Subspace-Based Emulation of the Relationship between Forecasting Error and Network Performance in Joint Forecasting-Scheduling for the Internet of Things

Abstract

We develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology, an Artificial Neural Network (ANN) learns this relationship while the forecasting error is kept inside a subspace of the entire space of forecasting errors during training. We apply our methodology to the case of Joint Forecasting-Scheduling (JFS) for the Internet of Things (IoT). Our results hold potential to improve the performance of JFS in next-generation networks and can be applied to a much wider range of problems beyond IoT.