Stochastic Geometric Analysis of Multiple Unmanned Aerial Vehicle-Assisted Communications Over Internet of Things

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.

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Internet of Things (IoT) for Monitoring Air Pollutants with an Unmanned Aerial Vehicle (UAV) in a Smart City

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Smart Technology ◽

10.1007/978-3-319-73323-4_11 ◽

2018 ◽

pp. 108-120 ◽

Cited By ~ 5

Author(s):

José-Isidro Hernández-Vega ◽

Elda Reyes Varela ◽

Natividad Hernández Romero ◽

Carlos Hernández-Santos ◽

Jonam Leonel Sánchez Cuevas ◽

...

Keyword(s):

Internet Of Things ◽

Unmanned Aerial Vehicle ◽

Smart City ◽

Air Pollutants ◽

Aerial Vehicle

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A review on non-terrestrial wireless technologies for Smart City Internet of Things

International Journal of Distributed Sensor Networks ◽

10.1177/1550147720936824 ◽

2020 ◽

Vol 16 (6) ◽

pp. 155014772093682

Author(s):

Aihua Wang ◽

Peisen Wang ◽

Xiaqing Miao ◽

Xiangming Li ◽

Neng Ye ◽

...

Keyword(s):

Internet Of Things ◽

Unmanned Aerial Vehicle ◽

Smart City ◽

Route Planning ◽

Dynamic Nature ◽

Research Directions ◽

Machine Type Communications ◽

Comprehensive Survey ◽

Aerial Vehicle ◽

High Dynamic

Smart City Internet of Things will become a fundamental infrastructure to support massive machine-type communications between the widely deployed sensors serving big cities. Since there exists many location constraints for the existing terrestrial Internet of Things, the non-terrestrial Internet of Things sheds light on breaking these limits. Therefore, this article conducts a comprehensive survey on non-terrestrial Internet of Things technologies for Smart City, which is an important complement to terrestrial Internet of Things. We first present the application scenarios of Internet of Things and point out where the existing terrestrial Internet of Things cannot work perfectly. Two non-terrestrial Internet of Things technical proposals are then introduced, namely satellite Internet of Things and unmanned aerial vehicle Internet of Things. However, the focuses of these non-terrestrial Internet of Things are distinct, that is, the major problems of satellite and unmanned aerial vehicle Internet of Things are the high dynamic nature of channel and high maneuverability of unmanned aerial vehicles, respectively. The key technologies for satellite and unmanned aerial vehicle Internet of Things are then reviewed separately. Both physical and non-physical layer technologies are surveyed for satellite Internet of Things, and the route planning is mainly investigated for the unmanned aerial vehicle Internet of Things. Finally, we draw a conclusion and give some potential research directions of non-terrestrial Internet of Things.

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Detection and identification technology of rotor unmanned aerial vehicles in 5G scene

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719853990 ◽

2019 ◽

Vol 15 (6) ◽

pp. 155014771985399 ◽

Cited By ~ 3

Author(s):

Fengtong Xu ◽

Tao Hong ◽

Jingcheng Zhao ◽

Tao Yang

Keyword(s):

Internet Of Things ◽

Unmanned Aerial Vehicles ◽

Unmanned Aerial Vehicle ◽

Millimeter Wave ◽

Millimeter Wave Radar ◽

Aerial Vehicles ◽

Wave Radar ◽

Radar Echo ◽

Aerial Vehicle ◽

Modulation Signal

In the 5G era, integration between different networks is required to realize a new world of Internet of things, the most typical model is Space–Air–Ground Internet of things. In the Space–Air–Ground Internet of things, unmanned aerial vehicle network is widely used as the representative of air-based networks. Therefore, a lot of unmanned aerial vehicle “black flying” incidents have occurred. UAVs are a kind of “low, slow and small” artificial targets, which face enormous challenges in detecting, identifying, and managing them. In order to identify the “black flying” unmanned aerial vehicle, combined with the advantages of 5G millimeter wave radar and machine learning methods, the following methods are adopted in this article. For a one-rotor unmanned aerial vehicle, the radar echo data are a single-component sinusoidal frequency modulation signal. The echo signal is conjugated first and then is subjected to a short-time Fourier transform, while the micro-Doppler has a double effect. For a multi-rotor unmanned aerial vehicle, the radar echo data are a multi-component sinusoidal frequency modulation signal, the k-order Bessel function base and the signal are used for integral projection processing, which better identifies the micro-Doppler characteristics such as the number of rotors or the rotational speed of each rotor. The noise interference is added to verify that the algorithm has better robustness. The micro-Doppler characteristics of rotor unmanned aerial vehicles are extracted by the above algorithm, and the data sets are built to train the model. Finally, the classification of unmanned aerial vehicle is realized, and the classification results are given. The research in this article provides an effective solution to solve the problem of detecting and identifying unmanned aerial vehicle by 5G millimeter wave radar in the Internet of Things, which has high practical application value.

