Comparison of path loss prediction models for UAV and IoT air-to-ground communication system in rural precision farming environment

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dc.contributor.authorDuangsuwan, Sarun
dc.contributor.authorMaw, Myo Myint
dc.date.accessioned2026-08-06T10:31:56Z
dc.date.available2026-08-06T10:31:56Z
dc.date.issued2021-02-01
dc.description.abstractThe 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.
dc.identifier.citationJournal of Communications, 16(2), 60-66, 2021
dc.identifier.doi10.12720/jcm.16.2.60-66
dc.identifier.issn17962021
dc.identifier.other2-s2.0-85100602611
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11846
dc.sourceJournal of Communications
dc.subjectAir-to-ground communication
dc.subjectIoT
dc.subjectMachine learning methods
dc.subjectPath loss
dc.subjectRural precision farming environment
dc.subjectUAV
dc.titleComparison of path loss prediction models for UAV and IoT air-to-ground communication system in rural precision farming environment
dc.typeArticle

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