# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 3 Measurement Methodology
### Measurement Methodology  
We conducted measurement campaigns on the shores of UFPA along the Guama River. An LoRa gateway was placed on the roof of the Mirante do Rio building, 30 m above the ground. Simultaneously, an LoRa device was mounted on a drone, flying over the riverbanks along the edge of UFPA. We calculated the Euclidean distance between the transceivers using the latitude and longitude values captured by the GPS module in the drone's LoRa device. The mobile end node stored the GPS position in motion to collect uplink channel information and transmitted it in the LoRa packet to the gateway (uplink). We utilized an end node consisting of an Arduino UNO board and a Dragino-developed shield plate, equipped with LoRa SX1276 and Quectel L80 GPS modules. The device was powered by a 5V USB and connected to a LoRa gateway, specifically the RAK7249 model. The end node was attached to a drone, running the LoRaWAN protocol as a transmitter. Figures 3 and 4 show the end node and the gateway used in the measurement campaign. The data collected at the gateway were stored in LoRa packets and transmitted to the LoRa network server. The LoRa gateway RAK7249 has LoRaWAN public settings, allowing it to receive different spreading factors (SFs) and various bandwidths, with a sensitivity of \(-148\) dBm. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**LoRa Applications over Water**} \\ \hline
**Reference** & **Frequency (MHz)** & **Scenario** & **Coverage (km)** & **Spreading Factor** \\ \hline
[17] & 868 (NLOS) & Sea & 12.96 & 7–12 \\
[18] & 433 (LOS) & Sea & 0.4 & 7–10 \\
[19] & 433/868 (LOS) & 22 & 7/10/12 \\
[24] & 915 (LOS) & River & 30 & 7 \\
[27] & 433 (NLOS) & Sea & 4 & 12 \\
[28] & 868 (LOS) & Sea & 4.15 & 7/9/12 \\ \hline \hline \multicolumn{4}{c}{**LoRa Applications with Vegetation**} \\ \hline
**Reference** & **Frequency (MHz)** & **Scenario** & **Coverage (km)** & **Spreading Factor** \\ \hline
[29] & 868 (LOS) & Rural with trees & 47 & 12 \\
[30] & 915 (NLOS) & Forest & 0.25 & 12 \\
[31] & 433 (NLOS) & Forest & X & 12 \\
[28] & 868 (LOS) & Forest & 0.71 & 7/9/12 \\
[32] & 915 (NLOS) & Rural with trees & 1.6 & 7–10 \\
[33] & 868 (NLOS) & Forest & 1.75 & 7–12 \\
[34] & 868 (NLOS) & Forest & 0.23 & 12 \\
[91] (NLOS) & Forest near lake & 1 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work for LoRa applications over water and in environments with vegetation. The LoRa gateway employs Message Queuing Telemetry Transport (MQTT), a messaging protocol designed for low-bandwidth networks and IoT devices with high latency, to manage an internal server [35]. This internal server, in conjunction with the LoRaWAN protocol, enables the gateway to receive data in JSON format and store persistent data through an MQTT broker server. We utilized a local broker server to receive the LoRa frame data and store them in a relational database for future reference. Upon receiving the LoRa frame, the gateway provides crucial information such as RSSI, SNR, the end node address in HEX, and the payload message. The local server then records these parameters for future analysis and processing. The end node transmitter attached to the drone sends packets containing GPS coordinates every second without using control mechanisms or automatic retransmissions. The radiated power is set to 20 dBm, employing SF7, SF8, and SF9 to analyze receiver sensitivity variation, with a code rate of 4/5 and a bandwidth (BW) set to 125 kHz on the 915 MHz channel. The packet payload is 40 bytes, including a 13-byte MAC header in the case of configurations. Table 2 shows the measurement configuration parameters. We positioned the gateway on the terrace of the Mirante do Rio building, 30 m above the ground. The end node was then moved along the route at an average speed of 25 km per hour, fixed on the drone, and positioned 3 m above the ground. This methodology allows for the measurement of signal attenuation through RSSI values and the evaluation of SNR in motion for the uplink channel.