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

2018 ◽  
pp. 108-120 ◽  
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

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

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.

scholarly journals Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986588 ◽  
Author(s):  
Shan Meng ◽  
Xin Dai ◽  
Bicheng Xiao ◽  
Yimin Zhou ◽  
Yumei Li ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Unmanned Aerial Vehicle ◽  
Millimeter Wave ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Base Stations ◽  
Communication Link ◽  
Multiple Sensors ◽  
Channel Tracking ◽  
Aerial Vehicle

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.

A LoRa-Based Air Quality Monitor on Unmanned Aerial Vehicle for Smart City

2018 ◽  
Author(s):  
Liang-Yu Chen ◽  
Hsiao-Shih Huang ◽  
Cheng-Ju Wu ◽  
Yi-Ting Tsai ◽  
Yue-Shan Chang
Keyword(s):  
Air Quality ◽  
Unmanned Aerial Vehicle ◽  
Smart City ◽  
Aerial Vehicle

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

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985399 ◽  
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.

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

2020 ◽  
Vol 69 (4) ◽  
pp. 4367-4378 ◽  
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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