## 5 Simulation
### Impact of Drones and Network Parameters on the LDFM Scheduling Scheme  
The first part of the simulations evaluates the impact of drones on the LDFM scheduling scheme.  
(1) Speed of drones  
Specifically, we analyzed the effect of the drones by varying their speeds from 20 to 50 m/s under three different traffic loads, with the vehicle carrying twenty drones departing from the base station. The results of these simulations were measured and are depicted in Figure 7.  
Figure 7: Cont.  
Figure 7a shows that the number of charging requests increases as the speeds of the drones increase, since more nodes can be charged at the same time, as demonstrated in Figure 7b. Under light traffic load, the number of charging requests from the sensors is less; the vehicle and the drones can fully meet the charging requests under different drone speeds, so the curve is kept stable, as well as number of nodes being charged within the given time. However, under medium and heavy traffic loads, the number of charging requests increases as the speeds of the drones increase, as well as the number of nodes being charged within the given time. Because the network is busier, there is greater a number of charging requests, and with the increasing speeds of the drones, more charging requests can be fulfilled. This means that the faster the speed of the drone, the timelier the charging of the matched nodes. As a result, the flight times between the matched pair nodes are shorter.  
(2) Impact of number of drones mounted on vehicle  
To investigate the effect of the number of drones mounted on vehicle, we conducted simulations with drone numbers ranging from 5 to 30 under medium traffic load. The speed of the drones was fixed at 35 m/s, while the threshold was set at 0.40 (meaning that the residual energy of the nodes is below \(0.40\bullet e_{max\_sn}\)). The resulting performance metrics are shown in Figure 8. Figure 8a shows that the number of charging request packets peak when the number of mounted drones reaches 20. Additionally, Figure 8b indicates that the number of nodes being charged within a given time increases with the number of drones, since more drones are available to share the charging tasks.  
Moreover, under light traffic load, the number of charging requests from the sensors is less, the curve remains a near curve, and so do number of nodes being charged within a given time. This is because the network is not busy; thus, one vehicle and five drones can meet all the charging requests from the nodes. However, under medium and heavy traffic loads, more charging requests occur with the increasing number of drones because more drones can meet the charging requests, which keeps more nodes active in the WRSN, and more nodes can be charged within a given time. This means that adopting more drones is useful for improving the performance of the network. However, when the number of drones is more than 15, the performance of the network does not improve significantly because the number of matched pairs is limited to one charging circle.  
Figure 7: Comparison of the proposed LDFM scheduling scheme with different speeds of drones. (**a**) Number of charging request packets; (**b**) number of nodes being charged in time.