**4. Evaluation**

We implemented our proposed space partition method (Algorithm 1) and the network topology simulation written in Python, and measured the numerical data to plot the results using MATLAB. We used OpenCV library to display the network topology for various cases. Also, we compared our scheme with the following network topologies to present the advantages of applying the topology control process to the UAV fleet network.


To examine the performance of the resulting topologies, we simulated a routing scenario for each case. We randomly sampled 50% of the existing UAVs, and searched the optimal route from each to all the other ones. We adopted Dijkstra algorithm [23] to find the shortest path to the target destinations. As mentioned in Section 3.1, our topology control algorithm can be any other path-finding algorithms, such as congestion-free ad hoc routing strategies discussed in [24]. Also, we set the distance between the UAVs as a link cost used in our Dijkstra algorithm, so the result of the algorithm is the most energy-efficient paths with respect to the topology of FC, SMST, and TC-*n*.

We claim that our exhaustive search can thoroughly validate the performance of the network topology, along with the stability, energy efficiency, and the network traffic. Commonly used routing protocols aim to optimize the routing table of each device. Analysis of the Dijkstra algorithm result shows the best path of each end-to-end connection derived from the network topology, so we derive the statistics from the optimal path to every UAV in the network. We evaluate the network topologies by the following metrics.


hop count not only increases the delay of the connection but also has the potential to drop the end-to-end throughput, since the packet is repeatedly propagated through the wireless medium per each hop. Thus, lower hop count results in less use of the wireless medium with low latency, which results in the overall throughput improvement of the UAV network.

• **Power consumption:** As discussed in Section 3, our proposed system determines the transmission power of each link. We summed the amounts of the transmission power required at all links on each end-to-end connection. In the case of FC, we assumed there is no power control method equipped, so the expected power consumption is the multiplication of the average hop count by the maximum transmission power. On the other hand, in other cases, the transmission power of each link is calculated from the distance using the Friis equation. Please note that excessively high hop count results in the higher power consumption of the end-to-end communication, despite the low power consumption due to the short distance of the links.

We evaluate the network topologies while varying the number of UAVs, maximum transmission power, shape of the formation, and the number of the partition *n*. Default value of each parameter is listed in Table 1. The following subsections discuss the evaluation result while varying the parameters.


**Table 1.** Simulation parameters.
