The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two machine learning-based propagation models, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors, such as the transmitter’s height relative to the trunk, the beginning of foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12, and the co-polarization of the transmitter and receiver antennas. The proposed models demonstrated higher accuracy, achieving values of root mean square error (RMSE) of 3.86 dB and standard deviation (SD) of 3.8614 dB, respectively, compared to existing empirical models like CI, FI, Early ITU-R, COST235, Weissberger, and FITU-R. The significance of this study lies in its potential to boost wireless communications in wooded environments. Furthermore, this research contributes to enhancing more efficient and robust LoRa networks for applications in agriculture, environmental monitoring, and smart urban infrastructure.

中文翻译：

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应用人工神经网络预测亚马逊森林环境中接收信号强度指示和信噪比

城市化城市中绿地的存在对于减少城市化的负面影响至关重要。然而，这些区域可能会影响使用无线通信（例如 LoRa 技术）的物联网设备的信号质量。植被会衰减电磁波，干扰物联网设备之间的数据传输，因此需要进行信号传播建模，该模型考虑植被对其传播的影响。在此背景下，这项研究是在帕拉联邦大学进行的，在由亚马逊典型的波乌穆拉托物种组成的树木繁茂的环境中进行了测量。开发了两种基于机器学习的传播模型 GRNN 和 MLPNN，以考虑亚马逊树木对传播的影响，分析不同的因素，例如发射器相对于树干的高度、树叶的开始位置和树冠的中部，以及 LoRa 扩频因子 (SF) 12，以及发射机和接收机天线的同极化。与 CI、FI、Early ITU-R、COST235、Weissberger 等现有经验模型相比，所提出的模型表现出更高的精度，均方根误差 (RMSE) 为 3.86 dB，标准差 (SD) 为 3.8614 dB和 FITU-R。这项研究的意义在于它有可能促进树木繁茂的环境中的无线通信。此外，这项研究有助于增强 LoRa 网络在农业、环境监测和智能城市基础设施中的应用，变得更加高效和强大。