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

Chen Peng¹, Qiqi Wang², Desheng Zhang³

¹Department of Journalism and Communication, Wuhan Sports University, Wuhan, China. 2021420002@whsu.edu.cn.

Scientific Reports

|April 8, 2025

Summary

This study introduces an efficient dynamic resource allocation (EDRA) algorithm for Unmanned Aerial Vehicle (UAV)-assisted edge computing during large-scale sports events. The EDRA algorithm significantly reduces energy consumption while enhancing system performance.

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Area of Science:

Computer Science
Electrical Engineering
Telecommunications

Background:

Increasing demand for high-quality viewing experiences at large-scale sports events strains traditional networks.
Internet of Things (IoT) technology exacerbates computational demands due to temporal and spatial event concentration.
Unmanned Aerial Vehicles (UAVs) offer a flexible solution for edge computing deployment.

Purpose of the Study:

To address resource allocation challenges in UAV-assisted edge computing for large-scale sports events.
To minimize overall system energy consumption while maintaining performance.
To develop an efficient dynamic resource allocation (EDRA) algorithm.

Main Methods:

Formulated the problem as a long-term stochastic optimization.
Proposed the efficient dynamic resource allocation (EDRA) algorithm.
Decomposed the problem into parallelizable sub-problems using stochastic optimization.
Solved sub-problems via convex optimization and linear programming.

Main Results:

The EDRA algorithm demonstrated a 32.4% reduction in energy consumption compared to existing advanced algorithms.
The proposed algorithm ensures superior system performance.
Theoretical analysis confirmed the solution's proximity to the optimal.

Conclusions:

UAV-assisted edge computing is a viable solution for high-demand events.
The EDRA algorithm effectively optimizes resource allocation for energy efficiency and performance.
This approach offers a scalable and efficient method for managing computational loads in dynamic environments.