## 4 Related Works  
The cluster-based routing protocol (CBRP) is one of the protocols of MANET for a two-tier network configuration with multi-node clusters and inter-cluster connections [42; 43]. Within a cluster, routing is divided into a network layer and an upper layer, and inter-cluster routing is established at the network layer [44]. The clustering can effectively improve the capacity for dealing with node mobility and for reining in the emergence of long route communications by cascading short route communications. The cluster head acts as the gateway (GW) of the cluster, has the relay function, and performs routing. This method uses clustering to accommodate node movements and cascade messages over short routes to reduce the load on long-distance communication. The algorithm selects the node with the \"lowest ID\" among its adjacent nodes as the cluster head. In other words, CBPR randomly selects nodes that operate the relay function. Therefore, it is not clear whether CBPR is an optimal method for selecting relay nodes. [PERSON]. [PERSON], et al. proposed Low-Energy Adaptive Clustering Hierarchy (LEACH), a clustering-based protocol in sensor networks [45; 46; 47]. This protocol randomly rotates local cluster base stations (cluster heads) to distribute the energy load evenly among the sensors in the network. [PERSON], et al. proposed an algorithm for the energy-efficient selection of cluster heads in a sensor network using the k-means method [48]. It applies machine learning to select nodes to build clusters, and to select the cluster heads from the clusters. [PERSON] al. proposed the optimal cluster head (CH)-selecting method based on fuzzy logic [49]. [PERSON], et al. reviewed atypical hierarchical routing protocols for wireless sensor networks [50]. [PERSON], et al. reviewed various clustering algorithms designed for WSNs, categorizing them based on performance metrics such as scalability, energy efficiency, and fault tolerance [51]. Their study provides a detailed analysis of the advantages and limitations of these algorithms, guiding future research in clustering methodologies. [PERSON], et al. propose a novel relay node selection scheme for WSNs using chaotic neural networks [52]. The method leverages the chaotic dynamics for efficient relay node selection, aiming to enhance communication reliability and reduce energy consumption in the network. [PERSON], et al. introduced a hierarchical semantic processing architecture designed for smart sensor networks used in surveillance [53]. It integrates semantic reasoning to enable intelligent data interpretation and decision making, improving the functionality and responsiveness of surveillance systems. [PERSON], et al. propose an adaptive topology management strategy tailored for large, dense, real-time WSNs [54]. This approach dynamically adjusts the network topology to optimize performance, enhance reliability, and maintain real-time constraints while minimizing energy consumption. [PERSON], et al. explores the application of reinforcement learning to select relay nodes in WSNs [55]. The proposed method aims to optimize network performance by dynamically adapting to changing conditions, reducing energy consumption, and ensuring reliable data transmission. [PERSON], et al. proposed a mobility-prediction-based virtual routing algorithm to enhance communication reliability in ad hoc UAV networks [56]. [PERSON], et al. developed a relay communication technique for multi-UAV networks, improving data transmission efficiency and network reliability [57]. [PERSON], et al. explored a multi-agent learning-based approach for packet routing in dynamic multi-hop UAV networks, enhancing adaptability and efficiency [58]. There have been several studies on sensor networks that create clusters of nodes and provide the relay function at the Cluster Head. There has also been a lot of research into controlling the position of UAVs as relay nodes in data transfer networks that use UAVs as relay nodes.