## 6 Results
### Overload Relief  
Figure 7 shows the behavior of the congested regions on the Manhattan map at 7 a.m. without the support of UAV-BSs. We note that several regions suffer from high user demand. With the support of UAV-BSs, the efficiency of each strategy can be seen in Figures 8 and 9, and thus, the effective reduction of overhead in the regions. Figure 10 also shows the Manhattan region suffering from high user demand, with an intensity higher than at 7 a.m. This intensity can be explained by the fact that it is late afternoon (6 p.m.) on a Friday in one of the largest shopping centers in the United States. Figures 11 and 12 show the regions with lower overhead due to the support of UAV-BSs with the two strategies discussed in this paper. Figure 8: Heat map of resources used for the naïve strategy at 7:00 a.m.  
Figure 10: Heat map for resource utilization at 6:00 p.m.  
Figure 9: Heat map of resources used for the optimized strategy at 7:00 a.m.  
Two schedules were selected for the case study to show that daily congestion has different distributions with variations depending on temporal and spatial flows. These flows depend on the demand in the region and are found in large shopping centers that operate during business hours or in regions with high traffic that can overload the mobile network at certain times, called peak hours, as shown in Figure 1. The number of UAV-BSs to support the macrocells was determined by two strategies (naive and optimized) to evaluate the relationship between the number of available resources (UAV-BS) and the QoS to work out a possible schedule. The idea is to evaluate the network before and after the support performed by UAV-BS for each macrocell using both strategies. For the naive strategy, fixed values (5) of UAV BSs were set for each congested macrocell. Figure 13a shows the results of the naive strategy at 7 a.m. compared to the scenario without UAV-BS. It is evident that the overhead was reduced in overloaded macrocells. For macrocells whose capacity was above the 80% threshold that defines them as congested, the overhead was minimized after support from UAV-BSs. This is because the UAV-BSs received a portion of the users assigned only in the macrocell, offsetting the load on the mobile network. The UAV-BSs used in the naive strategy are shown in Figure 13c, which shows the number of UAV-BSs and the percentage of resources used. We see that only a small fraction of the UAV-BSs had a capacity greater than 60%, showing that both the macrocells and the UAV-BSs remained in balance with their resources. Figure 11: Heat map of resources used for the naïve strategy at 6:00 p.m.  
Figure 12: Heat map of resources used for the optimized strategy at 6:00 p.m.  
To present optimized planning and maintain QoS, we defined a strategy based on the number of users in the coverage area of the macrocell that is congested, i.e., the number of UAV-BSs needed to support depends on the number of users connected around the macrocell. Figure 13b shows the results for the optimized strategy compared to the scenario without UAV-BSs. We see that the optimized strategy, similar to the naive strategy, shows promising results and significantly reduces the overhead of macrocells whose resource capacity is more than 80% utilized. Figure 13d shows the number of UAV-BSs used by the optimized strategy, and it is obvious that the number of UAV-BSs is lower than that of the naive strategy. This shows that we can achieve similar or better results with fewer UAV-BSs, because the fewer the UAV-BSs, the lower the deployment cost, or the better the scaling between UAV-BSs in the following period. As shown in Figure 13b, the network resources of all macrocells were less than 60%, so the QoS for users was maintained even though the resources provided by the UAV-BSs were an overhead that reduced the QoS for users, and fewer UAV-BSs helped to reduce the overhead. Although the network resources provided by the UAV-BSs in the optimized strategy had lower QoS, the number of blocked users decreased, as shown below.