iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery

TL;DR

iSatCR is a distributed graph-based framework that jointly optimizes onboard computing and routing in LEO satellite networks to improve data transmission efficiency amid increasing data volumes.

Contribution

It introduces a novel graph embedding and a distributed deep reinforcement learning algorithm for joint onboard computing and routing optimization in LEO satellites.

Findings

iSatCR outperforms baseline methods under high network load.

The approach effectively reduces data transmission volume.

It adapts to the dynamic and complex nature of LEO satellite networks.

Abstract

Sending massive Earth observation data produced by low Earth orbit (LEO) satellites back to the ground for processing consumes a large amount of on-orbit bandwidth and exacerbates the space-to-ground link bottleneck. Most prior work has concentrated on optimizing the routing of raw data within the constellation, yet cannot cope with the surge in data volume. Recently, advances in onboard computing have made it possible to process data in situ, thus significantly reducing the data volume to be transmitted. In this paper, we present iSatCR, a distributed graph-based approach that jointly optimizes onboard computing and routing to boost transmission efficiency. Within iSatCR, we design a novel graph embedding utilizing shifted feature aggregation and distributed message passing to capture satellite states, and then propose a distributed graph-based deep reinforcement learning algorithm that derives joint computing-routing strategies under constrained on-board storage to handle the complexity and dynamics of LEO networks. Extensive experiments show iSatCR outperforms baselines, particularly under high load.