## 2 Related Work
The authors of [28] integrated a convolutional neural network (CNN) and an autonomous drone with a thermal camera to locate victims quickly and efficiently in disaster zones. The experimental results confirm that detecting victims from an aerial thermal view was achieved with AP = 82.49%. However, the drone's altitude and coverage range were not clarified. Moreover, in [29], the CNN also used autonomous drone-surveillance and a RGB camera for search-and-rescue in natural disasters at 40 m altitude. The results confirm that detecting accuracy of victims from an aerial view was achieved with 98%. Further image processing and optimization techniques are introduced in [29; 30], where a drone-based system is developed to detect the breathing movements of casualties or lost people within natural-disaster zones. The results show reasonable precision average measures from altitudes ranging from 2-8 m. Further work is recommended, involving tuning to high altitude and high image resolution, to gain better results. The authors of [31] presented a multifunctional drone combined with a machine learning (ML) approach that helps in fighting against the coronavirus pandemic. The proposed drone, with its payload of thermal cameras and ML, can not only detect people wearing face masks in public for security reasons, but also help to sense elevated body temperatures in order to minimize cross-infection through close contact. Furthermore, the proposed work pays attention to the optimization of communication links for better wireless connectivity and power consumption. The authors of [32; 33; 34; 35; 36] covered different techniques that can allow UAVs to divide the target area for search and rescue operations. Where snooping or strips are the most well-known searching techniques. In [37; 38], the authors emphasize the importance of UAV trajectory design, which affects not only the communication and link budget between UAVs and ground users, but also UAVs' energy consumption. They also considered a deep-reinforcement algorithm for trajectory design and power allocation optimization. In the literature, a few types of camera have been used for detecting humans and/or wildlife animals from a bird's-eye view on satellites or UAVs; they are mainly classified as thermal cameras and RGB cameras. However, this work focuses on using a thermal camera based on the heat signature of objects using mid- or long-wavelength infrared energy. It has the advantages of easy installation and usage, independent of light, the provision of fast and accurate measurements of objects, and limited privacy issues [39; 40; 41; 42; 43; 44; 45]. The authors of [46; 47] studied a drone's ad hoc network for mobility and service-oriented modeling using various protocols and techniques, such as neuro-fuzzy, and a quality of service provisioning framework for UAV-assisted environments. The results of these innovative approaches have emphasized the significant potential of enabling smart services. Based on our review, our motivations and contributions are as follows. Our study aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement using the least squares tuning algorithm. The optimum experimental configuration is considered to allow a drone with a thermal camera and other payloads flying over disaster zones in rugged areas to help identify lost people as part of robust and efficient search-and-rescue operations. The deployment of portable drones with thermal cameras and state-of-art 5G MIMO antennae, as well as sensors, for search-and-rescue operations is scarcely reported in the literature, let alone the optimization of a propagation model to ensure strong wireless connectivity and a wider coverage footprint. To the best of our knowledge, this is the first work to consider the Hata-Davidson propagation model from a UAV perspective, despite UAVs' advantages of accuracy, coverage, and correction factors.