Edge Computing Task Offloading Based on Game Theory for Space-Air-Ground Integrated Network
Yuanyuan Gao, Jiayan Liu, Suiyan Geng, Xiongwen Zhao, Zhiyu Chen, Hongxi Zhou · 2024
This study investigates an efficient computational resource offloading mechanism based on game theory for space-air-ground integrated network (SAGIN). In the system for the LEO satellite, Unmanned Aerial Vehicles (UAVs) and users, parameters of time delay and energy consumption are considered to improve overall system performance. Moreover, based on the equilibrium of multilayer game theory, the resource allocation and pricing algorithm are taken into account to maximize system profit. Furthermore, based game algorithm (BGA) is proposed and compared with other benchmark methods such as random task offloading (RTO) and all user's tasks offloading (AUTO) to UAV in the system for performance analysis. Results show that the proposed BGA exhibits good convergence and outperforms, and thus provide useful information for design of SAGIN.