# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 6 Conclusions  
The central objective of this study was to characterize the uplink LoRa channel at 915 MHz to meet the IoT connectivity demands in Amazon rivers. The combination of long-range communication, low power consumption, security features, and affordability in LoRa technology makes it a valuable tool for scientific research and a catalyst for innovation in a wide array of IoT applications. In this context, understanding the propagation loss in the study environment is essential for developing a realistic channel characterization. We conducted extensive measurement campaigns on the banks of the river at the Federal University of Para. We installed an LoRa gateway on the roof of a building and deployed the end node on a drone to collect received signal strength indication and signal-to-noise ratio values, along with geolocated points. The measurements were taken at three different spreading factors (SF7, SF8, and SF9) along a predetermined route. Additionally, we applied the close-in (CI) and floating intercept (FI) models to estimate the path loss along the study route and compared them with the Okumura-Hata model. Moreover, we introduced a novel propagation model for dense vegetation environments (\(PL_{vgg}\)), utilizing a vegetation attenuation model from ITU-R P-833 and the Friis free space formula. This model can be employed to estimate path loss values in areas where direct measurements are not feasible. The root mean square (RMS) error values were 6.17 dB, 6.17 dB, 6.91 dB, and 8.43 B, for the CI, FI, Okumura-Hata, and \(PL_{vgg}\) at SF7; 6.57 dB, 6.55 dB, 7.53 dB, and 7.98 dB for CI, FI, Okumura-Hata, and \(PL_{vgg}\) at SF8; and 6.68 dB, 6.57 dB, 7.87 dB, and 7.5 dB, for CI, FI, Okumura-Hata, and \(PL_{vgg}\) at SF9. We encountered several limitations during our study, including challenges in controlling the drone over water due to reflections on the camera attached to it, the absence of works involving mixed environments and water, the limited battery life of the drone (approximately 20 min), the region's Amazon climate characterized by rapid and intense rains during the summer, which posed a risk to our measuring equipment, and the size of the route, covering the territorial limits of the university. The combination of these factors, along with the drone's battery constraints, made it impractical for us to conduct additional routes over water. Despite these limitations, our study yielded promising results. This paper contributes models for uplink planning in LoRa networks for monitoring boats or smaller vessels. The analysis of the receiver packet rate demonstrates that the maximum packet received with mobility is 92. Future work will encompass propagation studies to analyze high data throughput resulting from the use of two or more connected LoRa devices transmitting information to  
Figure 18: Box plot of the distribution of distance data in SF7, SF8, and SF9 with distance values of 954.35 m, 905.3 m, and 975.8 m representing the values of Q3 (in red) for the three SFs, respectively. the LoRa gateway. This will include exploring spreading factors beyond 9 for comparison with results described in the literature (see Table 1). Additionally, we plan to conduct new routes over water, covering distances greater than 2 km, to further compare our results with those outlined in Table 1. Another aspect of future investigation involves varying the height of the LoRa device to analyze path loss and its increased influence on vegetation. Conceptualization, W.G.L., A.V.R.L., M.R., J.P.L.A. and F.J.B.B. ; data curation, W.G.L., C.M.M.C., A.V.R.L., J.P.L.A. and F.J.B.B. ; formal analysis, W.G.L., A.V.R.L., J.P.L.A., M.C.A.N., M.E.L.T. and A.A.N. ; funding acquisition, F.J.B.B., M.E.L.T. and A.A.N. ; investigation, W.G.L., A.V.R.L. and F.J.B.B. ; methodology, W.G.L., A.V.R.L., C.M.M.C. and F.J.B.B. ; project administration, J.P.L.A., M.C.A.N., M.E.L.T., F.J.B.B. and A.A.N. ; resources, F.J.B.B., M.E.L.T. and A.A.N. ; software, W.G.L., A.V.R.L., C.M.M.C. and F.J.B.B. ; supervision, J.P.L.A., M.C.A.N., M.R.