# On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
## 1 Introduction  
Unmanned aerial vehicles (UAV) put several challenges to the communication technologies due to strict requirements on quality of the connection and, specially, on reliability [1]. In addition, connectivity is also at risk due to physical scenario with open air and long distances between nodes, whereas one positive feature for assuring good quality of transmission is the low number of physical obstacles in the air. Many efforts have been dedicated to find optimal routing for communication between nodes, where the nodes may be UAVs and base stations and compose the so-called aerial Wireless Sensor Network (aerial WSN). Many of the algorithms for routing inside aerial WSN use the well-known Ad-hoc On-demand Distance Vector (AODV) [2, 3] routing, however routing algorithms such as Optimized Link State Routing (OLSR) can be also implemented. The handicap of AODV is that the path discovery requires a high amount of time for the establishment of the initial forwarding routes [4]. On the other hand, link state routing sends to all the network nodes information about the neighbors, which implies that many routing messages must be sent for the establishment of forwarding routes. Moreover, [PERSON] and [PERSON] showed that high dynamicity makes the use of OLSR difficult [5]. The communication technologies of drones consider two main scenarios: (1) The UAV connects directly with the control station and (2) the UAV connects with other UAVs as intermediate hopes to the control station (so-called UAV swarms [6; 7]). For direct communication between UAV and control station [8], there are two main technologies: The first one uses 433 MHz unlicensed band and has a limitation of about 2 or 3 km of distance between the control station and the UAV [9]. The other is based on commercial mobile networks that use licensed (reserved) bands for reliable communication. Mobile networks (3G/4G/5G) are used in the case that 433 MHz-based technology cannot be used for different reasons such as interferences, maximum distance, and unavailable technology. In communication through mobile network infrastructure, the UAV must maintain direct connectivity with the mobile operator (network operator) [10]. With this scope, aerial WSN nodes have, in general, omni-directional antennas; however, ground antennas may be both omni-directional or with beamforming technology. This technology consists of pointing the radioelectric beam in the direction of the UAV, which reduces the energy used in transmission [11]. In order to ensure reliability of communication between UAV and mobile network, parallel routing should be provided, while leaving to the UAV the possibility of selecting the most appropriate routing path and, preferably, connection adaptation should be possible in the case that current connectivity losses quality. If the UAV has several paths that may serve a connection, one of the tasks of the communication system is to select the most appropriate path for a given connection [12]. This selection should be based on the current parameters of the network, and, specifically, on the current parameters of each potential path [13]. This multi-criteria selection of routing is an optimization problem that is, in general, _NP_-complete. Several approaches have been taken to solve multi-criteria routing. One of the newest ones is the use of Machine Learning approaches to predict the behavior of the network based on current measurements. This is the approach used in this paper. We introduce a two-phase routing selection implemented with the use of Machine Learning algorithms. The two phases refer to the two necessary routing selection in the following links: (1) The link between the UAV and the mobile network base station and (2) the link between the base station and the control station (controlling the UAV flight). Our study is based on a measurement analysis of the connectivity between UAV and control station through mobile networks.