## 1 Introduction
However, these data are only theoretical and do not consider the morphology of the environment or the possible impacts that may occur in the coverage area as a result of obstacles in the communication channel. According to [23], these factors are crucial for obtaining a realistic analysis. In [14], the coverage of a LoRa network was studied through measurements carried out in the city of Oulu, Finland, for a frequency band of 868 MHz and transmission power of 14 dBm. In this investigation, the maximum propagation factor was found with a transmission range of more than 15 km to the receiver on the ground (connected to the roof of a car) and 30 km in the water (connected to the mast of a boat). In [15], the propagation of LoRa signals in forest, urban, and suburban vehicular environments was discussed. In addition to environments with variable propagation conditions, scenarios with node mobility were evaluated. To characterize the signal propagation, the Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Packet  
Figure 1: Topology of a LoRa network [12]. Delivery Ratio (PDR) were used. The results showed that the link reached up to 250 m in the forest scenario. In scenarios with high-density buildings and human activity, the maximum reach of the link was 200 m in the urban scenario. The results of [14; 15] show the impact of the environment on the coverage area, which must be considered in the planning stage to provide maximum coverage. The authors in [16] evaluated the level of signal strength received by a LoRaWAN network deployed in Skellefeta, Sweden. The obtained data were compared with simulations of a radiofrequency planning tool using Irregular Terrain Propagation (ITM), Irregular Terrain With Obstruction (ITWOM), and Okumura-Hata propagation models. The results of the ITWOM model were closer to the collected data. Furthermore, it was found that the choice of the propagation model must be careful and must always meet the environmental conditions. This restriction must be considered when defining the objective function proposed in this study. Another aspect that should be considered in the study of LoRa technology is mobility, which was analyzed in [17], and the performance of a LoRaWAN network (915 MHz) in indoor and outdoor environments, taking into account different degrees of mobility, with end-to-end delay and Packet Loss Rate (PLR) as the main parameters. In [18], a comparison was made between the real and simulated scenarios (NS-3), referring to a LoRaWAN network (915 MHz) for vehicular communication (mobility), aiming to analyze and compare the performance in both cases by considering three measures, namely, PDR, Packet Intercept (PIR), and RSSI. The experiments were conducted at the Federal University of Rio de Janeiro (UFRJ) and consisted of traveling a route of 1 km at speeds of 30 km/h and 60 km/h with SF values of 7, 9, and 12. The results in [17; 18] indicate that the mobility significantly increased the delay, as well as the speed and distance traveled. To establish a greater accuracy of the propagation models, adjustments are made to adapt the average behavior of the propagated signal to the measured data in specific environments. Some works use bio-inspired optimization techniques. In [19], the Genetic Algorithm (GA), Flower Pollination Algorithms (FPA), and Bat Algorithm (BAT) are used to adjust for LTE frequency range in the Amazon region which is densely wooded. In [20], Particle Swarm Optimization (PSO) is used to optimize the fitting curve parameters to the models generated from mathematical regression techniques for an alfalfa plantation environment applied to ZigBee technology for smart agriculture. Other works were developed to optimize the gateway placement for the LoRa network, as in [21; 22], which uses network simulation software (NS-3 simulator) to evaluate performance based on Quality of Service (QoS) metrics. Both works seek to optimize gateways allocation in area X and can post them anywhere within that area by applying classical propagation models.