# Evaluation of LoRa Network Performance for Water Quality Monitoring Systems
#### 3.2.4 Received Power  
The computation of the transmission power, gains, and losses influencing the amount of received power has a significant effect on the total budget link. Correlating to the link budget, the findings of the received power and received sensitivity are significant in concluding the value of the fading margin. The result of the fading margin allows the design specification to ensure the system performance level.  
This parameter was not widely discussed in a previous study of the LoRa monitoring system, as it is known that the receiver sensitivity of LoRa can operate below \(-130\) dBm. Hence, it allows the interpretation of lower signals; however, it is crucial to determine the minimum strength to be detected and processed for transmission quality assurance. Table 10 shows a comparison of the total received sensitivity and received power.  
The received power gain from all locations could be perceived by the gateway, because the received sensitivity was distributed at a minimum of \(-129.1\) dBm. The received power analyzed from all locations remained below the received sensitivity values. The low value of the received sensitivity in this study allowed the receiver to interpret the weak signals transmitted from the LoRa nodes at each water station. The use of a higher antenna gain in this design system allowed it to maintain a minimum fading margin with an estimation of 10 dB from all distances.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Antenna Gain** & **Station** & **Distances** & **Received Sensitivity** & **Received Power** \\
**(dBi)** & **Station** & **(m)** & **(dBm)** & **(dBm)** \\ \hline \multirow{3}{*}{2.1} & P1 & 117 & \(-116.72\) & \(-109.65\) \\  & P2 & 1560 & \(-127.9\) & \(-120.75\) \\  & P3 & 566 & \(-121.35\) & \(-113.86\) \\ \hline \multirow{3}{*}{3} & P1 & 117 & \(-118.58\) & \(-108.34\) \\  & P2 & 1560 & \(-129.1\) & \(-118.02\) \\ \cline{1-1}  & P3 & 566 & \(-122.98\) & \(-112.44\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Total of received power and received sensitivity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Antenna Gain** & **Water Station** & **Distances** & **Path Loss FSDP** & **Path Loss Oku–Hata** \\
**(dBi)** & **Water Station** & **(m)** & **(dB)** & **(dB)** \\ \hline \multirow{3}{*}{2.1} & P1 & 117 & 65.31 & 88.21 \\  & P2 & 1560 & 83.61 & 127.34 \\  & P3 & 566 & 74.81 & 112.03 \\ \hline \multirow{3}{*}{3} & P1 & 117 & 64.41 & 88.21 \\  & P2 & 1560 & 82.71 & 127.34 \\ \cline{1-1}  & P3 & 566 & 73.91 & 112.03 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Total of path loss based on FSDP and Oku–Hata model.  
The proposed method for classifying water bodies using K-means on filtered and raw water quality data was found to be effective in real-time classification of water bodies. Additionally, the method provided a simple and informative summary of water quality conditions through a pentagon shape based on the standard water quality index (WQI) provided illustration information and allowed the normal user to understand. The evaluation of LoRa network performance based on antenna comparison showed the ability of LoRa network to adapt to its condition and environment, though the highest packet loss rates exceeded the undesirable value of 3%. The path loss determination provides insight for designing the network properties considering the reflection, diffraction, and penetration effects in non-line-of-sight (NLOS) settings. The design is also able to achieve the good minimum of received sensitivity that allows hearing weak signals in urban areas.