Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2026-01-30 10:30'''
|time='''2026-03-20 10:30'''
|addr=4th Research Building A518
|addr=4th Research Building A518
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
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{{Latest_seminar
{{Latest_seminar
|abstract = The recent proliferation of spacecraft in Earth's orbits has ushered in the rise of large-scale satellite constellations. However, this unprecedented growth of constellations has introduced a previously unforeseen challenge: ground track congestion. Specifically, the increasing density of orbital slots forces satellites to share similar orbit planes, causing their nadir-point projections on Earth's surface (i.e., ground tracks) to overlap or remain in close proximity within short time intervals. Such orbit-endowed ground track congestion can degrade constellation performance in remote sensing operations, specified by limited constellation coverage, redundant satellite count, and delayed data delivery. To address this issue, we propose SpaceSched, a hierarchical scheduling framework designed to resolve ground track congestion in satellite constellations. SpaceSched consists of two key components: an on-ground constellation scheduling pipeline, comprising a coverage distributor and a satellite selector, and an in-space downlink scheduling pipeline, featuring a queue regulator. The coverage distributor assigns attitude profiles over time to satellites, ensuring non-overlapping imagery capture regions. The satellite selector optimizes the constellation by strategically selecting a subset of satellites while maintaining coverage efficiency. During in-space downlink scheduling, the queue regulator manages the downlink traffic queue to minimize the delay of high-value data transmission. We evaluate SpaceSched on two operational modes (i.e., stripmap and spotlight) across three well-established satellite constellation systems: SKYSAT, LEMUR, and FLOCK, with 17, 50, and 126 evaluated satellites, respectively. Experimental results demonstrate that SpaceSched improves coverage by up to 1.84×, reduces satellite counts by up to 2.38×, and decreases downlink queue load by up to 36.46×, compared to the plain satellite constellation systems. Furthermore, our case study highlights SpaceSched's potential to meet diverse task demands.
|abstract = Satellite-based quantum networks are emerging as a promising solution for the development of a global quantum Internet in the near future. The ability to leverage the advantageous lower attenuation of optical signals from satellites to ground presents an exciting opportunity to establish a robust and secure quantum communication infrastructure on a global scale. By utilizing a constellation of satellites, it becomes feasible to continuously distribute high-fidelity quantum entanglements among ground stations over long distances, overcoming the limitations of traditional terrestrial-based quantum communication systems. In this article, we first provide a brief survey of existing solutions for satellite-based entanglement distribution, highlighting the various approaches and technologies that have been employed in this rapidly evolving field. We then delve into a formulated optimal entanglement distribution problem, aiming to optimize the distribution of quantum entanglement resources across the satellite network to maximize efficiency and reliability. This problem is addressed through a detailed exploration of several different methodologies and algorithms, each tailored to specific operational settings and constraints. Our experimental results confirm the efficiency of these approaches and provide valuable insights into their practical implementation and performance. Finally, we identify several key directions for further study and development in the realm of satellite-based quantum networks.
|confname =Mobicom'25
|confname =IEEE Network'25
|link = https://dl.acm.org/doi/10.1145/3680207.3765249
|link = https://ieeexplore.ieee.org/document/10526298
|title= SpaceSched: A Constellation-Wide Scheduling System for Resolving Ground Track Congestion in Remote Sensing
|title= Optimal Entanglement Distribution Problem in Satellite-Based Quantum Networks
|speaker=Yifei
|speaker=Yaliang
|date=2026-3-13
|date=2026-3-20
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract =Offering high-quality immersive content is the ultimate goal of volumetric video streaming. Although point clouds and meshes are dominant volumetric representations, their limitations in depicting photo-realistic content often undermine user experience. The recent advent of neural radiance fields (NeRF) offers a promising alternative content representation with superior photo-realism. However, streaming NeRF-based volumetric videos over wireless networks to mobile headsets faces significant challenges, including substantial bandwidth usage because of the large frame size, degraded visual quality due to even a low packet loss rate, and content artifacts caused by performance optimizations (e.g., remote rendering at the network edge). To address these challenges, in this paper, we introduce NeVo, a next-generation volumetric video streaming system for efficient delivery of neural content such as NeRF. NeVo incorporates the following innovations into a holistic system: (1) a novel method to model visibility of implicitly encoded neural content, thereby avoiding non-essential transmission to drastically reduce network data usage, (2) a lightweight, learning-based model for real-time content reconstruction after packet loss with carefully chosen data, and (3) judicious identification and selective delivery of intermediate data in edge-based NeRF rendering to effectively mitigate artifacts. Our extensive experiments indicate that compared with the state-of-the-art, NeVo saves up to 68.3% of bandwidth usage, maintains high visual quality despite packet loss, and enhances user experience by reducing artifacts.
|abstract =Rapid advances in low Earth orbit (LEO) satellite technology and satellite edge computing (SEC) have facilitated a key role for LEO satellites in enhanced Earth observation missions (EOM). These missions (e.g., remote object detection) typically require multi-satellite cooperative observations of a large region of interest (RoI) area, as well as the observation image routing and computation processing, enabling accurate and real-time responsiveness. However, optimizing the resources of LEO satellite networks is nontrivial in the presence of its dynamic and heterogeneous properties. To this end, we propose SECO, a SEC-enabled framework that jointly optimizes multi-satellite observation scheduling, routing and computation node selection for enhanced EOM. Specifically, in the observation phase, we leverage the orbital motion and the rotatable onboard cameras of satellites, and propose a distributed game-based scheduling strategy to minimize the overall size of captured images while ensuring full (observation) coverage. In the sequent routing and computation phase, we first adopt image splitting technology to achieve parallel transmission and computation. Then, we propose an efficient iterative algorithm to jointly optimize image splitting, routing and computation node selection for each captured image. On this basis, we propose a theoretically guaranteed systemwide greedy-based strategy to reduce the total time cost (i.e., transmission, computation and queuing delay) over simultaneous processing for multiple images. Extensive experiments based on real-world datasets demonstrate that SECO can achieve up to a 60.7% reduction in overall time cost compared to baselines.
|confname =Mobicom'25
|confname =INFOCOM'24
|link = https://dl.acm.org/doi/10.1145/3680207.3723473
|link = https://ieeexplore.ieee.org/document/10621270
|title= NeVo: Advancing Volumetric Video Streaming with Neural Content Representation
|title= SECO: Multi-Satellite Edge Computing Enabled Wide-Area and Real-Time Earth Observation Missions
|speaker=Mengfan
|speaker=LinQi
|date=2026-3-13
|date=2026-3-20
}}
}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 01:27, 20 March 2026

