Toward LEO Satellite Network Systems for Instantaneous Detection of Environmental Changes

TL;DR

This paper explores the potential of LEO satellite networks with onboard computing for real-time wildfire detection, demonstrating that certain configurations can achieve near-instantaneous environmental monitoring within 70 seconds.

Contribution

It develops a simulation framework to evaluate how LEO satellite constellation design impacts data freshness for environmental monitoring applications.

Findings

Best configurations achieve average AoI below 70 seconds

Orbital design significantly affects information freshness

Simulation results support feasibility of near-instantaneous detection

Abstract

The rapid deployment of Low Earth Orbit (LEO) satellite constellations has enabled the emergence of in-orbit edge computing and data centers-interconnected satellites equipped with onboard computing capabilities and high-speed inter-satellite links (ISLs). This paper investigates whether such architectures, integrated with a deep learning-based computer vision pipeline, can achieve sub-minute information freshness suitable for real-time wildfire detection. To evaluate this hypothesis, we develop a simulation framework that models orbital dynamics, distributed processing, and network routing, using Age of Information (AoI) as the primary performance metric. A total of 720 simulation trials are conducted across 12 real-world constellation configurations, including Starlink, Kuiper, Telesat, and OneWeb. The results demonstrate that constellation design has a significant impact on AoI performance, with average AoI values ranging from 66.5 s to over 6300 s. The best-performing configurations achieve an average AoI below 70 s and a peak AoI under 100 s, indicating that orbital edge computing systems can provide the level of timeliness required for near-instantaneous environmental monitoring.