# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 2 Background and Related Works  
The lowest identification (LID) clustering algorithm was put forward by [PERSON] et al. [11]. This algorithm is based on the least node ID cluster head election. The node is assigned a random ID and selects the neighbor nodes. The smallest ID node is the cluster head. The cluster structure maintenance method is based on the ID value. The LID algorithm has low computational complexity, but easily leads to an unbalanced load and has fixed cluster head selection. To improve the rationality of cluster head selection and enhance the adaptability of the scene, [PERSON] et al. proposed a weighted clustering algorithm (WCA) [12], which comprehensively considers the node degree, distance, and mobility. In the WCA, malicious nodes can be selected as CHs in the process of clustering, which in turn accompanies some security issues. For this reason, some researchers also propose improved or variant WCAs. According to the energy characteristics of the nodes, a series of clustering algorithms based on the number of nodes are presented. For example, the low-power adaptive clustering algorithm (Leach) selects CHs through random probability and changes CHs at certain intervals to balance the load in terms of the network energy and extend the network life. The disadvantage of Leach is that only the remaining energy of the node is considered, and the performance of this method is poor in the actual application process. Moreover, since CHs are assumed to be able to communicate with the base station, the method is not suitable for large-scale networks. Some researchers are inspired by biological behaviors and apply these for the clustering the FANETs. In [13], an ant colony optimization (ACO) clustering scheme is proposed for optimal clustering and data transmission in the FANETs. CHs are vertices in the search space, and each round provides the set of CHs from specific environments. The algorithm uses two objective functions to evaluate the fitness value of each cycle, namely, the Euclidean distance and the delta difference. In [14], the grey wolf optimization (GWO) algorithm is proposed for an energy-efficient routing protocol. This algorithm has the advantage of high robustness and accuracy, and its convergence rate is faster than similar algorithms. However, the disadvantage of this algorithm is that the convergence rate is too slow in the late stage, and it is easy to fall into the local optimal solution. In FANETs, maintaining network connectivity is an important consideration when designing UAV clustering algorithms. [PERSON] et al. [15] designed a connectivity degree-based high connectivity clustering (HCC) algorithm to implement UAV clustering. In the HCC algorithm, the node with the largest number of neighbors is first selected as the CH, and then a limited number of clusters is generated. Due to the movement of the UAV (i.e., the node), the number of neighbor nodes of each node may change, which means that the cluster head needs to be re-selected accordingly. Due to the frequent selection of cluster heads and multiple reconnections of nodes, the HCC algorithm has poor adaptability and reduced throughput for dynamic UAV nodes. Many researchers also combine some mature clustering algorithms with the characteristics of FANETs to design different UAV clustering algorithms [16]. The study [17] proposes a K-means-based (KMB) UAV clustering algorithm for the cluster head selection, and added a mobile agent to collect and fuse the data in the cluster. This algorithm overcomes the shortcomings of traditional K-means algorithm, and has the advantages, such as avoiding local optima, high precision solution and simple operations. The authors of [18] studied an evenly split ring-based UAV clustering (SRC) algorithm by applying fuzzy logic. In this algorithm, the energy of the node, the node degree, node-to-node distance, the ring width, and the staring energy parameters are utilized as the input of fuzzy rules. Then, the optimal number of clusters can be obtained.