Time: 2026-03-20 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [IEEE Network'25] Optimal Entanglement Distribution Problem in Satellite-Based Quantum Networks, Yaliang
    Abstract: Satellite-based quantum networks are emerging as a promising solution for the development of a global quantum Internet in the near future. The ability to leverage the advantageous lower attenuation of optical signals from satellites to ground presents an exciting opportunity to establish a robust and secure quantum communication infrastructure on a global scale. By utilizing a constellation of satellites, it becomes feasible to continuously distribute high-fidelity quantum entanglements among ground stations over long distances, overcoming the limitations of traditional terrestrial-based quantum communication systems. In this article, we first provide a brief survey of existing solutions for satellite-based entanglement distribution, highlighting the various approaches and technologies that have been employed in this rapidly evolving field. We then delve into a formulated optimal entanglement distribution problem, aiming to optimize the distribution of quantum entanglement resources across the satellite network to maximize efficiency and reliability. This problem is addressed through a detailed exploration of several different methodologies and algorithms, each tailored to specific operational settings and constraints. Our experimental results confirm the efficiency of these approaches and provide valuable insights into their practical implementation and performance. Finally, we identify several key directions for further study and development in the realm of satellite-based quantum networks.
  2. [INFOCOM'24] SECO: Multi-Satellite Edge Computing Enabled Wide-Area and Real-Time Earth Observation Missions, LinQi
    Abstract: Rapid advances in low Earth orbit (LEO) satellite technology and satellite edge computing (SEC) have facilitated a key role for LEO satellites in enhanced Earth observation missions (EOM). These missions (e.g., remote object detection) typically require multi-satellite cooperative observations of a large region of interest (RoI) area, as well as the observation image routing and computation processing, enabling accurate and real-time responsiveness. However, optimizing the resources of LEO satellite networks is nontrivial in the presence of its dynamic and heterogeneous properties. To this end, we propose SECO, a SEC-enabled framework that jointly optimizes multi-satellite observation scheduling, routing and computation node selection for enhanced EOM. Specifically, in the observation phase, we leverage the orbital motion and the rotatable onboard cameras of satellites, and propose a distributed game-based scheduling strategy to minimize the overall size of captured images while ensuring full (observation) coverage. In the sequent routing and computation phase, we first adopt image splitting technology to achieve parallel transmission and computation. Then, we propose an efficient iterative algorithm to jointly optimize image splitting, routing and computation node selection for each captured image. On this basis, we propose a theoretically guaranteed systemwide greedy-based strategy to reduce the total time cost (i.e., transmission, computation and queuing delay) over simultaneous processing for multiple images. Extensive experiments based on real-world datasets demonstrate that SECO can achieve up to a 60.7% reduction in overall time cost compared to baselines.

History

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2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

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