# Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint
## 2 Related Works  
According to various optimization objective, typical topology construction algorithms are shown in Table 1.  
Lowest ID method [14] is a typical low-complexity algorithm that can be used to rapidly construct the swarm topology. In Lowest ID method, ID numbers are randomly assigned to all network nodes and several nodes with the lowest ID number are selected as the cluster head. The cluster members are ascribed to the nearest cluster according to the relative distance. However, the dynamic characteristics and the network performance after topology construction are not considered, which results in unsuitable for UAV swarm system with high dynamics and high network performance requirements. K-means algorithm is also a low complexity method used in network topology construction. The algorithm consists of two main steps: firstly, network nodes are divided into K clusters by a determined K value; secondly, all nodes are ascribed into the nearest cluster. Reference [15] uses K-means algorithm for fast construction of swarm network topology, and simulation results show that its construction speed is superior to the group intelligent algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Evidently, the choice of K value is a key step for the K-means algorithm. In [16], a Mobile and Location-aware Stable Clustering (MLSC) algorithm is proposed, which employs the K-means algorithm for initial topology construction. The value of K is settled by the coverage probability of nodes, while the ascription of cluster member is determined by relative distance. In general, since the K-means algorithm requires a pre-set K value, the K-means algorithm has limitations for application in dynamic scenario. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimization Objective** & **Representative Algorithm** & **Advantages** \\ \hline \multirow{2}{*}{convergence speed} & lowest ID clustering [14] & Low algorithm complexity and \\  & K-means clustering [15; 16] & fast conversion speed \\ \hline \multirow{4}{*}{energy consumption} & EALC [17] & Energy-efficient oriented: reduce \\  & SIC [18] & energy consumption of key \\  & BIC [19] & nodes and consequently improve \\  & IWLC [20] & the network performance \\  & CSBWOA [21] & \\ \hline \multirow{4}{*}{network performance} & DOCG [22] & Directly adopted the network \\  & CDSTC-JO [23] & performance as \\ \cline{1-1}  & DCM [24] & optimization object \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Typical topology construction algorithms. As an important performance indicator of UAV swarm, the energy consumption is usually needs to be considered in the system design [26; 27]. In the topology construction of UAV swarm, a common approach is adopted the energy consumption of UAV nodes as the optimization object. It is worth mentioning that reducing the energy consumption of certain nodes, such as cluster head nodes, also consequently leads to some performance gains of systems. An Energy Aware Link-based Clustering (EALC) method is proposed for the topology construction of swarm system based on the optimization of node energy consumption [17]. On the one hand, a reasonable transmit power is designed to reduce energy consumption of nodes according to the link conditions; on the other hand, the node residual energy combined with relative distance and nodes' degree are considered in the step of cluster heads selection. Since the nodes with higher residual energy are adopted as the cluster heads, this method consequently improves the stability of the network. Simulation result has shown that the method has better performance on energy consumption and network stability in comparison with the Binary Particle Swarm Optimization (BPSO) and K-means-based schemes. In [18], the authors propose an energy-efficient Swarm Intelligent Clustering (SIC) algorithm.