## 4 Measurement Results and Analysis
### The Impact of Different Terrains  
The measured received power values in Figure 2 indicated that WSNs deployed in concrete road scenario and grass scenario had different transmission distances. Furthermore, other terrain factors may also have an impact on propagation characteristics of wireless channel, resulting in an influenced path loss. To investigate the effects of other terrain factors, two more scenarios were measured, which \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Item**} & \multicolumn{3}{c}{**Flat Concrete Road**} & \multicolumn{3}{c}{**Flat Grass**} \\ \cline{3-8}  & & **5 cm** & **50 cm** & **1 m** & **5 cm** & **50 cm** & **1 m** \\ \hline \multirow{4}{*}{One-slope} & \(n\) & 3.264 & 2.799 & 2.458 & 3.403 & 2.860 & 2.458 \\  & Mean error & \(-0.0147\) & \(-0.0139\) & \(-0.0031\) & \(-0.0086\) & \(-0.0005\) & \(-0.0169\) \\  & RMSE & 2.0727 & 2.0355 & 3.1902 & 2.4529 & 2.0175 & 4.4828 \\  & MAPE & 1.75\% & 1.73\% & 3.62\% & 2.25\% & 2.06\% & 5.20\% \\ \hline \multirow{4}{*}{Two-slope} & \(n_{1}\) & – & 1.637 & 1.946 & – & 1.059 & 1.666 \\  & \(n_{2}\) & 3.264 & 2.881 & 3.315 & 3.403 & 2.972 & 3.685 \\ \cline{1-1}  & Mean error & \(-0.0147\) & \(-0.0104\) & 0.0015 & \(-0.0086\) & \(-0.0129\) & \(-0.0104\) \\ \cline{1-1}  & RMSE & 2.0727 & 1.9448 & 1.7856 & 2.4529 & 1.8368 & 2.4192 \\ \cline{1-1}  & MAPE & 1.75\% & 1.59\% & 1.82\% & 2.25\% & 1.62\% & 2.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model parameters and precision. Figure 6: Comparison of path loss models in the flat concrete road scenario: (**a**) \(H_{t}\) = \(H_{r}\) = 5 cm; and (**b**) \(H_{t}\) = \(H_{r}\) = 1 m.  
can be described as \"undulating grass\" and \"flat grass with obstacles\", as shown in Figure 1c,d. In this comparison test, the antenna height of transceivers was fixed to 5 cm. Path loss models of these two derived grass scenarios were obtained from the fitting results in Figure 7. Figure 8 shows path loss models of the three grass scenarios. The path loss of undulating grass scenario was visibly greater than other two scenarios. The received power was about 6 dB lower than RX power of flat grass scenario when the distance reached 50 m. This illustrates that the NLOS propagation significantly decayed the signal strength and the received power. On the other hand, there was no obvious difference on path loss between flat grass scenario and flat grass with obstacles scenario, suggesting that signals could transmit through low shrubs without too much attenuation. These results can guide the deployment of the sensor network. When the terrain is undulating, or there are some huge obstacles, it is necessary to add more relay nodes to ensure the reliability of transmission. Based on the above measurements and analysis, we summarize the propagation model selection strategy in different scenarios in Figure 9. When we need to build a propagation model in a new scenario, the first question is whether we can actually measure the propagation characteristics of this scenario. Terrain and environmental factors can have a large impact on the propagation model, thus only the measured model is the most accurate. Through the actual measurement, we can derive the log-normal shadowing model. It is better to adopt the two-slope model than the one-slope model unless the antenna height is extremely low (e.g., lower than 50 cm). In some cases, it is dangerous or difficult to make actual measurements. At this time, one should first look for an empirical model with similar terrain and antenna height to the scenario, which can obtain predictions that are closer to the actual propagation characteristics. If such a model is not found in previous work, then using a theoretical model such as the two-ray model is also an alternative. Figure 8: Path loss models with different terrain factors (\(H_{t}=H_{r}=5\,\mathrm{cm}\)).