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

2026-03-19

AI Summary

This work addresses the growing bandwidth bottleneck in satellite-to-ground links caused by the surge in low Earth orbit (LEO) satellite remote sensing data, a challenge that traditional routing optimization struggles to manage. To this end, the paper proposes iSatCR, a graph-based distributed cooperative optimization framework that jointly schedules on-board computing and data routing. iSatCR innovatively integrates graph embedding with distributed deep reinforcement learning, employing offset-aware feature aggregation and message-passing mechanisms to dynamically model satellite states and enable coordinated compute-and-route decisions. Experimental results demonstrate that iSatCR significantly outperforms existing baselines under high-load conditions, effectively reducing the volume of data requiring downlink transmission while enhancing overall transmission efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• LEO satellite networks
• onboard computing
• data delivery
• routing
• bandwidth bottleneck

Innovation

• onboard computing
• graph-based reinforcement learning
• LEO satellite networks
• joint computing-routing
• distributed message passing