MOSAL: A Subspace-Based Forecasting Algorithm for Throughput Maximization in IoT Networks

Abstract

Predictive solution techniques have been developed recently to solve the massive access problem of the Internet of Things (IoT). These techniques forecast the traffic generation patterns of individual IoT devices in the coverage area of an IoT gateway and schedule the Medium Access Control (MAC)-layer resources at the gateway in advance based on these forecasts. Although predictive solutions have achieved high network performance a key problem is that their performance depends highly on the performance of forecasters. In this article to minimize the effects of forecasting errors on the performance of predictive networks we develop a subspace-based forecasting algorithm called Motion On a Subspace under Adaptive Learning rate (MOSAL). First our algorithm trains a forecaster by minimizing the performance loss of an IoT network based on the emulation of an Application-Specific Error Function (ASEF) by an Artificial Neural Network (ANN). Second the algorithm moves close to a subspace of the forecasting errors while aiming to maximize network throughput. Our results show that MOSAL achieves a throughput performance that surpasses the performance of commonly used standard gradient descent training algorithms at a reasonable execution time. These results open the way to the deployment of predictive solutions at IoT gateways in practice in the near future.