## 4 Experiment Setup and Results
### Experiment Results and Discussions  
Figure 6 illustrates the drone in the sky for search-and-rescue operations. The trial took place at the new campus of Taif university on 20 February 2021 at 1 p.m., at an altitude of 15 m. While the campus longitude is 40.4867323, and the latitude is 21.3320348. The reason why the trail took place on Taif university campus is that we obtained permission from the campus administration to fly drones at designated sites following the rules outlined by the university. Additionally, the ability to fly a drone over a limited number of people during lockdown was another reason to consider the campus as low-risk. Further reasons included accessibility to laboratories and hardware material, as well as the possibility to include some professional volunteers acting as missing people under objects such as trees or buildings. Figure 7 shows the weather conditions dashboard using the Blynk software application integrating the BME280 weather station sensor with the NEO6M GPS sensor, which were turn these connected to the Raspberry Pi 3 B+. Notably, Blynk is the most popular IoT interface platform for connecting any portable devices (e.g., laptop, mobile phone, tablet, smart watch) to the cloud. It is well known for allowing users to design apps to control their IoT devices, analyze telemetry data, and manage deployed products at scale. It can also support both Android and IOS operating systems. Five weather measurements besides altitude, longitude, and latitude were considered: temperature, pressure, humidity, wind direction, and wind speed. These parameters were considered due to their importance in determining the optimum flight configuration. The RSS parameter is useful when calculating path loss (PL) since it is related to the distance between the transmitter and the receiver through an adequate propagation model. Although RSS calculations are quite simple to perform, achieving an optimized propagation model might be the opposite. Therefore, optimizing propagation models is crucial for the IoT, as it is the key foundation for communication resource allocation, localization, interference management, and sensing. Propagation path loss is a useful approach for monitoring system performance, network planning, and coverage to achieve perfect reception. Thus, the proposed modified Hata-Davidson empirical model is a noticeable contribution of this paper for calculating path loss for two reasons. First, the consideration of the elevation angle in relation \(\theta\) to calculate the distance d that suits the drone's nature as a space-based system. Second, the enhancement of the Hata-Davidson empirical model's outcomes using experimental work with the least squares tuning algorithm. This adaptation yields improved coverage footprint, LoS connectivity, and RSS results from the receiver side. Figure 6: The proposed drone in the sky above Taif university campus for search-and-rescue trial. Figure 8 shows a GUI of the link budget plots of PL and the rest of the link budget parameters at an altitude of 15 m. Furthermore, it shows the Hata-Davidson model, modified-Hata-Davidson model, and measured data integrated between the communication payload on the drone and the receiver that connected to MATLAB. Notably, a MATLAB toolbox was used for predicting the link budget parameters. As can be seen, the PL increased with distance, while the RSS decreased as the PL increased. The average PL ranged between \(-150\) dB and \(-50\) dB, while the RSS ranged between 40 dB and 140 dB. Noticeably, the RSS was improved when increasing the transmitter altitude or transmission power. The SINR produced reasonable results: it increased as the transmitter altitude increased, due to the increase in coverage. However, T decreased with altitude, high PL, and shadowing.