Adaptive Predictive Sampling and Communication for Real-time Monitoring

Abstract

We propose an adaptive predictive sampling and communication framework for real-time remote monitoring over packet-erasure channels. Rather than relying on a prescribed dynamical model or continuous sensing, our model-free method uses Taylor-expansion-based prediction to adaptively estimate local signal dynamics and proactively schedule the next sampling times. An analytical compensation factor accounts for packet losses to satisfy a prescribed reconstruction-error level. We develop adaptive sample-wise and episodic policies and evaluate their performance against event-driven and uniform sampling and communication baselines.