# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Adaptive
#### 5.1.1 Enhancing Routing Performance Using Reinforcement Learning  
[PERSON] et al. (2019) propose an adaptive routing scheme that enables each node to use reinforcement learning (RL), which is an artificial intelligence approach, to learn from its operating environment and to select the most suitable next-hop node as part of a route in FANETs. The main objectives are to enhance route stability (O.1) as well as to enhance routing performance and QoS (O.3) in FANETs. The routing scheme addresses two challenges, namely high dynamicity (X.1) of network topology as flying nodes have high mobility and cost (X.2), particularly energy consumption and computational cost. The routing scheme is designed for hop-by-hop routing (C.1.1), specifically a next-hop node is selected as part of a route. The underlying medium access control (MAC) has three main characteristics to support routing. Firstly, nodes exchange handshaking messages, such as ready-to-send, wait-to-send, and clear-to-send, to clear a channel in order to increase link reliability. Secondly, there are two transceivers used for concurrent transmission and reception of control messages and data packets, respectively. Examples of information in control messages are geographical locations and handshaking messages. Thirdly, directional antennas are used, whereby the antennas are directed properly (i.e., the antennas are directed towards the transmitter at the receiver and towards the receiver at the transmitter) after a successful handshake, which helps to improve the link stability and, hence, the route stability.  
During the neighbor and route discoveries stage, each node sends control messages that contain mobility metrics (M.1) (i.e., nodes' velocity), node identity (M.5) (i.e., node addresses), and the geographical locations (M.3) (i.e., GPS coordinates) of one-hop neighboring nodes to form a neighbor set comprised of two-hop neighboring nodes as well as to predict the next geographical location of the neighboring nodes based on their velocity. During the route selection stage, each node selects a next-hop node, which is in the idle state after a successful handshake, that provides the shortest route with highest possible packet deliver rate towards the destination node. RL is embedded in each node, and it has three main representations, including the following:  
* state represents the status of the node (i.e., whether ready-to-send, wait-to-send, or clear-to-send in a handshake);
* action represents whether to transmit data packets;
* reward represents positive reward (i.e., when packets arrive at the destination node) or negative reward (i.e., when packets fail to arrive at the destination node).  
RL enables each node to observe its state, to learn, and to take the right action at the right time in order to maximize its reward as time goes by. During the route maintenance stage, which is required when the accumulated reward of the node becomes lower than a predefined threshold as a result of packet loss, the node initiates the route discovery mechanism (or the first stage).  
The routing scheme is compared with a state-of-the-art scheme that utilizes directional antennas and location estimation within the MAC layer, which uses unicasting and geo-casting routing approaches based on location and trajectory information so as to keep track of high-speed flying UAVs of which the topology changes. Random waypoint mobility model (A.2) is used in simulation. The routing scheme has been shown to increase QoS (P.1) (i.e., lower delay), route setup success rate (P.4) (i.e., packet deliver rate), and route lifetime.