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Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning

Author

Listed:
  • Alexis Barrios-Ulloa

    (Department of Electronic Engineering, Universidad de Sucre, Sincelejo 700001, Colombia)

  • Alejandro Cama-Pinto

    (Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla 080002, Colombia)

  • Emiro De-la-Hoz-Franco

    (Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla 080002, Colombia)

  • Raúl Ramírez-Velarde

    (School of Engineering and Sciences, Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey 64849, Mexico)

  • Dora Cama-Pinto

    (Faculty of Industrial Engineering, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru
    Department of Computer Architecture and Technology, University of Granada, 18071 Granada, Spain)

Abstract

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 .

Suggested Citation

  • Alexis Barrios-Ulloa & Alejandro Cama-Pinto & Emiro De-la-Hoz-Franco & Raúl Ramírez-Velarde & Dora Cama-Pinto, 2023. "Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning," Agriculture, MDPI, vol. 13(11), pages 1-15, October.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:11:p:2046-:d:1266770
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    References listed on IDEAS

    as
    1. Dora Cama-Pinto & Miguel Damas & Juan Antonio Holgado-Terriza & Francisco Gómez-Mula & Alejandro Cama-Pinto, 2019. "Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse," IJERPH, MDPI, vol. 16(10), pages 1-15, May.
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