Toward LEO Satellite Network Systems for Instantaneous Detection of Environmental Changes

Zian Wang; Peng Hu; Grant Gunn

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arXiv · 2605.01243

Toward LEO Satellite Network Systems for Instantaneous Detection of Environmental Changes

Zian Wang·Peng Hu·Grant Gunn

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Abstract

The rapid deployment of Low Earth Orbit (LEO) satellite constellations has enabled the emergence of in-orbit edge computing and data centers, where satellites with onboard processing and high-speed inter-satellite links can collaboratively process data in space. 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.

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Zian Wang, Peng Hu, Grant Gunn. 2026-07-01. Toward LEO Satellite Network Systems for Instantaneous Detection of Environmental Changes. https://arxiv.org/abs/2605.01243

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