# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 4 Our Proposed Architecture for Smart Solar Plant
### Perceptual Layer
#### 4.1.3 Communication Technologies  
For higher accuracy and precision, component-level monitoring provides more granular data for assessment. This is in sharp contrast to the conventional measurement that occurs at central inverter levels. It is therefore recommended to install end devices at panel and inverter levels for better data analytics. These sensed parameters can then be transmitted to either a centralized server/storage or a cloud platform for data analytics. Although multiple communication technologies are available, the sheer size of a typical solar plant presents challenges for devising a wireless sensor network. The most prominent of these challenges is selecting a wireless technology that can meet the network requirements  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Type** & **Model** & **Cost** & **Range** & **Accuracy** \\ \hline \multirow{3}{*}{Current Sensor} & ACS712 & USD 1 & 30 A & \(\pm\)30 A, output & \\  & & & & error of \(\pm\)1.5\% \\ \cline{2-5}  & Yocto & USD 100 & \(\sim\)2 mA & 1-3\% \\ \cline{2-5}  & INA219 & USD 6 & \(\pm\)3.2 & 0.2-0.3\% \\ \hline \multirow{3}{*}{Voltage Sensor} & INA219 & & 0-26 V & 0.2-0.5\% \\ \cline{2-5}  & LM24 & USD 6 & 6-36 V & 0.05 repeated \\  & & & & precision \\ \cline{2-5}  & Voltage Divider & USD 1 & 0-50 V & \(\pm\)1-\(\pm\)5\% \\ \hline \multirow{6}{*}{Temperature} & LM35 & USD 3 & \(-\)55-150 \({}^{\circ}\)C & 10 mV/\({}^{\circ}\)C \\ \cline{2-5}  & DHT-11 & USD 250 & 0-50 \({}^{\circ}\)C & 95\%, \(\pm\)2\({}^{\circ}\)C \\ \cline{1-1} \cline{2-5}  & DHT22 & USD 105 & 0-100\%RH; \(-\)40\(\sim\)80 \({}^{\circ}\)C & 0.05 \(\div\)2\(\div\)8 RH, \\ \cline{1-1} \cline{2-5}  & DSH18B20 & USD 3 & 55 to +125 \({}^{\circ}\)C & +0.125 \({}^{\circ}\)C \\ \cline{1-1} \cline{2-5}  & & & & +0.25 \({}^{\circ}\)C \\ \cline{1-1} \cline{2-5}  & DSH18B20 & USD 3 & 55 to +125 \({}^{\circ}\)C & +0.25 \({}^{\circ}\)C \\ \cline{1-1} \cline{2-5}  & & & & +0.5 \({}^{\circ}\)C \\ \hline Irradiance & LDR & USD 0.5 & 8-200 M ohm & \\ \hline Dust & GP2Y1010 AU0F & USD 12 & 0.5 V/0.1 mg/m\({}^{3}\) & \\ \hline Light intensity & GY-49 MAX44009 & USD 12 & 0.045–188,000 lx & 15\% \\ \cline{2-5} sensor & BH1750 & USD 2 & 1-65,535 lx & 20\% \\ \hline Humidity & HM2301 & USD 1 & 0–100\%RH & \(\pm\)3\%RH; \(\pm\)1 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 4: Meteorological and electrical sensors employed in existing literature.  
of such a system. This selection is made based on the range, operating frequency, topology, network security, power consumption, and cost of each communication module. We have analyzed the existing literature to identify the most commonly used communication technologies for monitoring, as detailed in Table 5.  
Bluetooth [6; 28] provides the highest bandwidth. However, the disadvantages are short distances and comparatively higher power consumption per bit transmission. The two viable options are Zigbee, observed by [29; 41; 42; 49], and Wi-Fi, observed by [2; 3; 7; 14; 20; 21; 31; 32; 35; 36; 37; 38; 41; 42; 45; 46; 47; 48; 49]. On the basis of this review, Zigbee and Wi-Fi have the potential to create local wireless sensor networks.