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Author: search.filters.author.Duangsuwan, SarunSubject: search.filters.subject.IoT

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    A Study of A2A Channel Modeling for Small UAV-Enabled Wireless Communication
    (2022-01-01)
    Supramongkonset, Jatuporn
    ;
    Duangsuwan, Sarun
    ;
    Promwong, Sathaporn
    The objective of this work is to study the ground reflection modeling case study of air-to-air (A2A) channel modeling for an unmanned aerial vehicle (UAV)-enabled wireless communications. We consider the Internet of Things (IoT) WiFi modules at 2.4 GHz standard enabled with the transmitter Tx- UAV and the receiver Rx-UAV. The received signal strength indicator (RSSI) and path loss characteristics are addressed by using the free space path loss (FSPL) model, air-to-air two- ray (A2AT-R) model, and the modified Log-distance path loss model. The measurement results indicated that the limit of power constraint of WiFi modules was a level at 10 m Rx-UAV altitude, and the received sensitivity was limited at -95 dBm. It is shown that the impact of ground reflection path loss was characterized at 1-3 m Rx-Uavaltitudes which are accordingly related to the A2AT-R model and the relative resulting with the modified Log- distance model above 4 m Rx-UAV altitudes.
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    Comparison of path loss prediction models for UAV and IoT air-to-ground communication system in rural precision farming environment
    (2021-02-01)
    Duangsuwan, Sarun
    ;
    Maw, Myo Myint
    The comparison of path loss model for the unmanned aerial vehicle (UAV) and Internet of Things (IoT) air-to-ground communication system was proposed for rural precision farming. Due to the uncertainty of propagation channel in rural precision farming environment, the comparison of path loss prediction was investigated by the conventional particle swarm optimization (PSO) algorithms: PSO (exponential or Exp), PSO (polynomial or Poly) and the machine learning algorithms: k-nearest neighbor (k-NN), and random forest, are exploited to accurate the path loss models on the basic of the measured dataset. Meanwhile, the empirical model in the rural precision farming was considered. By using the machine learning-based algorithms, the coefficient of determination (R-squared: R<sup>2</sup>) and root mean squared error (RMSE) were evaluated as highly accuracy and precision more than the conventional PSO algorithms. According to the results, the random forest method was able to perform more than other methods. It has the smallest prediction errors.
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    Development of soil moisture monitoring by using IoT and UAV-SC for smart farming application
    (2020-07-01)
    Duangsuwan, Sarun
    ;
    Teekapakvisit, Chakree
    ;
    Maw, Myo Myint
    Soil moisture is a fundamental factor for smart farming that is used to control the water management system. In this paper, the unmanned aerial vehicle (UAV) small cell (UAV-SC) can provide Internet of things (IoT) as the hotspot mobility network, due to the minimum limitation energy of connected IoT. The development of ground sensor (GS) communicates to the UAV-SC called GS-UAV-SC model for soil moisture monitoring is proposed to smart farming. UAV-SC aims to fulfill the data collection task with a limitation of GSs power. In the experiment, the two case scenarios: Napier grass farm and Ruzi grass farm are implemented. The result of soil moisture status is demonstrated as an example of data in real time on a mobile application monitoring system. The proposed system is useful for users/farmers to know the soil moisture data quickly for smart farming applications.