## 6 Discussion  
To optimize the MBFLR model for all the measured RSSIs, the measured RSSI data were first fitted with the LR dual-slope models (Equations (18) and (19)) for the short grass environments. All measured RSSIs were in the boundary with membership level 0.4 (see Figure 9). Meanwhile, in the case of the tall grass and weed in Equations (20) and (21), the boundary was expanded with membership level 0.2 to cover the measured RSSI (see Figure 10). The central line of both graphs shows the same equation; however, the lowest RSSI occurred at a 40 m distance with a \(-\)103 dB value when the environment was tall grass and weed. In the case of one blockage movement in Equations (22) and (23), the measured RSSIs fluctuated along the measurement distances of 1-40 m with a maximum range of 28 dB (\(-\)66 to value \(-\)94) at a 10 m distance (see Figure 11). The third boundary was also expanded with membership level 0.0 to cover all the measured RSSIs. However, outward RSSIs existed at the lower boundary, which occurred only at the 1 m distance with a maximum range of 23 dB (\(-\)68 to \(-\)91) when the two blockages were close to the transmitting and receiving nodes (see Figure 12). This model is shown in Equations (24) and (25). Note that the PLEs before \(d_{bp}\) in the case of the blockages were small (i.e., less than 20) due to the weak RSSI at the reference distance of 1 m. This phenomenon can hardly occur because the blockage was close to the monitoring node (i.e., receiving node), which should be clear from any obstructions. In addition, this minimum signal level (\(-\)91) was sufficient to connect the communication. Finally, the MBFLR model can be expressed in Equations (26) and (27) with a 0.4 \(\mu_{AI}\) value, which can predict the path loss when the environment changes due to natural and human causes. This makes the WSN more stable after planning with the proposed model. In the statistical error analysis results presented in Table 2, the smallest value indicated that the model had the best matching performance between the predicted and observed values. Based on the statistical evaluation, the proposed MBFLR model had the lowest RMSE scores of 0.8, 1.2, and 2.6 for the short and tall grass and blockages, respectively. Note that the observed values were compared with the predicted boundary values. The proposed dual-slope models provided a large RMSE because of the large RSSI fluctuation from the regression line, especially in the tall grass and weed environments (see Figure 10). In the case of the LR line models, the best model was FITU-R MA FSPL, with the lowest RMSE score of 4.08 for the short grass environment. However, the RMSE scores for the tall grass and blockage environments were 10.9 and 8.1, respectively, due to the large RSSI fluctuation. The second-best model was the One Slope PL_LOS 433 MHz model, which showed the lowest RMSE score of 5.7 for the short grass environment. The RMSE scores for the tall grass and blockage environments were 11.8 and 8.3, respectively. The worst model was the Optimized FITU-R NG, with the lowest RMSE scores of 25.2, 27.7, and 24.7 for the short and tall grass and blockage environments, respectively, especially at the distance before \(d_{bp}\). However, this model meets the upper boundary and the lower boundary of the MBFLR model at distance of approximately 1 m and 40 m, respectively (see Figure 14).