## 4 Proposed Network Architecture  
The fundamental paradigm behind the UAV computing-based surveillance system for SAR is to employ UAV computing systems for fast scanning the disaster regions with the help of smart devices (e.g., camera, human-computer interfaces, and integrated computing modules) and a model installed on the drones. This system helps pinpoint precisely where fast and early assistance is needed to serve people in a localized area. Figure 5 depicts an example of automated surveillance and search operations. In Figure 5, after identifying the position of individual SAR, the GPS location of humans may be given to the SAR team for a quick and efficient rescue. The recent success of explainable AI techniques for object identification and activity recognition has led us to use AI techniques in UAV computing surveillance. The use of an AI-leveraged system will provide fast identification and localization of outbreaks in forest areas or disaster areas, though the AI-enabled UAV surveillance system requires a large amount of data for training. Furthermore, AI techniques employ the gathered data to train UAV models to perform various tasks, including object classification and localization of outbreaks in vulnerable areas. The integrated computing modules of image processing and computer vision enabled by AI techniques can automatically perform future extraction and representation for accurate classification of different objects in futuristic applications of drone studies. Moreover, the deep multimodal system with CNN architectures needs large datasets for fast computations, which can be obtained from sensor units, and can perform better calculations and analysis of objects. The deep learning-based computing models may be more suited  
Figure 4: Artificial neural network model. for image-based feature extraction than any neural network utilized for classification and localization. UAVs, SAR, the disaster center unit, and the ad-hoc network are all part of the proposed network design, as shown in Figure 2. Our provided network architecture is appropriate for all stages of a crisis. UAV-assisted SAR network capabilities will play a critical role in disaster preparedness. Sensors onboard the UAV, for example, are utilized to gather physical data in a disaster region and transmit data to the center unit of disaster management. Images of the impacted region are captured using the camera. As a result, data collected by a UAV fused with SAR were evaluated in this study to aid future SAR operations. Our suggested UAV-assisted SAR network delivers precise and suitable disaster management and SAR team guidance based on disaster event updates during a crisis. Then, UAV computing significantly enhances the information available to the SAR team and improves safety, as well as helping SAR personnel do their jobs successfully and efficiently. The ad-hoc network aids in the reconnection and delivery of previously disconnected communication services. A UAV-connected SAR mission is more efficient in supplying deliveries during the recovery period. A UAV equipped with the data acquired from the smart devices significantly improves the performance of a SAR mission. Furthermore, the information gathered may be utilized to assess damage and determine the need for rebuilding. Based on data collected by UAV IoT sensors, the ad-hoc network provides mission critical input to a central disaster unit located in a safe place. The role relies mainly on the link between UAVs and the SAR team during a disaster event and recovery. An ad hoc network links heterogeneous nodes with UAVs and the catastrophe unit center for processing, as illustrated in Figure 5. UAVs and SAR team individuals in the impacted region and at the catastrophe center are examples of heterogeneous teams. The suggested cooperation tools help people plan for and recover from disasters. They meet numerous requirements, including real-time imaging, deployment, high-resolution picture output, energy efficiency, connection, and dependability.