自适应预测采样与通信用于实时监控
Adaptive Predictive Sampling and Communication for Real-time Monitoring
AI总结：
针对丢包信道上的实时远程监控，提出一种基于泰勒展开预测的无模型自适应采样与通信框架，通过补偿因子保证重建误差，并优于事件驱动和均匀采样基线。
AI中文摘要：
我们提出了一种自适应预测采样与通信框架，用于在丢包信道上进行实时远程监控。与依赖预设动力学模型或连续感知不同，我们的无模型方法利用基于泰勒展开的预测来自适应估计局部信号动态，并主动调度下一次采样时间。一个解析补偿因子考虑了数据包丢失，以满足规定的重建误差水平。我们开发了自适应逐样本和逐回合策略，并将其性能与事件驱动和均匀采样及通信基线进行了评估。
英文摘要：
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.
