Computation Offloading in Space–Air–Ground Integrated Networks for Diverse Task Requirements with Integrated Reliability Mechanisms

The sixth-generation (6G) system has been attracting increasing attention from both industry and academia, with the space–air–ground integrated network (SAGIN) identified as one of its key applications. This study investigates a SAGIN framework tailored for deployment in remote areas. To address the differing needs of users with emergency and routine tasks, an offloading strategy is proposed that enables direct offloading for emergency tasks and optimized UAV-assisted offloading for routine tasks. Additionally, considering the limited satellite coverage duration, a reliability mechanism for task offloading is designed. The study formulates a task offloading optimization problem aimed at maximizing the completion rate of routine tasks—while reducing their energy consumption and latency—under the premise of guaranteeing the completion of emergency task offloading. The problem is modeled as a Markov Decision Process (MDP). To solve it, a D-MAPPO reinforcement learning algorithm is proposed, which integrates the Dirichlet distribution with the Multi-Agent Proximal Policy Optimization (MAPPO) framework. Simulation results show that, compared with the MAPPO and PPO algorithms, the delay is reduced by 38% and 31%, respectively, while the energy consumption is reduced by 7% and 48%, respectively.