ATLAS: Expert-Guided Sustainable Satellite Backhaul for Off-Grid Wildlife Monitoring

Key points

• Remote wildlife monitoring increasingly depends on high-fidelity cameras and sensors deployed far from grid power and terrestrial networks.1
• Low Earth orbit satellite links can provide connectivity in these settings, but their high power draw and variable bandwidth, combined with intermittent on-site solar harvesting, make continuous backhaul infeasible.1
• ATLAS uses foundation models to compile natural-language expert intent into semantic scoring logic, combines semantic relevance with environmental and energy state, and uses a constrained reinforcement-learning scheduler to decide which clips to upload and when.1
• As a representative case study, we deployed ATLAS for over six months across 18 off-grid salmon-monitoring river sites.1

Sources (1)

• [1]ATLAS: Expert-Guided Sustainable Satellite Backhaul for Off-Grid Wildlife Monitoring
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 11:12 PM

  We present ATLAS, an expert-guided edge-to-cloud system that treats satellite backhaul as a scarce resource for off-grid wildlife monitoring.

  Remote wildlife monitoring increasingly depends on high-fidelity cameras and sensors deployed far from grid power and terrestrial networks.

Extractive summary: sentences quoted from the sources.