Gradient-based adaptive modeling for IoT data transmission reduction

Spatial and temporal correlation between sensor observations in an Internet of Things environment can be exploited to eliminate unnecessary transmissions. Transmitting less data certainly contributes to meet the growing need for energy-saving and robust transmissions, thus prolong the lifespan of the entire WSN. Spatiotemporal correlation-based dual prediction (DP) and data compression (DC) schemes aim to reduce the amount of data transmission while ensuring data accuracy. In practice, however, the existing methods restrict the stability of the system when the model hyper-parameters are uncertain. Thus adaptive model has lately attracted extensive attention for the development of resource-constrained WSN. In this paper, we propose a gradient-based adaptive model that implements both schemes in a two-tier data reduction framework. To the best of our knowledge, the proposed scheme is the first attempt to introduce adaptiveness into both the DP and DC schemes by using a simple gradient optimization method. Gradient-based Optimal Step-size LMS (GO-LMS) is introduced to make the DP aspects adaptive, while a Gradient-based Adaptive PCA (GA-PCA) approach is used for the DC aspects. The Barzilai–Borwein method is incorporated into the gradient optimization to enable adaptive computation of the step-size for each iteration. Through extensive simulations, the developed framework was found to outperform other state-of-the-art schemes in terms of both the transmission reduction ratio and data recovery accuracy.