Intelligent Task Scheduling in Hybrid GEO-LEO Satellite-Assisted Marine IoT Network
Dongqing Li, Shaohua Wu, Ye Wang, Wen Wu, Qinyu Zhang
Abstract
The objective of this article is to investigate an update scheduling issue in the satellite-based network for time-sensitive marine Internet of Things (marine IoT) applications. In this particular scenario, multiple gateways capture updates from surrounding marine IoT sensors and make online decisions regarding task scheduling for orbital processing by a specific satellite. A hybrid low earth orbit and geosynchronous earth orbit (hybrid GEO-LEO) satellite architecture shows promise in achieving timely update delivery. However, the limited communication and orbital processing resources create significant challenges for ensuring timely task scheduling in the hybrid network. To address this challenge, we model the age-optimal scheduling issue as a collaborative gateway association and resource management problem. We first transform it into two corresponding subproblems: 1) resource management and 2) scheduling decision making. Subsequently, we employ the Lagrange multiplier algorithm to achieve optimal resource allocation results while utilizing deep reinforcement learning techniques to determine the scheduling decisions intelligently. Extensive simulation results demonstrate that our designed intelligent task scheduling scheme with optimal resource management outperforms state-of-the-art schemes in terms of peak-age, thereby highlighting the effectiveness of hybrid GEO-LEO networks for time-sensitive marine IoT applications.