# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
###### Abstract  
Flying ad hoc networks (FANETs) have been gradually deployed in diverse application scenarios, ranging from civilian to military. However, the high-speed mobility of unmanned aerial vehicles (UAVs) and dynamically changing topology has led to critical challenges for the stability of communications in FANETs. To overcome the technical challenges, an Improved Weighted and Location-based Clustering (IWLC) scheme is proposed for FANET performance enhancement, under the constraints of network resources. Specifically, a location-based K-means++ clustering algorithm is first developed to set up the initial UAV clusters. Subsequently, a weighted summation-based cluster head selection algorithm is proposed. In the algorithm, the remaining energy ratio, adaptive node degree, relative mobility, and average distance are adopted as the selection criteria, considering the influence of different physical factors. Moreover, an efficient cluster maintenance algorithm is proposed to keep updating the UAV clusters. The simulation results indicate that the proposed IWLC scheme significantly enhances the performance of the packet delivery ratio, network lifetime, cluster head changing ratio, and energy consumption, compared to the benchmark clustering methods in the literature. unmanned aerial vehicle (UAV); K-means++ clustering; cluster head selection; cluster maintenance; flying ad hoc network (FANET) 2022 22202  
station (GCS) and UAVs, and constrained resource supply [6]. UAVs are equipped with polymer lithium batteries that sustain the flight time. Moreover, the high mobility of UAVs exacerbates frequent topology changes in the FANET. Thus, the effective deployment and management of UAVs can significantly enhance network stability. Clustering, as a method of network organization, can improve the network performance by increasing the network lifetime and packet delivery ratio (PDR), while reducing the routing overhead. UAV swarms can accomplish efficient deployment and stable communication in the clustering structure. Specifically, the overall network is separated into clusters in the clustering process. A cluster is composed of a cluster head (CH) and cluster members (CMs). The burden of CHs is heavy in clustering networks because they must manage the changes in CMs and transmit packets. The CHs are chosen by a selection algorithm that takes into account the significant physical variables of the candidate network nodes. Because the initial cluster formation and CH selection are critical to the clustering structure, it is necessary to develop an effective clustering scheme for enhancing the Quality of Service (QoS) of FANETs. To address the related issues, an Improved Weighted and Location-based Clustering (IWLC) scheme is proposed to ensure effective clustering, link stability and network lifetime in this paper. A location-based K-means++ clustering algorithm is first developed to determine the number of clusters in a FANET and set up the initial clusters. Secondly, we propose a CH selection algorithm, where the remaining energy ratio, adaptive node degree, relative mobility, and average distance of a UAV node are all involved in the selection criteria. Furthermore, a cluster maintenance strategy is presented for enhancing the stability and robustness of the clustering network. Simulations have been conducted to analyze the performance of the proposed IWLC scheme. The simulation results indicate that the proposed scheme outperforms the benchmark clustering schemes in the literature, regarding the network lifetime, PDR, cluster head changing ratio, throughput and energy consumption. The main contributions of this paper are summarized as follows:  
1. An improved weighted and location-based clustering (IWLC) scheme has been proposed for FANETs, to improve the network performance. The proposed IWLC scheme is composed of an initial cluster formation, CH selection, and cluster maintenance. 2.