# An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
## 2 Related Work
Lower rate of link breakages improves QoS and network lifetime. compared to DQN and optimal approaches due to its randomness. The network lifetime of the optimal approach remains constant since the optimal route is selected in each iteration. A higher network lifetime provides stability, contributing to a higher throughput and a reduced signaling overhead caused by route formation. DQN improves network lifetime by up to 120% compared to RL, and so it increases network stability. #### 5.7.4 Effects of Node Mobility to Energy Consumption

The energy consumption caused by data transmission from a source UAV to a destination UAV increases gradually as mobility rate increases as shown in Figure 5.9

DQN outperforms RL with at least 10 units lower energy consumption at lower mobility rate (i.e., 10 m/s), and at least 40 units lower energy consumption at higher mobility rate (i.e., 100 m/s). Therefore, DQN increases network lifetime and network stability. This improvement is attributed to the use of DQN at CC to predict the state-action value of a route based on the residual energy and mobility of intermediate UAVs with respect to the proximity of a destination UAV. Subsequently, DQN converges to the most favorable route, which has a lower energy consumption as time goes by, across various network planes (i.e., macro-, pico-, and femto-planes). The energy consumption of the DQN and optimal approaches does not vary considerably for most mobility rates upon convergence to the most favorable route It should be noted that (a) the random approach has a higher energy consumption (i.e., more than 95 units) due to its random nature, and (b) the RL approach has a higher energy consumption (i.e., more than 75 units) when the mobility rate is greater than 80 m/s. #### 5.7.5 Effects of Node Mobility to Rate of Link Breakages

The link breakage between UAVs of a route increases gradually as node mobility increases as shown in Figure 4:0

DQN outperforms RL with at least 60% lower link breakages at lower node mobility (i.e., 10 m/s), and at least 45% lower link breakages at higher node mobility (i.e., 100 m/s). Therefore, DQN increases successful data transmission, contributing to higher network stability and throughput. This improvement is attributed to DNN that learns the mobility pattern of UAVs and provides state-action values that help to choose the most favorable route with a lower mobility rate. Meanwhile, the random approach has at least 40% of the links are broken at lower node mobility (i.e., 10 m/s) and up to 80% of the links are broken

Figure 4:1 Energy consumption increases with respect to mobility rate. DQN achieves lower values as compared to RL and random approaches. Lower energy consumption improves the energy efficiency of a network. at higher node mobility (i.e., 100 m/s). In contrast, the optimal approach has the lowest number of link breakages from 7% to 20% as node mobility increases. It selects a route comprised of nodes with lower mobility and higher residual energy. #### 5.7.6 Effects of Node Mobility to Network Lifetime

The network lifetime of a network increases gradually as the mobility rate increases as shown in Figure 4:2 Note that network lifetime increases with the mobility rate. This is because the number of link breakages increases the need to form new routes. Therefore, energy consumption incurred by data transmission and reception reduces, leading to a longer network lifetime. DQN outperforms RL with at least three times longer network lifetime at lower mobility rate (i.e., 10 m/s), and DQN achieves at least 5000 s longer network lifetime at higher mobility rate (i.e., 100 m/s). This improvement is attributed to DNN that learns about the states of nodes and predicts the best state-action value to choose the most favorable node with a higher residual energy and a lower mobility rate in order to minimize link disconnection and dead nodes. This helps UAVs to perform data transmission over their respective routes for a longer time duration, contributing to higher network lifetime and QoS.