## 1 Introduction  
The fast growth of unmanned aerial vehicles (UAVs) has transformed the premise of wireless communication technology. UAV networks can collect data from sensors, connect with ground users, and provide Wi-Fi coverage in areas where humans are unable to access. The application of UAVs has not been limited to only military purposes as when they were first developed. UAV networks are proving to be a beneficial and appropriate technology for a wide range of civilian and military purposes. UAVs enable us to perform risky and complex operations such as firefighting, monitoring regions affected by natural disasters, launching and tracking cruise missiles, and remote surveillance [1]. Recently, UAVs are being made more economical and smaller in size, and the availability of autopilot software raises their popularity in the public and private sectors [2]. UAVs are used for a variety of purposes, including agriculture [3], remote sensing [4], forest fire detection [5], patrolling [6], and providing communication facilities in remote and disaster-prone areas [7, 8]. However, in order to carry out tough tasks, UAVs must be able to communicate effectively. Despite their numerous applications and benefits, drones are frequently referred to as \"terrorism by joystick\" to emphasize their negative effects [9]. Smuggling and other criminal operations, invasions of privacy, terrorist attacks, cyber assaults, surveillance, unauthentic monitoring, and so forth are all examples of UAV abuse. The growing number of crimes has highlighted the significance of identifying and disabling unlawfully deployed drones. In [10], the authors discuss techniques for identifying drones in the presence of flying objects such as birds. In [11], the authors propose a deep learning method for detecting malicious dronesusing image and audio data. Similarly, in [12], deep learning-based drone detection and type recognition based on radio frequency emissions are proposed. The goal of this study is to find and identify illegal drones that could endanger civilians. UAV networks are classified into two forms based on the number of deployed UAVs: single-UAV networks and multi-UAV networks. In the case of a single-UAV network, the UAV is connected to a base station or satellite. On the other hand, in a multi-UAV network, numerous UAVs are interconnected and interact with one another to accomplish the mission. A multi-UAV network provides benefits over a single UAV network with self-recovery capabilities. As a result, in a multi-UAV network, effective UAV collaboration is required to complete tasks. This necessitates the establishment of an inter-UAV wireless communication network [13]. The current flying ad hoc networks (FANET) suffer from a lack of collaborative interaction between UAVs, emphasizing the requirement for a better routing solution to improve data transmission between UAVs. Furthermore, in the FANET network, data transfer is problematic due to the irregular distribution of UAVs, their rapid mobility in three-dimensional space, and frequent topology changes. In order to address the issue of existing FANET, a UAV ad hoc network (UANET) was established by combining mobile ad hoc networks (MANET) and vehicular ad hoc networks (VANET) to develop a novel network topology incorporating homogeneous or heterogeneous flying agents known as UAVs. The term \"UANET\" refers to a network comprising several UAV nodes. UANET addresses the leakage of centralized and cellular communication in the UAV communication network. UANET is predominantly a multi-UAV network with several characteristics distinct from MANET and VANET. UAVs can function as clients, routers, and servers in UANET and collaborate to complete tasks [14; 15]. It is a very dynamic network, with new nodes joining and old nodes leaving very frequently. As a consequence, the network's topology could alter at any moment, which is a key characteristic missing from wireless communication networks [14].