# Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
## 4 Heuristic and System Model  
The main goal of the proposed IM is to improve communication connectivity by providing more stable communication links. As a consequence, it increases the reliability of the delivery of messages between the UAVs. Therefore, its use of a set of conditions that are essentially composed of the following parameters: quantity of bytes received, quantity of bytes lost, network throughput, the received signal strength indication (RSSI), and the signal to noise ratio (SNR). These parameters are used to analyze the strength of a connection established between neighboring nodes that use the same network interface. A strong connection is defined by the composition of various network conditions dynamically evaluated during the mission according to calculated network metrics and to the sensed metrics, calculated online, during each UAV journey. These conditions define a decision heuristic used by the interface manager to make decisions about which interface will provide a strong a connection. Equal weights for each metric were adopted, aiming to maintain connectivity and stability in the communication links, given that these metrics have the same importance in this kind of high-mobility wireless networks. So, it is not enough to simply have a larger volume of bytes received, but also how often these bytes are received and with which quality they are received. Otherwise, if, in the presence of high loss and low RSSI, such received bytes could be constituted by trash or truncated messages. So, when heterogeneous communication is used, a set \(INT\) is constituted, formed by all communication interfaces that integrate the system. The decision conditions must be evaluated for each communication interface \(INT_{x}\) based on the last sample interval. The heuristic currently adopted is based on a rewards mechanism, where each interface accumulates points based on its performance through the defined decision conditions. Thus, a given interface \(INT_{x}\) increases one every time its decision condition \(c_{i}\) is better classified, as shown in (1),  
\[INT_{x}^{c_{i}}= \begin{cases}1,&\text{if }c_{i}^{x}\text{ has the best performance},\\ 0,&\text{otherwise},\end{cases}  \tag{1}\]\(c_{i}^{x}\) stores the value of a network metrics \(i\) from Table 1, sensed or calculated using an interface \(x\) in the last messages sent/received by each UAV. Thus, the intensity of a connection is defined by the sum of the points obtained from a comparison of the decision conditions obtained by each interface. The proposed IM is distributed in a set \(X=\{x_{k}\}_{k=1}^{|X|}\) of UAVs that are part of an ad hoc network established for a mission. Each UAV represents a mobile node belonging to the network, which must be able to communicate with each other in order to enable the sending of the execution status of the tasks, sharing goals in mission time. Here, a decision tree (DT) is used to evaluate which communication interface will provide the strongest connection for sending the next group of messages within the current sample interval. The quality of communication is evaluated based on the communication of a node with its neighbors within the communication range. Notably the DT is run in a distributed way, i.e., it runs separately within each UAV node. The entire process of deciding which interface will be used occurs based on the data of the wireless medium collected by a determined UAV and on the network metrics sensed in the communications with its neighbors. Table 1 presents the metrics used in the DT. Each of these metrics is applied as a condition \(c_{i}\), as stated in Equation (2). The IM captures information either through directly sensing the medium or by calculating each decision condition. Therefore, it uses the last frames received by the UAVs, verified at each sample interval \(N=n_{0},n_{1},n_{2},\ldots,n_{n}\) defined for a new metric sample.