## 4 Results
### Gateway Optimization  
The simulations showed that the propagation model and equipment sensitivity significantly affected the number of gateways needed and their positions. Therefore, it is necessary to use a specific propagation model for the deployment scenarios. Furthermore, the sensitivity must be configured according to the project needs such that the planning simulations have better accuracy.  
In Figure 10, all the optimal points are presented at each generation for the threshold of \(-108\) dBm, taking into account the 300 simulations at 10 Pareto fronts and 50 generations, totaling 150,000 solutions; the y-axis represents the covered area values in relation to the number of gateways ranging from 1 to 10 on the x-axis.  
Note that the covered area for solutions with one gateway is approximately 50% in the best cases. For the two gateways, it is approximately 95%, while for the three gateways, the covered area is fully maximized, with EPSO achieving a solution capable of serving 100% of the established route. Thus, the most suitable solution for implementing the LoRa network is to use three gateways because it can cover 100% of the route with the smallest number of gateways.  
Figure 10: Solutions for \(-108\) dBm sensitivity.  
Figure 9: Comparison between measured data and models.  
In Figure 11, the results of the simulations obtained through the EPSO technique are presented, applying the Monte Carlo method for the Pareto front 8 and a threshold of \(-108\) dBm, in which the optimal solutions obtained for each of the 300 simulations. The x-axis represents the number of simulations performed, the left y-axis represents the gateway position, and the right axis represents the percentage of the covered area. The black circles indicate the solutions found by EPSO, and the red stars indicate the percentage of the coverage area resulting from these solutions.  
Figure 12 illustrates the frequency of occurrence of optimal solutions for the threshold of \(-108\) dBm for the Pareto 8 front as a function of the number of simulations. The most frequent solution is composed of three gateways, 9- 13-22, and it occurred in 4.33% of the 300 simulations, from which 65 different optimal solutions were obtained. Each value of the most frequent solution is represented by its respective location using a pair of latitude and longitude coordinates, as illustrated in Figure 6.  
The other 64 solutions presented for locating gateways can also serve 100% of the area to be covered with only three gateways. However, they were not selected because they  
Figure 11: Monte Carlo simulation result for Pareto front and \(-108\) dBm.  
Figure 12: Frequency of Pareto 8 solutions with \(-108\) dBm sensitivity.  
were less frequent. Nevertheless, depending on the implementation cost, they can also be considered.  
The positioning of the gateways for the optimal solution with a sensitivity of \(-108\) dBm using the Pareto 8 front is illustrated in Figure 13. The yellow dots are the optimal locations of the gateways in positions 9-13-22, and the red circles represent the coverage radius of each gateway estimated by the UFPA model, adjusted to 662.68 m, which provides 100% coverage.  
When developing an IoT network for scenarios where communication must be reliable and have low latency, the received signal power must be prioritized. Therefore, it is crucial to define a higher reception sensitivity that reduces the coverage area. In this case, the number of gateways must be increased until the required total coverage area is achieved. However, for cases in which coverage must be prioritized or implementation costs must be reduced to the detriment of signal quality, it is possible to work with lower sensitivity, thus attaining greater coverage distances by equipment.