# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 2 Related Works  
Several research efforts have been dedicated to the clustering schemes for FANETs. This section provides an overview of the existing literature and an in-depth analysis of their principles and remaining technical limitations. The lowest ID clustering algorithm (LID) was proposed by [PERSON] et al. [7], where UAV nodes with lower IDs were selected as CHs. This algorithm is widely used, owing to its low time complexity and overhead of maintaining the clustering structure. However, the UAV nodes with lower IDs run out of battery faster than other nodes, because they act as the CHs and consume more energy, which finally leads to faster network partitions. LEACH-C (Low Energy Adaptive Clustering Hierarchy-Centralized) [8] is a classic centralized CH generation algorithm, where the base station is responsible for selecting CHs in the algorithm. The hybrid energy-efficient distributed clustering algorithm (HEED) [9] states that extending network lifetime and load balance are the main requirements in FANETs. The network lifetime is extended by evenly distributing energy consumption across the network. The CH generation is carried out within finite iterations in HEED, which minimizes the control message overhead. To enhance the QoS performance in FANETs, an SDN-MQTT (Software Defined Networking-Message Queue Telemetry Transport) [10] scheme is proposed. The paper presents a hybrid structure of SDN and MQTT for battlefield UAV clusters, which is scalable and controllable for network topology and payloads. The SDN-MQTT structure can support flexible packet transmission and improve communication security. In [11], the authors proposed a predictive scheme combined with unicasting and geo-casting routes using trajectory information, which keeps track of the changing topology and increases link stability. In [12], the authors used the relative typical node degree to evaluate the stability of nodes when selecting CHs, but did not take the average distance between nodes into account. The author in [13] proposed an Energy Aware Link-based Clustering (EALC) route to improve network performance. EALC uses an improved K-means fitness algorithm, which adopts energy level and average distance for optimal CH selection. EALC achieves routing calculation optimization and energy efficiency, but it only considers UAVs with low mobility. A mobility prediction-based clustering algorithm (MPCA) estimated the link lifetime based on the location and speed information of UAV nodes [14]. The algorithm combines average connectivity, link expiation time, and the probability that a candidate node maintains its current status in CH selection, which can prolong the network lifetime and PDR, but the routing overhead of the algorithm is much higher because of the frequent updating of the network topology. In [15], a mobility control-based clustering scheme (MOOC) was proposed to maintain the clustering structure in FANETs. The weighted clustering algorithm (WCA) considered the influence of different physical factors [16; 17; 18]. This algorithm evaluates the ability of nodes to run for the CH and can adjust the corresponding physical factors, based on the different network characteristics. The algorithm in [19] selects only one CH in each cycle, increasing network routing overhead and time complexity. [PERSON] [20] solved the WCA's issue of lacking network communication security in CH selection, and improved the encrypted authentication mechanism in FANETs. The authors in [21] proposed a weighted stable clustering algorithm (WSCA), which comprehensively considered four physical factors, and each node in the network maintained its own neighbor table for CH election and link communication. [PERSON] et al., presented a cluster-based location-aided routing protocol (CBLADSR) to address network security and stability issues [22], which is a heuristic-based clustering scheme to select CHs and achieve cluster formation. The main task of CBLADSR is to reduce end-to-end delay in the FANET.