# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
## 4 Results and Discussion
### Capability of PGP as a Remote Monitoring System  
The adaptive software algorithm formulated using Equations (6) to (9) is instrumental in our approach. The initial step entails determining the solar irradiance value without a laser. This is achieved by calculating the average value (\(\overline{\
abla}_{\mathrm{si}}\)) over 256 iterations or within the time frame spanning from t\({}_{1}\) = 0 s to t\({}_{2}\) = 5 s. Subsequently, this value is translated into an ADC (Analog-to-Digital Converter) value, which falls within 0 to 1023 discrete analog levels. The outcome of the \(\overline{\
abla}_{\mathrm{si}}\) calculation establishes the threshold value. If there is a significant change of more than 40 ADC units, the system reads \((\overline{\
abla}_{(\mathrm{si+laser})})\) over 256 iterations to ensure the obtained value stabilizes before synchronization for data reception. The threshold value of 40 ADC was determined through testing, as depicted in Figure 13. These tests were conducted under three distinct lighting conditions (Ix): dark (Ix = 4 lux), low light (Ix = 293 lux), and normal light (Ix = 392 lux). Measurements were taken from 20 m, considering that the drone's operational range typically spans 10 to 20 m. This range ensures the effectiveness of the camera's field of view for monitoring suspicious  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Environmental Light**} & \multicolumn{3}{c}{**Detection Range (m)**} \\ \cline{2-4}
**Intensity (lux)** & **Triangle** & **Square** & **Pentagon** \\
**I\({}_{\mathbf{o}}\)** & **(I\({}_{\mathbf{x}}\) = 398 lux)** & **(I\({}_{\mathbf{x}}\) = 450 lux)** & **(I\({}_{\mathbf{x}}\) = 496 lux)** \\ \hline \(0-1\) & 14 & 20.5 & 15.3 \\ \(160-170\) & 13 & 18.4 & 14.3 \\ \(380-390\) & 10.1 & 17 & 12.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Light shape detection results.  
Figure 12: Object classification results.  
activities. It is important to note that the solar panel's input voltage value plays a pivotal role in these calculations in the absence of laser reception.  
The findings reveal that, at reading distances exceeding 10 m, the disparity between \((\overline{\
abla}_{\text{(si+laser)}})\) and \(\overline{\
abla}_{\text{si}}\) exceeds 200 mV or surpasses 30 ADC discrete analog levels. These results significantly facilitate calculations to distinguish between ambient light and laser-generated light. Moreover, it is observed that as the distance between the laser and the solar panel increases, the solar panel's input voltage experiences a corresponding rise. This phenomenon is attributed to the larger radius of laser beam divergence, as illustrated in Figure 14. Additionally, under darker environmental conditions, the disparity value becomes more pronounced, enhancing the precision of the readings.  
In this experimental phase, air-based data transmission involves the transmission of only 8 bits of data. The successful transmission and reception of identical data are verified through the serial monitor. These data functions are akin to RFID technology, enabling the extraction of information from PGP (Prescribed Ground Point), including the capability for PGP to relay timestamps to the cloud, serving as check-in information. The research demonstrates that due to the ample surface area of the solar panel, the system can receive data even when the laser experiences shifting or vibration caused by the drone's unstable hovering. It is important to note that this preliminary investigation does not prioritize factors such as bandwidth or delivery speed; these aspects will be explored in subsequent phases. From this initial trial, it can be deduced that the size of the solar panel and the choice of LED shape significantly influence the ease of detecting and transmitting information.