# A Visitor Assistance System Based on LoRa for Nature Forest Parks
## 4 Environment  
The forest is an environment hostile to propagation. From the viewpoint of radio signals, the forest is a random medium with many discrete scatters, such as the randomly distributed leaves, branches, and tree trunks. Radio waves propagating in the vegetation naturally experience multiple scattering, diffraction, and absorption of radiation. These different propagation mechanisms, when combined, result in severe fading of the received signal ([PERSON] et al., 2018). Figure 1a,b show the vegetation and the trail where the experiments were done. Figure 1: Vegetation and experiment location. Large- and small-scale fading are related to slow and sudden variations in the signal envelope, respectively. Large-scale fading, or shading, is caused by path loss and slow, predictable variations around the average. Small-scale fading refers to sudden, fast, and small displacement attenuation. The main cause of small-scale fading is the occurrence of the multipath phenomenon, which consists of overlapping different versions of the same signal with different phases at the receiver. The composition of these signals can severely attenuate the received signal, reducing the received power and signal-to-noise ratio (SNR), and thereby reducing the link range and packet delivery ratio. As a result, the maximum range of LoRa in the forest, considering SF12, the most robust spreading factor, is reduced from several kilometers in the open air to just 250 m.  
To evaluate the feasibility of LoRa in the rain forest, a measurement campaign was carried out to identify the behavior of a radio link within this environment. We have performed practical experiments with LoRa at 915 MHz. Two prototype nodes based on Arduino microcontrollers are used. Figure 2a shows a map of the test site, and Figure 2b shows the prototypes. This experimental equipment is summarized in Table 1, and its configuration parameters are listed in Table 2.  
Figure 2: Measuring location map and prototypes. **(a)** Map of the site of experiments at PARNASO ([[https://www.icmbio.gov.br/parnaserradosorgaos/](https://www.icmbio.gov.br/parnaserradosorgaos/)]([https://www.icmbio.gov.br/parnaserradosorgaos/](https://www.icmbio.gov.br/parnaserradosorgaos/))). **(b)** The pole-mounted prototype node. The Log-distance distribution is widely used to model large-scale fading [23]. To describe the behavior of the signal considering only large-scale fading, it is necessary to eliminate local variations caused by small-scale fading. For this, the averages of the various measurements performed were used. This behavior can be seen in Figure 3, which shows the RSSI histogram and the Probability Density Function (PDF) for the measured data. Figure 4 shows the RSSI histogram and the PDF for small-scale fading estimated using instantaneous power measurement. We observe that the Nakagami-m [24] distribution is the one that best fits forest propagation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Value** \\ \hline Controller & Arduino Uno R3 \\ Wireless interface & Dragrino RF96 \\ GPS Receiver & U-blox NEO-6M \\ Antenna & Monopole 7 dBi \\ Transmission antenna height & 1.5 m \\ Supply Current in Receive Mode & 10.8 mA \\ Supply Current in Transmit Mode & 120 mA \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hardware components used in the prototype nodes. Figure 3: Probability Density Function (PDF) of average power measurements (W) for LoRa SF12 at a distance of 250 m.  
Signal attenuation in the forest environment is composed of small-scale fading superimposed on large-scale fading. In this situation, the PDF of the envelope can be considered as the composition of two probability distributions, and calculated by their convolution. Although the Log-distance distribution is the best fit to large-scale fading, its combination with multipath model distributions results in open formulas. This resulting formulation is difficult to use in calculations, such as bit error rate, diversity schemes, outage probability, and coverage prediction, according to [25].