Toward LEO Satellite Network Systems for Instantaneous Detection of Environmental Changes
This work addresses the feasibility of using orbital edge computing for near-instantaneous environmental monitoring, a critical application for disaster response.
The paper investigates whether LEO satellite networks with onboard computing and inter-satellite links can achieve sub-minute information freshness for real-time wildfire detection, finding that the best configurations achieve an average Age of Information below 70 seconds and peak under 100 seconds.
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.