# A Visitor Assistance System Based on LoRa for Nature Forest Parks
## 6 Simulation
The slower experiment--with more samples--shown in Figure 16 presents a better approximation between simulated data and measured data than the fastest experiment (Figure 17), with approximate correspondence between the contact intervals and the maximum and minimum values of measured power for practical and simulated experiments. Figure 14: Second day trail landscape. Figure 13: Route of Petröpolis—Teresopolis crossing. Figure 16: Received Signal Strength Indicator (RSSI) vs. distance—50 m/min—simulated vs. practical. Figure 15: Locations for installing totems. In Figure 20, we evaluate the capacity of transferring data of the simulated network. The maximum daily load produced per visitor expected is of the order of 182 kB in the worst case. This worst-case is defined using the maximum capacity of the mountain shelters, which are 100 visitors. If there is no formation of groups and these 100 visitors walk alone along the trail, their contacts must generate 4950 records per day, combining them 2 to 2. Considering the size of each record of 13 B, from the mobile terminal beacon of 10.5 B and the two acceptance messages added to 13.25 B, we have 36.75 B of transmitted data. Thus, the daily load of approximately 182 kB (\(\approx\) 4950 \(\times\) 36.75 B) per visitor is reached in the worst-case, with no formation of groups. However, this requirement is not met using the spreading factor SF12, especially in the stretch of the route with the least totems. This is because the load of data generated per visitor depends on the number of visitors on the trail and the records of encounters between them. The transfer capacity of the network depends on the number of totems in the network. Nevertheless, the amount and positioning of the totems are a function of the risks that each location offers to the visitor or its potential to assist in locating lost visitors. Moreover, the number of packets flowing over the network varies with the contact time, the size of the packets and the spreading factor used. In Figure 20, we plot the load growth day by day, for the three travel speeds. The red curve represents this growth for a slower visitor, who moves at 3 km/h (50 m/min). As the contact time for this visitor is longer, more packets are sent favoring the greater data transfer. Data produced for each day of the trail have the following sizes:  
* 1 st day: approximately 150 kB, below the worst-case load of 182 kB;
* 2 nd day: approximately 500 kB \(-\) 150 kB = 350 kB. Above 182 kB;
* 3 rd day: approximately 600 kB \(-\) 500 kB = 100 kB. Below 182 kB. The blue curve represents this growth for a faster visitor, who travels at 6 km/h (100 m/min). As the contact time for this visitor is shorter, fewer packets are sent. We can see the evolution values over the days:  
* 1 st day: approximately 80 kB, below the worst-case load of 182 kB;
* 2 nd day: approximately 250 kB \(-\) 80 kB = 170 kB. Below 182 kB;
* 3 rd day: approximately 300 kB \(-\) 250 kB = 50 kB. Below 182 kB. The use of other spreading factors for the transfer of records and the grouping of visitors are ways provided in the proposed architecture to deal with this demand, improving the performance of the network, as described in Section 5.2. In this way, the capacity of the network is increased, and the daily data load per visitor transmitted over the network can be reduced, making the monitoring system feasible, even given the hard propagation conditions inside a rainforest. Figure 20: Accumulated load offered to the network as a function of distance and speed of the visitor (mobile node) in the trail.