Satellite edge computing (SEC) has emerged as a promising paradigm to enhance in-orbit data processing capabilities and reduce transmission latency. However, satellite image processing tasks in SEC environments face critical challenges in efficient data handling, resource coordination, and transmission scheduling. The...
This study tests the hypothesis that orbiting data centers equipped with a deep-learning computer vision pipeline can achieve sub-minute information freshness suitable for real-time wildfire detection, and confirms that orbital edge computing can deliver the such level of timeliness for near-instantaneous environmental...
CoOrbit is presented, a collaborative EO system that conserves onboard storage by retaining only changed and non-overlapping regions and achieves over 108.6× reduction in storage cost and 41.6× reduction in communication size compared to existing EO pipelines.
Mega-constellations of Low Earth Orbit (LEO) satellites are enabling a new Space Cloud paradigm in which edge servers hosted on-board process tasks autonomously, reducing ground-segment latency for globally dispersed users. Realizing this vision requires Collaborative Edge Intelligence (CEI): distributed algorithms tha...
This work proposes a "Summarize First, Download Later" paradigm that exploits recent advances in onboard edge computing and Vision-Language Models (VLMs) and substantially reduces bandwidth consumption while accelerating time-to-insight for time-sensitive missions.
STARS-GS is proposed, a truthful and feasibility-aware scheduling mechanism that combines bid-aware admission control, best-fit ground-station assignment, Earliest Deadline First (EDF)-based bandwidth scheduling, and critical-payment pricing, and scales smoothly to workloads containing up to 900 tasks.
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