# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 6 Simulation Results  
In this section, we evaluate the performance of the proposed algorithm through simulation experiments. The Python environment was utilized to build the system framework. The main simulation parameters are shown in Table 3[15; 20; 24; 25; 35]. The communication coverage range of the UAV was set to 30 m [20]. SPs were then distributed within the UAV coverage area by using different distribution methods based on different disaster scenarios. For the disaster scenarios, we conducted experiments for both mild disaster and severe disaster scenarios. In the case of mild disaster, one UAV was set to cover two-three DRCs, the arrival rate of the SPs ranged from 0.02 to 0.09 people/s, and the speed of the distribution of survival supplies ranged from 0.05 to 0.1 people/s. The baseline approaches for comparison are given as follows. Stochastic Task Scheduling Algorithm (STS): The algorithm randomly matches tasks with SPs to execute tasks. Emergency Task Priority Scheduling Algorithm (ETP): The algorithm prioritizes the assignment of tasks with high urgency to the nearest SPs for execution, in which tasks with smaller maximum tolerated delay have higher urgency. Original bat algorithm (OB): The algorithm is the unimproved original bat algorithm. Figure 2 shows the final optimization results of different algorithms. The comparison in the figure shows that our algorithm is far superior to STS. In addition, compared with the ETP algorithm that uses a relatively monotonic optimization indicator, our algorithm also achieves better performance. Finally, our indicators are also higher than the traditional OB algorithm, which shows the effectiveness of our algorithm innovation and further improves the performance of the algorithm. In summary, it reflects the effectiveness of our algorithm and its good optimization ability on multi-objective problems. \begin{table}
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Further, we validated the robustness of the algorithm through two sets of control-variable experiments. Firstly, we changed the number of tasks while keeping the number of SPs constant, setting the number of SPs to 70 and the number of tasks gradually increasing from 35 to 60. Figure 3 demonstrates the optimization of our algorithm for the two objectives in mild disaster and the comparison with other algorithms. It can be seen that the overall overhead of the UAV system inevitably increases with the increase in system load, but the utility of users also increases relatively. Our algorithm can always optimize multiple objectives and achieve the best optimization results. We also conducted experiments to control the number of SPs. We set the number of tasks to 40 and increased the number of SPs from 50 to 80. In Figure 4, the performance of our algorithm in mild disaster scenarios is compared with that of other algorithms. It can be seen that with abundant computational resources, the computational power of our system is substantially improved and still outperforms other algorithms. However, as the computational resources continue to increase, the optimization effect is gradually saturated. Figure 3: Variation in system performance with number of tasks in mild disaster scenario. (**a**) Variation in total UAV overhead with number of tasks. (**b**) Variation in total utility of SPs with number of tasks. Figure 2: Performance comparison between different baseline approaches and MOMIB. (**a**) System overhead of UAVs. (**b**) Execution utility of SPs. As shown in Figure 5, we conducted experiments on the performance of the algorithm as iterative convergence in two environments. It can be seen that at the beginning of the iteration, the two objects obtained by the initial allocation of the system have not yet achieved the best optimization effect. As the algorithm iterates, the two objectives are continuously optimized. By continuously optimizing the task allocation scheme, the overhead of UAVs is reduced and the utility of SPs is gradually increased. And convergence is achieved in fewer cycles.