QoE-Oriented Task Offloading in SAG Integrated IoT: An Interactive Dual-Agent PPO Approach

Space-air-ground integrated Internet of things (IoT) networks can enable seamless global coverage for IoT devices and effectively alleviate the burden on terrestrial IoT data transmission. However, due to the complexity of network architecture and the wide geographical distribution of IoT nodes, significant discrepancies may occur in the timing of control information reception among certain nodes. New challenges arise accordingly for the unified management of multi-dimensional resources and task offloading in the network. To mitigate this, in this paper, we propose a dynamic two time-scales resource management framework that simultaneously takes task types, requirements, and priorities into account. Moreover, quality of experience (QoE) models are designed for the IoT users generating data-transmission and computation-intensive tasks, and an optimization problem is formulated to maximize the overall QoE. Given the multi-scale coupling constraints and mixed-integer program of the formulated problem, we decouple it into two Markov decision process-based subproblems and develop an interactive dual-agent proximal policy optimization algorithm to address them. Numerical results demonstrate the superiority of our proposed algorithm compared to benchmark algorithms.