# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 4 Analysis of Resource Allocation Experiments
### Convergence Analysis  
First, as shown in Figure 3, in the system with the participation of only one drone, in the Stackelberg game phase, the demand for the drone decreases with the number of iterations, and converges at a demand of 7 after almost 60 iterations. While in the auction algorithm phase, the demand of the UAV first rises and then tends to equilibrate and converges at the seventh iteration. It can be concluded from the results of this experiment that both phases of the game lead to an equilibrium solution. Next, in order to better verify the convergence of the two-stage algorithm, we increase the number of UAVs and analyze the two stages in separate experiments. We analyze the process of the Stackelberg game separately, as shown in Figure 4, and we increase the number of drones--there are three drones involved in resource allocation. As the game progresses, each drone's demand decreases and then converges to the equilibrium as the number of iterations increases, so the drones all have the highest demand at the beginning and then gradually decrease to a stable value, and almost all of the drones iterate more than 70 times before they start to converge. Because the drones initially received many resource requests from users, the demand for drones was relatively high, but as many users were satisfied, the demand for drones gradually decreased and eventually converged to equilibrium. By analyzing the convergence of the Stackelberg game above, we can observe that the first stage can already converge, next, Let's analyze separately the change in UAV demand with the number of iterations during the auction algorithm. As shown in Figure 5, due to the introduction of the auction mechanism, at the beginning, each user has introduced resource requests and bid against each other, which makes the demand for UAVs significantly increase, and the demand for UAVs gradually tends to balance as most users obtain resources. In the aforementioned experiments, we have verified the convergence of the DSO two-stage optimization algorithm through three sets of independent tests, proving that the algorithm is able to converge stably to the optimal solution within a limited number of iterations, ensuring the effectiveness of resource allocation and the reliability of computation. To further assess the advantages and applicability of the DSO algorithm in practical applications, the next work will systematically analyze the performance of  
Figure 3: Convergence diagram of the two-stage algorithm. the DSO algorithm in comparison with other mainstream resource allocation algorithms by focusing on three key dimensions, namely user demand, convergence speed, and the rate of change in the utility function. This multi-dimensional evaluation will not only reveal the performance and limitations of the DSO algorithm in different scenarios, but also verify its stability and efficiency in dynamic, multi-user environments, providing a more comprehensive theoretical basis and practical support for resource management in UAV-assisted MEC systems. First, in terms of user demand, we will analyze the performance of the DSO algorithm when facing different numbers, complexities, and dynamically changing user tasks, and evaluate its adaptability and scalability in high demand density and complex environments. Secondly, convergence speed is a core indicator of algorithm efficiency, which affects the real-time response and computational cost of the system. We will verify whether the DSO algorithm can reach a stable solution at a faster speed and improve the efficiency of resource allocation by comparing the number of iterations and convergence time. In addition, the utility function change rate reflects the performance improvement speed  
Figure 4: Convergence diagram of drone demand in the Stackelberg game. Figure 5: Convergence diagram of drones under auction algorithm. and final utility value of the algorithm during the iteration process, which directly affects the system revenue and user satisfaction.