable tool in mitigating and managing incidents. ...
The information technology era has always been important for different industries, therefore, software systems based on machine learning models have been developed to perform object detection in the past ...
doi:10.14569/ijacsa.2022.0130445
fatcat:7iny3z2gejdhxbbsxrfdqibwxm
Efficient 3D Aerial Base Station Placement Considering Users Mobility by Reinforcement Learning
[article]
2018
arXiv
pre-print
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The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. ...
This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. ...
In the latter system, the position of the aerial-BS determined by Q-learning. This shows that overall, the given method improves the SINR parameter of system. ...
arXiv:1801.07472v2
fatcat:24striqi7jhkderdjpcwqkcsse
Communications and Networking Technologies for Intelligent Drone Cruisers
[article]
2019
arXiv
pre-print
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The drone-cruiser base station can overcome the communications problem for three types of flash crowds, such as in stadiums, parades, and large plaza so that an appropriate number of aerial base stations ...
This work needs to overcome the following five technical challenges: innovative design of drone-cruisers for the long-time hovering, crowd estimation and prediction, rapid 3D wireless channel learning ...
[7] used machine learning algorithms to solve the problem of indoor positioning and NLOS channel identification. ...
arXiv:1910.05309v1
fatcat:pgq4punhcnbwriagblrpijareq
Hybrid Sensing Platform for IoT-Based Precision Agriculture
2022
Future Internet
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In this paper, we propose a Hybrid Sensing Platform (HSP) for PA—an IoT platform that combines a small number of ground-based sensors with aerial sensors to improve aerial data accuracy and at the same ...
Existing Internet of Things (IoT) solutions for PA are typically divided in terms of their use of either aerial sensing using unmanned aerial vehicles (UAVs) or ground-based sensing approaches. ...
In addition, as more data collection is performed, supervised machine learning can be used to determine the expected prediction error rate and to understand the lowest obtainable prediction error level ...
doi:10.3390/fi14080233
fatcat:q2o32gyuwbgdxdonoyxgmxk7ni
Artificial Intelligence Aided Next-Generation Networks Relying on UAVs
[article]
2020
arXiv
pre-print
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In the AI-enabled UAV-aided wireless networks (UAWN), multiple UAVs are employed as aerial base stations, which are capable of rapidly adapting to the dynamic environment by collecting information about ...
the users' position and tele-traffic demands, learning from the environment and acting upon the feedback received from the users. ...
Multi-UAV are employed as aerial base stations for supporting the users in a
particular area in which the existing terrestrial networks may be limited in capacity or coverage. ...
arXiv:2001.11958v1
fatcat:i35weka7wndghp3folyzsd4mi4
Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices
2019
Sensors
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In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. ...
In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. ...
I would also like to pay a deep sense of gratitude to all CAIAS (Center for Artificial Intelligence and Autonomous System) lab members for their support and CAIAS lab for providing me all the facilities ...
doi:10.3390/s19153371
fatcat:htf3ilkn3ndrxjzoaos6vj6fc4
DRaGon: Mining Latent Radio Channel Information from Geographical Data Leveraging Deep Learning
[article]
2021
arXiv
pre-print
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In this paper, we present Deep RAdio channel modeling from GeOinformatioN (DRaGon) as a novel machine learning-enabled method for automatic generation of Radio Environmental Maps (REMs) from geographical ...
However, as demonstrated by recent results, there remains an untapped potential to innovate this research field by enriching model-based approaches with machine learning techniques. ...
This information provides the foundation for a multi-MNO REM that allows to dynamically determine the serving MNO based on the current user position. ...
arXiv:2112.07941v1
fatcat:pgw3jpsexzdjxl7nevvwztwlrq
Deep Learning for Rain Fade Prediction in Satellite Communications
[article]
2021
arXiv
pre-print
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Experiments show that the proposed DL architecture outperforms current state-of-the-art machine learning-based algorithms in rain fade forecasting in the near and long term. ...
In this paper, a deep learning (DL)-based architecture is proposed that forecasts future rain fade using satellite and radar imagery data as well as link power measurements. ...
Recently, machine learning (ML)-based rain attenuation prediction is proposed in several works [13] - [15] . ...
arXiv:2110.00695v1
fatcat:6dagoxpnkvfgbh4clkuhspdica
Keywords
2021
2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
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Metrics Quantitative Comparisons of Edge Based and Region Based Feature Detection in Digital Aerial Imagery Analysis K nearest neighbor A Comparative Study with Different Machine Learning Algorithms for ...
Machine Learning Algorithms for Diabetes Disease Prediction Diabetic foot Temperature prediction based on ANN linear regression with an LWIR sensor for the study of diabetic foot Diffusive coupling Autonomous ...
doi:10.1109/cce53527.2021.9633101
fatcat:7ffdhuyqevhmpawcjajs2rgniq
Self-Evolving Integrated Vertical Heterogeneous Networks
[article]
2021
arXiv
pre-print
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Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. ...
6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services ...
In [141] , to efficiently predict the success and failure rates in an aerial network, two learning algorithms based on linear regression (LR) and support vector machine (SVM) was employed. ...
arXiv:2106.13950v2
fatcat:z3a6vig6fza2bmnll4fodgczie
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