# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 5 Results
### Uplink Channel Modeling Considering Attenuation by Vegetation  
The CI and FI models provide more accurate adjustments than the Okumura model and help plan IoT networks in environments similar to the study environment. However, adjusting the CI and FI model parameters is impossible if measurement campaigns cannot be performed in the study environments. Furthermore, the Okumura-Hata model has a significantly higher RMSE value when compared to the RMSE values of the CI and FI models in all SFs under study. Our objective was to measure the reduction in signal strength caused by thick vegetation in our current setup. Additionally, we planned to investigate the impact of selecting different spreading factors (SF) on LoRa technology while taking into account the longer transmission time and higher sensitivity associated with higher SF values. Based on these analyses, we aimed to identify the most suitable SF for a reliable and robust link in this environment. Therefore, we propose a propagation model to predict vegetation losses using the free space model added to the vegetation attenuation calculated through the models present  
Figure 14: Path loss data and the modeling with CI, FI, and Okumura–Hata models for SF9. in Recommendation ITU-R P-833 (_Vegloss_). We use (8) to calculate all attenuation values based on all elevation angles, with an average tree height of 25 m, operating frequency of 915 MHz, \(A=1.5\), \(B=0.28\), \(E=0.01\), \(G=-0.12\), \(kh=2.5\), and \(p=121.1\). Thus, the proposed model is shown as follows:  
\[PL_{vgg}[dB]=20*log_{10}(\lambda/4\pi d)+Veg_{loss} \tag{11}\]  
where \(d\) is the distance between Tx and Rx, \(\lambda\) is the wavelength as a function of frequency, \(Veg_{loss}\) is the vegetation attenuation calculated through the models present in Recommendation ITU-R P-833, and \(PL_{vgg}\) is the attenuation vegetation model. Figures 15-17 show the modeling with the CI, FI, Okumura-Hata, and the proposed model \(PL_{vgg}\) models for SF7, SF8, and SF9, respectively. Figure 16: Path loss models for SF8 with CI (magenta), FI (blue), Okumura–Hata (red), and proposed vegetation model (green). Figure 15: Path loss models for SF7 with CI (magenta), FI (blue), Okumura–Hata (red), and proposed vegetation model (green). Our study found that the FI model had the best RMSE values among all SFs, and the CI model had the second-best RMSE value. We can observe differences between theoretical data (represented by modeling with the Okumura-Hata (O-H) model) and empirical data (represented by modeling with the floating intercept (FI) model). This difference is accentuated by the fact that O-H modeling was proposed for static links; however, our measurement methodology involves a moving device (dynamic). Considering the mobility of the LoRa device, the attenuation caused by multipath and physical phenomena such as diffraction, dispersion, and shadowing affect the path loss value. This path loss, calculated with empirical data, is significantly different and greater than the loss calculated for a link with a static device when the device is motionless and collecting data. Considering that our device is moving and that there is dense vegetation between the LoRa device and the gateway, we observe an average difference between the RMSE values of the O-H model and the FI model of 0.74 dB, 0.98 dB, and 1.3 dB, for SF7, SF8, and SF9, respectively. According to Figures 15-17, the FI (in blue) and O-H (in red) models show no significant difference between the two models. The results show that the proposed model is able to predict the best RMSE values among all SFs. Figure 17: Path loss models for SF9 with CI (magenta), FI (blue), Okumura–Hata (red), and proposed vegetation model (green).