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Joint Unmanned Aerial Vehicle (UAV) Deployment and Power Control for Internet of Things Networks

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2020.2975031 ◽

2020 ◽

Vol 69 (4) ◽

pp. 4367-4378 ◽

Cited By ~ 2

Author(s):

Shu Fu ◽

Yujie Tang ◽

Ning Zhang ◽

Lian Zhao ◽

Shaohua Wu ◽

...

Keyword(s):

Internet Of Things ◽

Power Control ◽

Unmanned Aerial Vehicle ◽

Aerial Vehicle

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A blockchain-based secure healthcare scheme with the assistance of unmanned aerial vehicle in Internet of Things

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2020.106627 ◽

2020 ◽

Vol 84 ◽

pp. 106627 ◽

Cited By ~ 4

Author(s):

Anik Islam ◽

Soo Young Shin

Keyword(s):

Internet Of Things ◽

Unmanned Aerial Vehicle ◽

Aerial Vehicle

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An Internet of Things assisted Unmanned Aerial Vehicle based artificial intelligence model for rice pest detection

Microprocessors and Microsystems ◽

10.1016/j.micpro.2020.103607 ◽

2021 ◽

Vol 80 ◽

pp. 103607

Author(s):

Sourav Kumar Bhoi ◽

Kalyan Kumar Jena ◽

Sanjaya Kumar Panda ◽

Hoang Viet Long ◽

Raghvendra Kumar ◽

...

Keyword(s):

Artificial Intelligence ◽

Internet Of Things ◽

Unmanned Aerial Vehicle ◽

Aerial Vehicle ◽

Rice Pest ◽

Pest Detection

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Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle

Remote Sensing ◽

10.3390/rs4092736 ◽

2012 ◽

Vol 4 (9) ◽

pp. 2736-2752 ◽

Cited By ~ 95

Author(s):

Ryan Hruska ◽

Jessica Mitchell ◽

Matthew Anderson ◽

Nancy F. Glenn

Keyword(s):

Unmanned Aerial Vehicle ◽

Hyperspectral Imagery ◽

Geometric Analysis ◽

Aerial Vehicle

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Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things

Sensors ◽

10.3390/s19081908 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1908 ◽

Cited By ~ 5

Author(s):

Bingjie Liu ◽

Haitao Xu ◽

Xianwei Zhou

Keyword(s):

Resource Allocation ◽

Internet Of Things ◽

Information Transmission ◽

Unmanned Aerial Vehicle ◽

Power Level ◽

Dynamic Game ◽

Proposed Model ◽

Wireless Nodes ◽

Optimal Resource ◽

Aerial Vehicle

Most of the wireless nodes in the Internet of Things (IoT) environment face the limited energy problem and the way to provide a sustainable energy for these nodes has become an urgent challenge. In this paper, we present an unmanned aerial vehicle (UAV) to power the wireless nodes in the IoT and an investigation on the optimal resource allocation approach based on dynamic game theory. This IoT system consists of one UAV as the power source and information receiver. The wireless nodes can be powered and collected by the UAV. In order to distinguish the wireless nodes, the wireless nodes are divided into two categories based on the energy consumption. The UAV tries to power these two categories of nodes using a different power level based on the proposed approach, where the wireless nodes control the resources for information transmission. Based on Bellman dynamic programming, the optimal allocated resources for power transfer and information transmission are obtained for both the UAV and wireless nodes, respectively. In order to show the effectiveness of the proposed model and approach, we present numerical simulations.

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Evaluation of UAV Photogrammetric Accuracy for Mapping and Earthworks Computations

GEOMATICA ◽

10.5623/cig2014-405 ◽

2014 ◽

Vol 68 (4) ◽

pp. 309-317 ◽

Cited By ~ 24

Author(s):

Chris Cryderman ◽

S. Bill Mah ◽

Aaron Shufletoski

Keyword(s):

Unmanned Aerial Vehicle ◽

Confidence Level ◽

Geometric Analysis ◽

Weather Conditions ◽

Ground Control ◽

Surface Models ◽

Aerial Vehicle ◽

Survey Results

This study quantifies the accuracies achieved and tests the validity of an in-house developed Unmanned Aerial Vehicle (UAV) system employed in a stockpile volumetric survey. UAV photogrammetric results are compared with conventional GNSS survey results. To test the repeatability of the UAV system, multiple flights were flown over the same stockpile using different GNSS ground control, at different times and weather conditions. Positional accuracies of UAV photogrammetric results were found to be very similar to those from GNSS RTK survey, at the scale of photography flown. UAV stockpile volume results agreed with those from GNSS within 3 755 m3 (0.7%) on a 530 255 m3 pile. Stockpile volume comparisons between subsequent UAV surface models agreed within 877 m3 (0.2%) on the same pile. Geometric analysis of independent UAV photogrammetric models over the same area indicated that they could be considered the same at a 95% confidence level. We conclude that the UAV photogrammetric approach is, at the very least, equivalent in accuracy to GNSS RTK surveys at the scale of photography observed. The accuracy of the UAV photogrammetric surveys were sufficient for 1:200 scale mapping and 0.145 m contours. The UAV photogrammetric approach also provided greater detail, resulting in more representative models of the measured surfaces.

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