Machine and Deep Learning Impact on Energy Consumption Enhancement in IoT Systems

In the recent years, the Internet of Things (IoT) has grown incredibly and become a benchmark in many systems and frameworks. In addition, energy consumption optimization and management have become globally significant and inevitable issues to be addressed, particularly in developed countries. Recently, researchers have consistently sought to identify valuable approaches and solutions related to the integration of Machine Learning and Deep Learning into IoT-driven systems to enhance energy efficiency and sustainability. This study aims to show how important Machine Learning (ML) and Deep Learning (DL) are in systems that use the Internet of Things (IoT) for energy efficiency. It concentrates on the newest advancement in this field, exploring the contributions of 20 indexed references from Scopus and Web of Science databases over the last half-decade. It is concluded that supervised learning techniques, particularly ANN and DNN algorithms integrated with the LSTM method, are the most compatible with IoT-based systems for better energy efficiency.