# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 1 Introduction  
Unmanned Aerial Vehicles (UAVs) represent groundbreaking technology with diverse applications ranging from defence operations to disaster management and surveillance [1]. These UAVs function within Flying Ad-hoc Networks (FANETs) and can be controlled remotely either manually or through pre-programmed algorithms [2]. Their communication capabilities are facilitated by various modules, such as transceivers and antennas, allowing communication through different technologies like Reliability Factor (RF), satellite, and cellular [3, 4]. Establishing network topology within FANETs is critical, and UAVs play a central role in this process. The different topologies, including star, mesh, cluster, and hybrid-mesh, offer varying degrees of connectivity and flexibility [5]. In a star topology, direct connections are established between the ground station and each UAV. Conversely, mesh topology involves a single cluster head linking to the ground station and relaying data among UAVs. Cluster-based topology extends this concept by connecting the ground station to multiple cluster-head UAVs, while hybrid mesh networks allow cluster heads to relay information, creating a dynamic network structure [6]. Communication stands out as the primary method for wireless connections in FANETs, ensuring efficient and reliable communication between UAVs and ground stations. This comprehensive communication infrastructure is essential for enabling the seamless operation of UAVs across various applications and scenarios. To ensure intelligent connectivity among Unmanned Aerial Vehicles (UAVs), cognitive radio emerges as a valuable tool. Think of cognitive radios as smart devices capable of quickly adapting to different situations. They can make necessary adjustments to parameters like transmitting power, carrier frequency, and modulation strategies based on the current conditions [7]. This adaptability is crucial for optimizing connectivity within the network. When these cognitive radios interact with each other, they form what is known as a cognitive radio network (CRN). It is essentially a sophisticated wireless communication network where the radios can continuously adjust their connectivity parameters to enhance the overall performance of the system [8]. With the rising deployment of Unmanned Aerial Vehicles (UAVs), there is a growing need for streamlined routing and energy-saving strategies [9]. This is crucial to strike a balance between minimizing network delays, ensuring secure data transmission, and upholding data integrity. The primary objective of UAV networks is to exchange of information between UAVs and Ground Stations (GS), relying heavily on efficient routing protocols to facilitate intelligent communication [10]. However, crafting effective routing solutions for UAV networks, particularly in dynamic, densely populated, and energy-sensitive environments, presents notable hurdles [11]. The challenge lies in finding ways to prioritize energy-efficient UAV-to-UAV communication while still upholding Quality of Service (QoS) standards [12]. In this article, a routing protocol for CR-UAVs is presented, treating the problem as a weighted graph issue. It incorporates parameters like distance, speed, direction, link quality, energy, and reliability to optimize route selection, aiming for faster and more reliable data communication. A key feature of the proposed protocol is the introduction of the Central Node Resolution Factor (\(CNRF\)). This parameter, influenced by common idle channels and transmitting weight value (\(\xi_{T}\)), guides routing path decisions. Utilizing the Received Signal Strength Indicator (RSSI) for distance estimation enhances data forwarding and improves network performance in dynamic environments. To address the challenges of high mobility, the protocol employs predictive algorithms to anticipate neighboring nodes' future locations, effectively preventing link failures and packet drops.