## 2 Related Work  
Effective management and coordination of communication among UAVs is a critical aspect of UAV networks. Various standardization organizations have initiated standardization processes for UAV networks and FANETs. For instance, the IEEE 802.11 series includes multiple protocols and modes suitable for FANETs, such as IEEE 802.11s (mesh \begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Weight** & **Altitude** & **Active Range** \\ \hline Very small & 2–5 lb & \textless{}1000 ft & 1–5 km \\ \hline Small & 5–20 lb & 1000–5000 ft & 10–100 km \\ \hline Medium & 100–1000 lb & 10,000–15,000 ft & 500–2000 km \\ \hline Large & 10,000–30,000 lb & 20,000–40,000 ft & 1000–5000 km \\ \hline Tactical/combat & 1000–20,000 lb & 10,000–30,000 ft & 500–2000 km \\ \hline \end{tabular}
\end{table} 
Table 1: UAV classification [9]. networking), IEEE 802.11p (vehicle communication), and IEEE 802.11 ah (low power long distance communication). Additionally, ITU-R M.2171, 3 GPP TR 36.777, and ISO/IEC 29180 discuss concepts, application scenarios, deployment and management issues, and safety requirements related to FANETs. Different from a vehicular ad hoc network (VANET) or mobile ad hoc network (MANET), specific network topologies and operating mechanisms are required due to characteristics in the UAV network such as high mobility, frequent topology changes, unstable wireless links, and limited energy resources. Therefore, clustering algorithms in UAV networks need to be specially designed compared with other statistic or low mobility ad hoc networks. The clustering algorithms in UAV networks have recently been widely discussed. For example, the authors in [12; 13] provided overall investigations of the clustering algorithms in multi-UAV networks and compared them qualitatively. Aiming at the characteristic of high mobility, in [14], a mobility and location-aware stable clustering (MLSC) mechanism is proposed by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resources. Additionally, [PERSON]. [15] proposed a location-based k-means UAV clustering algorithm by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resources. However, clustering algorithms in [14; 15] mainly focused on the location and path of movement of the UAV, while the energy consumption of the UAV is not considered. To enhance the lifetime of the network, Ref. [16] proposed the energy aware link-based clustering (EALC) model based on the k-means algorithm for the selection of cluster heads. The node energy level and distance to neighbors are jointly considered when selecting cluster heads. EALC improved the cluster lifetime and reduced the overhead. In [17], a bio-inspired clustering scheme for FANETs (BICSF) is proposed based on a hybrid mechanism of glowworm swarm optimization (GSO) and krill herd (KH). UAVs' residual energy and lucirlein levels are considered when electing cluster heads and a KH intelligent algorithm is utilized for cluster management. However, Refs. [16; 17] are relatively vulnerable when UAV nodes are of high mobility. [PERSON]. [18] proposed an energy balance and mobility prediction (EBMP) clustering algorithm which considered the balance of the energy consumption and stability of UAV nodes when selecting cluster heads, while the otherness of size of different clusters is not mentioned. Moreover, in [6], the authors proposed an energy-efficient swarm-intelligence-based clustering (SIC) algorithm based on particle swarm optimization (PSO), in which the particle fitness function was exploited for intercluster distance, intracluster distance, residual energy, and geographic location, therefore the network energy consumption was reduced and the network lifetime was extended. Survivability refers to the ability of a system to remain alive or continue to exist while facing natural or man-made interference or disaster.