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Authors: Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra

Abstract

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Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, Ranveer Chandra. Github Repository (Software, Github Repository). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@misc{dagstuhl-artifact-25662,
   title = {{Github Repository }}, 
   author = {Chabra, Om and Li, Chenning and Hsieh, Kevin and Segarra, Santiago and Arzani, Behnaz and Olsen, Peder and Chandra, Ranveer},
   note = {Software (visited on 2026-03-19)},
   url = {https://github.com/microsoft/OrbitalBrain},
   doi = {10.4230/artifacts.25662},
}

Document

DOI: 10.4230/OASIcs.NINeS.2026.5

OrbitalBrain: A Distributed Framework for Training ML Models in Space

Authors: Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract

Earth observation nanosatellites capture high-resolution photos of the Earth in near real-time. These images increasingly support ML applications that are critical for safety and response, such as forest fire and flood detection. However, the downlink bandwidth is limited, resulting in days or weeks of delay from image capture to training. In this work, we propose OrbitalBrain, an efficient in-space distributed ML training framework that leverages limited and predictable satellite compute, bandwidth, and power to intelligently balance data transfer, model aggregation, and local training. Our evaluations demonstrate that OrbitalBrain achieves 1.52×-12.4× speedup in time-to-accuracy while always reaching a higher final model accuracy compared to state-of-the-art ground-based or federated learning baselines. Furthermore, our approach is complementary to satellite imagery capturing and downloading, enhancing the overall efficiency of satellite-based applications.

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Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra. OrbitalBrain: A Distributed Framework for Training ML Models in Space. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 5:1-5:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chabra_et_al:OASIcs.NINeS.2026.5,
  author =	{Chabra, Om and Li, Chenning and Hsieh, Kevin and Segarra, Santiago and Arzani, Behnaz and Olsen, Peder and Chandra, Ranveer},
  title =	{{OrbitalBrain: A Distributed Framework for Training ML Models in Space}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{5:1--5:32},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.5},
  URN =		{urn:nbn:de:0030-drops-255907},
  doi =		{10.4230/OASIcs.NINeS.2026.5},
  annote =	{Keywords: Satellite networks, Distributed machine learning, Federated learning, Earth observation, In-orbit computing}
}

Document

DOI: 10.4230/OASIcs.NINeS.2026.10

Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery

Authors: Ziqian Liu, Om Chabra, James Lynch, Aaron Martin, Chenning Li, and Hari Balakrishnan

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract

This paper presents CityMesh, a city-scale decentralized mesh network designed for disaster recovery and emergency scenarios. When wide-area Internet connectivity is unavailable or severely degraded, CityMesh leverages both static access points and mobile devices equipped with Wi-Fi to provide intra-city connectivity and reach opportunistic gateways to the Internet (e.g., via satellite links). The main contribution of this paper is a scalable routing protocol that supports millions of devices, addressing a long-standing limitation of wireless mesh and mobile ad hoc networks. Unlike prior approaches, CityMesh exploits rich building-location and building-geometry data from widely available city maps to guide route computation, improving packet delivery while significantly reducing transmission overhead. Simulation results from 70 cities show that CityMesh improves packet delivery rates by 88% over WEAVE (a state-of-the-art geographic routing protocol). A campus-scale deployment of 300 Wi-Fi devices across 31 buildings shows the practical deployability of CityMesh. These results demonstrate the promise of map-aware routing as a foundation for scalable, resilient city-wide Wi-Fi networks.

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Ziqian Liu, Om Chabra, James Lynch, Aaron Martin, Chenning Li, and Hari Balakrishnan. Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 10:1-10:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{liu_et_al:OASIcs.NINeS.2026.10,
  author =	{Liu, Ziqian and Chabra, Om and Lynch, James and Martin, Aaron and Li, Chenning and Balakrishnan, Hari},
  title =	{{Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{10:1--10:31},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.10},
  URN =		{urn:nbn:de:0030-drops-255954},
  doi =		{10.4230/OASIcs.NINeS.2026.10},
  annote =	{Keywords: mesh networking, disaster recovery, geographic routing, scalability, Wi-Fi}
}

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Documents authored by Li, Chenning

Github Repository

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OrbitalBrain: A Distributed Framework for Training ML Models in Space

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Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery

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