# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 2 Related Works  
Creating routing protocols for Unmanned Aerial Vehicle (UAV) networks in situations with high mobility and network density poses significant challenges due to the distinctive characteristics of these networks. In this section, we will discuss some of the existing routing protocols employed in UAV networks. The Reactive-Greedy-Reactive (RGR) protocol utilizes HELLO signals to keep tabs on the whereabouts and identities of nearby nodes. This helps in monitoring the positions and identities of neighboring nodes. The protocol relies on four types of control packets: Route Requests (RREQs), Route Replies (RREPs), Route Error (RERR) messages, and hello signals. While all these packets offer geographical information, they function similarly to the Ad Hoc On-Demand Distance Vector (AODV) protocol [13]. Despite employing a routing table, the RGR protocol is prone to message loss when the closest neighbor is unavailable, leading to the need for frequent path searches. However, it does leverage mobility prediction, which can be advantageous. Nonetheless, this prediction capability might interfere with tracking and search scenarios. In [14], a refined routing approach that utilizes Optimized Link State Routing (OLSR) is proposed to meet the low-delay requirements within Flying Ad-hoc Networks (FANETs). This scheme aims to minimize the routing overhead stemming from node movements, effectively addressing the challenges presented by the high speed of nodes and frequent updates typical in FANET environments. The proposed scheme integrates sophisticated OLSR techniques, taking into account various factors such as neighbor connectivity, power consumption, available bandwidth, and employs neural network-based methods for route computation. Through simulations, it is shown to enhance FANET services by reducing control message volume, improving routing efficiency, and achieving superior performance in metrics like packet delivery rate, throughput, and network stability compared to traditional OLSR approaches. The modified OLSR (MOLSR) protocol, which places particular emphasis on ensuring connection stability and optimizing node bandwidth utilization, emerges as a standout performer when compared to the standard OLSR protocol [14]. The CLRM-DSR protocol is designed to improve the efficiency of Dynamic Source Routing (DSR) in Flying Ad Hoc Networks (FANETs) by reducing routing overhead through the evaluation of network quality. Unlike traditional DSR, this protocol tackles issues like increased latency and the absence of endpoint connection information. CLRM-DSR achieves its goal of efficient routing by utilizing cross-layer resource control and connection quality indicators, which are employed in uni-cast routing queries and responses. It excels particularly in scenarios involving mobility and minimizing end-to-end delay. However, it is worth noting that CLRM-DSR may not be the ideal choice for extremely dense networks. This is because it does not fully account for factors like latency, energy efficiency, quality of service, dynamic topology changes, and network dependability [15]. Routing Approach (ICRA) is introduced to tackle the challenges posed by rapidly changing environments. ICRA comprises three key components: the clustering module, the clustering strategy modification module, and the routing module. This approach is tailored for UAV ad hoc networks, which consist of UAV nodes (NU) and a ground station (GS) facilitating communication between UAVs and the ground. It operates under the assumption that nodes are equipped with GPS and employ a network time protocol system for synchronization [16]. ICRA leverages a clustering algorithm to organize UAV nodes into clusters, ensuring a consistent network topology. When compared to fixed-weight approaches, ICRA demonstrates notable advantages across various experimental scenarios. These include faster clustering, enhanced topological stability, improved energy efficiency, and superior quality of service.