Performance Evaluation of Satellite-Based Data Offloading on Starlink Constellations

2025-01-24

AI Summary

To address the edge-computing bottleneck on autonomous vehicles and limited ground-edge network coverage, this paper proposes a Starlink-enabled space–ground collaborative low-earth-orbit (LEO) edge computing offloading framework. We construct, for the first time, a spatiotemporal dynamic topology model based on real Starlink orbital and constellation parameters; integrate LEO link simulation, sensor frame-rate–computation-load coupling modeling, and end–edge–cloud cooperative scheduling; and design a dynamic offloading decision mechanism tailored for vehicular clusters. Our key contribution is overcoming conventional coverage constraints to enable wide-area, real-time offloading. Experimental results demonstrate that, under onboard compute capacity ≥8 TOPS and traffic density ≤50 vehicles/km, end-to-end latency remains consistently below 100 ms—validating Starlink’s feasibility and effectiveness in supporting real-time autonomous driving data processing.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Vehicular Edge Computing (VEC) is a key research area in autonomous driving. As Intelligent Transportation Systems (ITSs) continue to expand, ground vehicles (GVs) face the challenge of handling huge amounts of sensor data to drive safely. Specifically, due to energy and capacity limitations, GVs will need to offload resource-hungry tasks to external (cloud) computing units for faster processing. In 6th generation (6G) wireless systems, the research community is exploring the concept of Non-Terrestrial Networks (NTNs), where satellites can serve as space edge computing nodes to aggregate, store, and process data from GVs. In this paper we propose new data offloading strategies between a cluster of GVs and satellites in the Low Earth Orbits (LEOs), to optimize the trade-off between coverage and end-to-end delay. For the accuracy of the simulations, we consider real data and orbits from the Starlink constellation, one of the most representative and popular examples of commercial satellite deployments for communication. Our results demonstrate that Starlink satellites can support real-time offloading under certain conditions that depend on the onboard computational capacity of the satellites, the frame rate of the sensors, and the number of GVs.

Problem

• Starlink Satellite Network
• Vehicle Edge Computing (VEC)
• Autonomous Vehicle Sensor Data

Innovation

• Satellite-Vehicle Task Allocation
• Vehicle Edge Computing (VEC)
• Non-Terrestrial Networks (NTNs)