Multimodal Remote Inference
The work addresses how to schedule data delivery from multiple remote sensors to minimize errors in a machine learning model performing real-time inference. It models this as a scheduling problem based on the Age of Information (AoI) vector. The research develops optimal policies, including an index-based threshold policy for two modalities and an error-aware switching policy for more modalities, showing how to balance data freshness against limited network resources.