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Comparison of Path Loss Prediction Models for UAV and IoT Air-to-Ground Communication System in Rural Precision Farming Environment

Sarun Duangsuwan 1 and Myo Myint Maw 2
1. Information Engineering, King Mongkut’s Institute of Technology Ladkrabang, Prince of Chumphon Campus, 17/1 Chumcoo District, Pathio, Chumphon, Thailand
2. Department of Computer Engineering and Information Technology (CEIT), Mandalay Technological University (MTU), Patheingyi Township, Mandalay, Myanmar

Abstract—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: R2) 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.
Index Terms—UAV, IoT, air-to-ground communication, path loss, machine learning methods, rural precision farming environment

Cite: Sarun Duangsuwan and Myo Myint Maw, "Comparison of Path Loss Prediction Models for UAV and IoT Air-to-Ground Communication System in Rural Precision Farming Environment," Journal of Communications vol. 16, no. 2, pp. 60-66, February 2021. Doi: 10.12720/jcm.16.2.60-66

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