Modeling of path loss for radio wave propagation in wireless sensor networks in cassava crops using machine learning

Modeling radio signal propagation remains one of the most critical tasks in the planning of wireless communication systems, including wireless sensor networks (WSN). Despite the existence of a considerable number of propagation models, the studies aimed at characterizing the attenuation in the wirel...

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Autores:
Barrios-Ulloa, Alexis
Cama-Pinto, Alejandro
De-La-Hoz-Franco, Emiro
Ramírez-Velarde, Raúl
Cama-Pinto, Dora
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10944
Acceso en línea:
https://hdl.handle.net/11323/10944
https://repositorio.cuc.edu.co/
Palabra clave:
Agriculture
Cassava crops
Machine learning
Radio wave propagation models
Wireless sensor networks
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
Description
Summary:Modeling radio signal propagation remains one of the most critical tasks in the planning of wireless communication systems, including wireless sensor networks (WSN). Despite the existence of a considerable number of propagation models, the studies aimed at characterizing the attenuation in the wireless channel are still numerous and relevant. These studies are used in the design and planning of wireless networks deployed in various environments, including those with abundant vegetation. This paper analyzes the performance of three vegetation propagation models, ITU-R, FITU-R, and COST-235, and compares them with path loss measurements conducted in a cassava field in Sincelejo, Colombia. Additionally, we applied four machine learning techniques: linear regression (LR), k-nearest neighbors (K-NN), support vector machine (SVM), and random forest (RF), aiming to enhance prediction accuracy levels. The results show that vegetation models based on traditional approaches are not able to adequately characterize attenuation, while models obtained by machine learning using RF, K-NN, and SVM can predict path loss in cassava with RMSE and MAE values below 5 dB.