**1. Introduction**

The biggest advantage of Unmanned Aerial Vehicles (UAVs) is three-dimensional mobility with a high degree of freedom, and the relatively low cost of the devices, which leads to the possibility of large-scale operation [1]. For instance, [2] expands the network infrastructure by using UAVs as an Access Point (AP) with mobility, and [3] deploys a scalable surveillance network with three-dimensional vision of UAVs. To operate these UAV applications well, there are numerous requirements regarding the networking. In particular, since the UAV network needs to be operated under various conditions, it needs to be resilient to dynamic changes of topology, intermittent links failures, resource constraints, three-dimensional mobility, equality on link replacement, and so on [4]. Furthermore, to fully use the multi-UAV large-scale, fast, and flexible mobile wireless network, designing a high-performance multi-hop UAV network is regarded as one of the core objectives of the UAV industry, which has been continuously addressed [5–8]. Compared to the importance of these network design trends, research into UAV networks has suffered from the lack of applicability or the reliability, due to the variety of environments and the hardware specifications. In particular, from the point of the view of energy efficiency of multi-hop UAV networks, there are serious leaks in the power consumption of communications, which also has the potential to degrade the throughput of the entire network.

In a conventional UAV network configuration, one UAV transmits the messages with the same power level to all UAVs in its transmission range. As shown in Figure 1a, a UAV makes the link connection to all UAVs which are in its transmission range, with the same transmission power *PTx*. Indeed, constructing *full* connections provides strong stability to the network. However, such topology with indiscriminate transmissions generates an inefficient UAV network by continuously generating

more power consumption than actually needed, which highly reduces each UAV's network operating time. Also, the transmission power exceeding the minimal requirement on the links of the UAVs increases the possibility of the interference or unexpected silence of the wireless medium, which drops the channel use. Although there were some studies actively controlling the transmission power [9–11], the targeted network topology is constrained such as WLAN, and the existence of the centralized coordinators could limit the extensibility of multi-UAV operation. To resolve the power consumption problem, constructing a Minimum Spanning Tree (MST) of the network and minimizing the transmission power can be desirable. Figure 1b shows the graphical representation of the network topology, which is shaped like a MST. *PTx*,*<sup>i</sup>* refers to the transmission power of *i*-th UAV, which is managed by the central or global controller. The root of the tree might be the gateway or the sink node of the UAV network. Although this centralized way can highly reduce power consumption with less routing overhead [12], its resulting topology can also bring connectivity issues. If all UAVs are connected by only a few paths, the energy consumption per hop is reduced but the overall network connectivity becomes unstable due to there being fewer options to route, and this is critical in UAV networks which have high mobility. Furthermore, increasing hop count can cause higher power consumption compared to a smaller hop count connection, and increase the forwarding overhead.

**Figure 1.** Existing network topologies. (**a**) Conventional topology constructed by the *Tx* threshold; (**b**) Conventional topology constructed by the global MST.

To find the proper design of the network topology, we propose a novel distributed topology control scheme such that each UAV variably adjusts the transmission power while maintaining the efficient link connections through the space partition method. The core motivation of our concept is the intermediate layer design between the data link layer (L2) and the network layer (L3). As the network layer constructs a routing table using all its nearby UAVs, the number of the available links is too large in a dense environment, so some links might be inefficient due to the relatively large distance, interference, or hidden terminals. On the other hand, although the data link layer can control the transmission power, it cannot consider the packet-level power control by itself; the data link layer does not know the proper power for each hop. If a layer that designs the *topology* of the network prunes the links and determines proper transmission power, then it can help the network layer design a more efficient routing table, while making the data link layer transmit with more efficient power. We called this concept of the intermediate layer the *topology control layer (L2.5)*. By explicitly controlling the available links of the UAVs, it can reduce the power consumption while maintaining the robustness of the network connection.

Pruning the links in the topology control layer is based on the space partition, to gain the advantage in the link cost. Figure 2 shows the graphical representation of our proposed topology control scheme. Periodically, each UAV equally divides its transmission space into several partitions. Then, from each partition, the UAV picks one or more nearby UAVs and removes the other links. In Figure 2, *PTx*,*<sup>k</sup>* refers to the determined transmission power of the link between the UAV itself and

the neighbor UAV, which is one of the nearest ones in the *k*-th quadrant. By doing so, the UAV network can maintain its topology where each UAV has multiple links with nearby neighbors, which has a chance to dynamically control the transmission power at each hop.

**Figure 2.** Example of topology control layer with 4 partitions.

Based on the suggested topology control method, we implemented the simulation to legitimize the performance improvements in the UAV network. From the numerical analysis, it is shown that topology control layer has a gain in throughput, network stability, and energy efficiency in the UAV network. Our contribution can be summarized as follows.


In the following sections, we provide further explanation of our research. In Section 2, we introduce previous research on UAV networking technology. In Section 3, we present our methodology that explains the system overview. The evaluation of our methodology is presented in Section 4, and Section 5 lists the discussions about the simulation results. Finally, we conclude our work in Section 6.
