# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 2 Materials and Methods  
### Related Work on UAV-MEC  
UAV-assisted mobile edge computing (UAV-MEC), an emerging technology that combines unmanned aerial UAVs and mobile edge MEC, has already demonstrated great potential in areas such as disaster relief, environmental monitoring, and traffic management. This technology can provide low-latency, high-performance computing services in a wide range of application scenarios by pushing computing and storage resources to the edge of the network, combined with the flexibility and mobility of drones. With the development of 5G and the Internet of Things (IoT) technology, the application prospects of UAV-MEC have been gradually recognized and have become a significant research topic. A large amount of research has been conducted on UAV-MEC in terms of path planning, resource allocation, network optimization, and security at home and abroad. In order to optimize the UAV offloading strategy and resource allocation efficiency, [PERSON] et al. [10] conducted a study on the UAV-MEC network. They proposed a user grouping method based on the k-means algorithm to group users into different groups, and designed a new user satisfaction model by jointly considering the task processing delay and energy efficiency. Based on this model, the researchers further proposed an optimization problem to maximize the overall user satisfaction, aiming to jointly optimize the task offloading decision and the UAV scheduling strategy, thereby improving the overall performance of the system. [PERSON] et al. [11] focused on the energy efficiency of the UAV-MEC system under the RIS-assisted non-orthogonal multiple access protocol, and proposed a dual-loop iterative algorithm based on the Dinkelbach method and the block coordinate descent technique to optimize the bit allocation, transmission power, phase shift, and UAV trajectory. This method effectively improves the energy efficiency of the system, reduces the energy consumption, and optimizes the system performance. In terms of research supporting applications in remote areas, [PERSON] et al. [12] proposed a UAV-enabled MEC service solution designed to serve IoT devices randomly distributed on the ground. This solution takes full advantage of the aerial mobility of UAVs to provide low-latency computing and communication services to devices in remote areas, thus solving the problem of scarce computing resources in remote areas. [PERSON] et al. [13] investigated task offloading schemes in asynchronous MEC systems, especially considering the heterogeneity of task generation moments among UAVs, and proposed a solution framework based on deep reinforcement learning. This framework can handle mixed discrete and continuous action spaces, and effectively improve the efficiency of computational offloading and system performance. Through deep reinforcement learning, the system can adaptively adjust the task offloading strategy to optimize the allocation of computational resources. In addition, [PERSON] et al. [14] studied a novel multi-user multi-hotspot MEC network supported by UAVs. In this network architecture, UAVs help to offload computational tasks from end-users in multiple hotspots, which greatly improves the computational performance and efficiency of the system. Through the flexible scheduling of UAVs, the resource allocation in a multi-point environment can be adjusted in real time, improving the overall processing capability of the network. [PERSON] et al. [15] analyzed the task transmission and computation problems in a multi-antenna UAV-assisted MEC network, and proposed a scheme that takes into account the dual functions of UAVs. The UAV not only processes tasks as an antenna MEC node, but also serves as an antenna relay node, which improves the reliability and safety of task transmission. This solution effectively solves the task transmission bottleneck in the UAV network and ensures the robustness and security of data transmission. [PERSON] et al.