2026-09-22T22:36:13Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129256 2025-10-20 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms The student, Bill Tao, accepted the attached license on 2025-04-25 at 12:45. The student, Bill Tao, submitted this Dissertation for approval on 2025-04-25 at 12:50. This Dissertation was approved for publication on 2025-04-28 at 16:29. DSpace SAF Submission Ingestion Package generated from Vireo submission #21984 on 2025-10-19 at 18:11:02 Near-real-time insight delivery pipeline for earth observation satellite images Tao, Bill 2025-04-28 Vasisht, Deepak Vasisht, Deepak Gupta, Indranil Nahrstedt, Klara Kumar, Swarun Satellite Networking Remote Sensing eng The emergence of the new generation of small yet computation-capable low-earth-orbit (LEO) satellites for earth observation (EO) has created opportunities for humanity to monitor events that happen in every corner of the globe and even use the emerging Computer Vision (CV) models to generate insights from these satellite images. However, the current pipeline suffers from multiple challenges, including the limitation on both computing and energy capacity of the satellite hardware, and the limited network capacity in the whole pipeline. This dissertation mitigates these challenges by delivering a cross-layer end-to-end optimization solution. It focuses on answering this key question: what can be done in EO satellite systems to help it satisfy the real-time requirements from users as well as the tight energy, compute and networking budgets? To answer this question, this dissertation built 2 major systems: Umbra system for optimally using satellite link capacity to avoid congestion, Serval system for distributed edge computing on EO satellite systems to reduce latency as well as reduce computation costs. This combination of systems greatly enhances the quality of the satellite image & insight delivery system, while greatly boosting the efficiency within the whole pipeline end to end. It showcases the great benefit of a detailed understanding of the underlying physical dynamics as well as unique patterns when designing novel systems in unseen environments. By modeling the orbit dynamics of satellites, Umbra and Serval were able to predict the future activities of satellites and proactively schedule compute and network transmission. As a result, Umbra can reduce the P90 latency for all network traffic by 3.5x, while Serval can reduce the median latency for the critical data from 3 days to 2 minutes. 2025-05 Thesis https://hdl.handle.net/2142/129256 Copyright 2025 Bill Tao Siebel School Comp & Data Sci Computer Science University of Illinois Urbana-Champaign Ph.D. Dissertation