Performance Evaluation of Satellite-Based Data Offloading on Starlink Constellations

TL;DR

This paper evaluates how Starlink satellites can be used for real-time data offloading in vehicular edge computing, considering factors like satellite capacity, sensor frame rate, and vehicle density.

Contribution

It introduces new data offloading strategies for GVs and satellites in LEOs, using real Starlink data to optimize coverage and delay in VEC systems.

Findings

Starlink satellites can support real-time offloading under specific conditions.

Offloading performance depends on satellite computational capacity and sensor frame rate.

Number of GVs influences the effectiveness of satellite-based data offloading.

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.