IoT Data Analytics Hybrid AutoML Framework with Energy Awareness in Dynamically and Adaptively Changing Environments

Abstract

The high rate of Internet of Things (IoT) deployments has contributed to high volumes of heterogeneous data that are generated under drastic conditions of energy and computing constraints. The paper is a fully energy-saving IoT data analytics framework viewed through the prism of Automated Machine Learning (AutoML). The described comparison between classical machine learning models (ML), deep learning models (DL), and hybrid models (using an ensemble) evaluates the models using metrics where the scores are multi-dimensional, i.e., they consist of such measures as accuracy, power consumption and energy efficiency, inference latency, CPU usage, and memory consumption. An experimental evaluation of a practical smart city dataset of IoT demonstrates that there is a pronounced trade-off between the predictive performance and power expenditure. Deep learning models are also power-intensive despite their accuracy being high. To surpass that, a hybrid ML-DL mixture composed of CNN, SVM, and Random Forest ones is proposed in the assistance of energy-related weighted averaging. The power consumption of the hybrid model is 28.18 W, and its accuracy is 0.897, which offers the trade-off of using the hybrid model in the resource-constrained and dynamic Internet of Things.