The paper shows that deriving a structured belief from the prediction operator's needs and using it in non-myopic scheduling yields up to 28% better predictive loss than activity-paced baselines on a physics-calibrated synthetic wildfire environment.
Online learning based efficient resource allocation for LoRaW AN network,
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Belief-Aware Scheduling for Predictive Wildfire Hazard Mapping under Sparse-Window Telemetry
The paper shows that deriving a structured belief from the prediction operator's needs and using it in non-myopic scheduling yields up to 28% better predictive loss than activity-paced baselines on a physics-calibrated synthetic wildfire environment.