# Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint
## 4 The Proposed GTSC Method
### Topology Generation
When the number of nodes is less than 64, the end-to-end delay of MST, K-means, PSO-based, DCM and the proposed GTSC methods is close to the same. With small number of nodes, the benefits of the proposed GTSC method for optimizing the number of forwarding hops cannot be epitomized. As analyzed in the previous sections, the end-to-end delay in the swarm network is mainly determined by the number of forwarding hops, the MST method shows disadvantage, especially when the number of nodes increases. The end-to-end delay of the MST method is almost several hundred times higher than other methods when the number of nodes reaches 128 or more. ### Total Throughput

The total throughput of the network topology constructed by different methods under varies number of nodes is provided in Figure 7. As mentioned in Section 3.2, the throughput of network is mainly influenced by the number of forwarding hops and transmission distance theoretically. Simulation results as shown in Figure 7 matches the analysis. When number of nodes reaches 128 or more, the proposed GTSC method can achieve higher total throughput than any other methods due to the consideration of both distance and number of forwarding hops, which shows the advantage of the proposed GTSC method. And it can be assumed that our proposed method will outperform any other methods when number of nodes continue increasing. When the swarm size is small, for example, when \(N=32\), the PSO method has a better performance than the proposed method. This is due to the fact that the data transmission links constructed by PSO method does not require cluster heads to relay, which leads to lower forwarding hops. When the number of nodes increasing,

Figure 5: Comparison of topology duration under various methods. network topology constructed by PSO method can no longer ensure low forwarding hops, and therefore results in reduced throughput performance. ### Residual Energy Distribution

The energy balance of swarm network can be evaluated from the variance of residual energy of nodes. In this simulation, we compared the energy balance ability of swarm network constructed based on various methods from the view of the variance of residual energy of nodes. As shown in Figure 8, the residual energy variance of the proposed GTSC method performs the best compared with other five methods. Regardless of the number of nodes, the proposed method can always achieve lower residual energy variance than the initial residual energy variance. Simulation results indicate that the energy consumption of the nodes is well balanced when the topology is constructed based on the proposed GTSC method. The well performance of the DCM method in terms of residual energy distribution can also demonstrate the advantage of the double cluster heads scheme. Figure 6: Average end-to-end delay of swarm network constructed based on various methods. Figure 7: The total throughput of swarm network constructed based on various methods. ### Convergence Speed

As shown in Figure 9, the increase of number of nodes leads to lower convergence speed, especially for PSO method and PO-based DCM method. When the number of nodes increases, the computational complexity of the PSO and DCM methods increases exponentially, such a convergence performance makes the PSO and DCM methods unrealistic in practical. MST and K-means methods are the most typical and widely used algorithms. The most important feature of MST and K-means methods is their fast convergence, which is confirmed by the simulation results, the convergence time barely varies with increasing nodes. As a result, the K-means++ based IWLC method also achieves good performance in terms of convergence speed. Meanwhile, the simulation results show that the convergence time of the proposed GTSC method continues increasing with the number of nodes but still within acceptable range. The proposed topology construction method will not cause excessive computational complexity and is of practical interest. Figure 8: Energy balance of swarm network constructed based on various methods.