# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 4 Analysis of Resource Allocation Experiments
### DSO Algorithm Comparison Experiment  
In the next section, we verify the superiority of the DSO algorithm by designing comparative experiments. The experiments are conducted through three aspects: user requirements, convergence speed, and rate of change in the utility function.  
We compare the DSO algorithm with other algorithms by designing experiments, as shown in Figure 6, we compare the DSO algorithm with three algorithms, namely DRP, EAA, and SSP, where the algorithm, DRP, dynamically adjusts the price of resources according to the change in resource supply and demand in order to optimize the allocation. It is usually used in cloud computing or distributed computing environments. In the EAA algorithm, resources are evenly distributed to all participants without considering specific demand or utility functions. In the SSO algorithm, resources are allocated directly between servers and UAVs without considering user demand or secondary optimization. As can be seen in Figure 6, the DSO algorithm reaches its maximum value at 27 iterations and levels off later. The final experimental result is the DSO \(\geq\) SSO \(\geq\) DRP \(\geq\) EAA.  
As shown in Figure 7, the DSO algorithm shows significant advantages in convergence speed, which not only can quickly reach a stable solution, but also always outperforms other compared algorithms in the whole iteration process. Specifically, the DSO algorithm completes convergence in about 30 iterations, which is much faster than the number of iterations required by other algorithms, reflecting the high efficiency of the algorithm in complex resource allocation scenarios. In addition, from the convergence trend after each iteration, it can be observed that the DSO algorithm shows faster utility improvement in the initial stage and rapidly approaches the optimal solution with less computational cost, which is suitable for UAV-assisted MEC environments with high requirements for real-time  
Figure 6: Comparison of algorithms.  
and computational efficiency. This improvement in convergence speed is mainly due to the synergistic optimization of the Stackelberg game and auction mechanism in the DSO algorithm, which accelerate the resource allocation process between the edge server and the UAV and the UAV and the user, respectively, and thus effectively reduce the number of iterations and improve the overall efficiency.  
Next, we will further analyze the average change in the utility function over time to verify the performance advantages of the DSO algorithm in terms of resource utilization and system efficiency. The comparison will comprehensively reflect the utility growth rate and final utility value of the DSO algorithm in different time scales, and further evaluate its adaptability and stability in real UAV-assisted MEC environments.  
As shown in Figure 8, in terms of the average change in the utility function over time, the utility values of all the algorithms show a gradual upward trend with the increase in the number of iterations, indicating that all the algorithms are able to enhance the system benefits in the process of continuously optimizing the resource allocation. However, the DSO algorithm consistently maintains a higher average utility value throughout the iteration process, and its utility enhancement speed and final utility level are significantly better than those of the other compared algorithms. This advantage suggests that the DSO algorithm is able to allocate resources among edge servers, UAVs, and users more efficiently, ensuring that the benefits of all parties are maximized while improving the overall system benefits.  
Figure 8: Average change in utility function over time.  
Figure 7: Convergence speed comparison.