# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 3 Model of a Drone-Assisted Mobile Edge Computing System
### System Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Parameter** & **Value** & **Description** \\ \hline Drone dynamics & Number of drones (USD) & 10 & Typical drone swarm size in a simulated urban environment \\  & Coverage area & 500 m \(\times\) 500 m & Reference city-level MEC \\  & Flight speed & 5-15 m/s & Speed range based on commercial UAVs (e.g., DJI Matrice 300) \\ \hline  & Flight path & Randomized waypoint model with path updated every 10 s & Simulates the dynamic maneuvering characteristics of the UAV \\ Resource and communication constraints & Number of edge servers (USD) & 3 & Reflects multi-region edge computing deployment scenarios \\  & ECS computing power & 8 GHz/unit & Refers to actual disappointing devices (e.g., NVIDIA Jetson TX2) \\  & Drone computing power & 2 GHz/frame & Refers to commercial UAV embedded computing module \\  & ECS–UAV communication bandwidth & 20 MHz & Refers to downlink bandwidth for 5G MEC systems \\  & Drone–user communication bandwidth & 5 MHz & Typical configuration for reference drone to terminal communication \\  & Communication latency (ECS–drone) & 10 ms & Approximate low-latency MEC network characteristics \\  & Communication delay (UAV–user) & 5 ms & Reflects the real-time communication requirements between UAV and terminal equipment \\  & & & Generated by Poisson distribution, in line with the requirements of intelligent transportation \\ Task characteristics & Task arrival rate & 0.5 times/s/user & Including video analysis, image processing and other computation-intensive tasks \\ Task computation requirements & 500–1500 Megacycles & & Typical range of data uploaded from IoT terminals. \\  & Task data size & 10–50 MB & Refers to cloud and edge computing resource market pricing \\ Resource pricing & Initial transaction price & 0.1 USD/GHz & Refers to cloud and edge computing resource market pricing \\ and allocation & for ECS resources & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System model parameters. Figure 1: System model. The server, as the leader of the system, first sets the price of each task based on the current task demand and resource maintenance costs. The server's goal is to maximize its utility function, i.e., to optimize its revenue through a rational pricing strategy. After the server sets the prices, the drones, as followers, decide how to respond to those prices. Each drone sets its service price for each task. The goal of the drones is to maximize their utility function. The users, as consumers, have their demands affected by the prices of the drones' services. In the proposed system, since the drones are supposed to provide computing services to mobile users using resources obtained from the ECS, we assume that there are enough resources available in the ECS for resource allocation. In the communication model, we assume that the uplink and downlink channels between the ECS and the UAV are symmetric. The available transmission capacity of the allocated resources calculated by [PERSON]'s theorem is considered sufficient. Each UAV is responsible for collecting service requests from users within its coverage area. Then, based on the amount of service demands collected from the mobile users, the UAVs make a decision to request edge computing resources from the ECS. In order to obtain the edge computing resources from the ECS, the drones have to pay a certain amount of revenue to the ECS, which can make a profit by allocating the edge computing resources to the drones by setting a reasonable transaction price for the allocated edge computing resources. In the above process, the transaction price of the edge computing resources is an important factor in determining the resource trading behavior, which is controlled by the ECS.