Age-Aware Task Scheduling Scheme in Hybrid GEO-LEO Satellite Networks

In this paper, we consider a task scheduling problem for the freshness-critical services in the Internet of Remote Things scenario (IoRT). In the IoRT scenario, a gateway collects status updates from the surrounding devices and then makes a scheduling decision, in which the status updates would be offloaded to a specific satellite for on-orbit processing. Our objective is to propose a task scheduling scheme which can minimize the age of information of the system. To this end, we use the promising hybrid geosynchronous earth orbit and low earth orbit (hybrid GEO-LEO) satellite networks and design an age-aware task scheduling scheme to utilize heterogeneous communication and processing resources. The issue of task scheduling is considered as cooperation between gateway association and resource management problem. To cope with this complicated problem, we formulate it as a Markov Decision Process with minimum peak age and decompose it into two sub-problems, which are resource management with fixed gateway association indexes and scheduling decisions for gateway association. The convex optimization algorithm is utilized to obtain optimal resource management results, and the deep reinforcement learning network is used to achieve the optimal gateway association indexes. Extensive simulation results demonstrate that the peak age of the designed strategy has an advantage over other referred strategies.