# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 3 Network Model
The clustering mechanism proposed in this article is designed to be independent of any specific Primary User (PU) activity model. For analytical performance evaluation, it adopt the Semi-Markov ON-OFF model, which characterizes PU traffic on any channel. In this model, channels can be in one of two states: busy (ON) or idle (OFF). The duration of these busy and idle periods are treated as independent random variables, reflecting the autonomous operation of PUs who have licensed access to the spectrum bands. Consequently, Secondary Users (SUs) only utilize the available idle channels and must vacate them whenever PU activity is detected. Additionally, it assumes the presence of a global common control channel within the network to facilitate this dynamic spectrum access [24]. The suggested protocol in the CR-UAV network stresses cluster formation, which is critical for improving network performance. A weighted graph algorithm is used to intelligently classify UAVs into clusters depending on their geographical closeness. CNs are nodes with a stronger impact, ensuring that they lead their respective clusters. This strategy improves spectrum use, communication reliability, and data interchange within the CR-UAV network, all of which contribute to the protocol's overall success. Central Node selection in the proposed protocol is vital for efficient cluster management within CR-UAV networks. The highest-weighted node within each cluster becomes the Cluster Head (CN), chosen for its strong connectivity and energy reserves. Furthermore, a secondary high-weight node assumes the role of secondary CN (SCN), providing support to the CN and ensuring network resilience. These criteria are central to the protocol's objective of enhancing network performance and resource utilization in CR-UAV deployments. The protocol prioritizes robust inter-cluster communication, clustering strong links between Central Nodes (CNs) and neighboring SCN within clusters. This strategic approach aims to facilitate seamless data exchange across clusters, achieved through a sophisticated routing mechanism enabling CNs and SCN to act as data relays when necessary. This enhancement significantly boosts the overall efficiency and performance of the Cognitive Radio UAV network. Our proposed protocol for Cognitive Radio UAV networks (CR-UAV) relies heavily on efficient cluster management. Each cluster continuously monitors nodes and nearby clusters for any issues, such as the sudden entry of Primary Users (PU), which can disrupt the network. When such issues arise, clusters may need to reform with new Nearest Networks (NN), potentially losing access to previous channels. To adapt, nodes may discover new channels while traveling, leading to changes in cluster membership. Some nodes may become disconnected and attempt to reconnect with the nearest network cluster or form their own. To mitigate energy depletion or communication interruptions, nodes proactively select new Cluster Heads (CN) or secondary CN (SCN) from high-weight nodes. This proactive approach enhances operational efficiency and network resilience, ensuring success in CR-UAV deployments. Figure 2 illustrates the overall process for our proposed cluster formation and maintenance protocol in Flying Ad-hoc Networks (FANETs). The flowchart is divided into several stages, each representing a crucial step in the protocol. 1. Start: The process begins with the initialization of the algorithm. 2. Node Detection and Identification: UAVs scan their surroundings to detect and identify neighboring nodes. 3. Broadcasting and Sharing: Detected nodes broadcast their information, including Adjacency Clustering List (ACL) and Node List (NL), to neighboring nodes. 4. Graph Construction and CNRF Calculation: Each UAV constructs a graph based on the received information and calculates the Cluster Node Relevance Factor (CNRF). Figure 2: Overview of the Proposed Clustering scheme. Cluster Joining Decision: The UAVs decide whether to join an existing cluster or attempt to form a new one based on the CNRF values.