# Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint
## 1 Introduction
These literatures perform the construction of UAV network topology by employing clustering scheme, in which the topology stability is usually used as an evaluation index after the topology construction, rather than an optimization objective for the topology construction. In addition, the topology stability, network throughput, end-to-end delay and energy consumption have not been comprehensively considered in the process of topology construction. In this paper, we focus on the method of network topology construction for UAV swarm based on the criterion of topology stability, while fully consider the system performance of UAV swarm. Generally, topology stability can be represented by topology duration, which refers to the time interval from the establishment of the topology to its reestablishment. On the other hand, clustering is an important method widely used in network topology construction, which has two critical steps, i.e, clustering and cluster head election. In this paper, clustering method is employed and an innovative design is made for the above steps by jointly considering the characteristics of the swarm system and the operation mechanism of the clustering network. The main innovations and contributions of this paper are as follows:  
(1) A novel model of network topology construction for UAV swarm systems is proposed, in which the topology construction is formulated as a problem of maximizing the topology duration while satisfying the constraints of specific network throughput, end-to-end delay and nodes energy consumption of the system. (2) A novel group trend similarity based double-head clustering method (GTSC) is proposed to solve the optimization problem. The proposed method takes full account of the current state and movement trends of the swarm system. In the process of clustering, based on the group mobility trend similarity of the swarm system, clusters are formed with nodes in close proximity and with similar mobility trend in order to maximize the topology duration. In the process of cluster head election, we take full account of mobility similarity, energy consumption strategies, and communication performance related factors including distance between nodes and inter-cluster hops. (3) In the simulations, we employed algorithms with fast convergence speed Minimum Spanning Tree (MST) and K-means, representative swarm intelligence algorithm Particle Swarm Optimization (PSO) that is capable of achieving approximate optimal solution, a newest algorithm Improved Weighted and Location-based Clustering (IWLC) where factors of residual energy ratio, adaptive node degree, relative mobility and average distance are considered, and a state-of-the-art double cluster heads based algorithm DCM with load-balancing to perform the performance comparisons. The proposed GTSC method is more superior to the above algorithms in the performance of topology duration, network throughput, end-to-end delay and energy consumption balance. The rest of the paper is organized as follows: Section 2 discusses work related to topology construction methods for UAV swarms. In Section 3, we model the UAV swarm system and formulate the problem of network topology construction. The proposed topology construction method GTSC is described in detail in Section 4. Section 5 gives simulation results and performance evaluations, and Section 6 concludes the paper.