Efficient dynamic task offloading and resource allocation in UAV-assisted MEC for large sport event

With the rapid development of Internet of Things (IoT) technology, the people's demand for the viewing experience of large-scale sport events is increasing. However, due to the significant concentration of time and space in large-scale sports events, which leads to a surge in computation-intensive tasks, making traditional network models difficult to cope with such high demands. Fortunately, with the advantages of flexible deployment of Unmanned Aerial Vehicles (UAVs), UAV-assisted edge computing technology provides an innovative solution. This paper studies the resource allocation problem in UAV-assisted edge computing system for large-scale sport events. Our goal is to minimize system energy consumption while satisfying system performance. We formulate the problem as a long-term stochastic optimization problem. To address this issue, we propose the efficient dynamic resource allocation (EDRA) algorithm. By employing stochastic optimization techniques, the original problem is decomposed into multiple sub-problems that can be solved in parallel. We solve each subproblem through convex optimization and linear programming. Through theoretical analysis, we prove the gap between the proposed solution and the optimal solution. Experiments shows that the EDRA algorithm can reduce energy consumption by 32.4% compared to advanced algorithms while ensuring stronger system performance.