An empirical review of Machine Learning models for Energy Optimizations in IoT Networks

The Internet of Things (IoT) has transformed how users interact with their devices and enabled the accumulation of enormous quantities of data from various sources. There has been an increase in energy consumption due to the expanding use of IoT devices, resulting in environmental concerns and higher operational costs. The use of Machine Learning (ML) algorithms to optimize energy consumption in IoT networks is widespread for different scenarios. This paper empirically analyzes the most widely used machine learning models for energy optimization in IoT networks. The researchers evaluate the efficacy of various ML models regarding energy efficiency, precision, and scalability. The review includes a summary of the most recent research in this field and a comparison of different algorithms, including Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and Deep Learning methods. In addition, this paper discusses the difficulties and limitations of ML-based energy optimization in IoT networks, including data acquisition and processing, model selection, and scalability levels. Finally, this paper presents recommendations for future research and prospective machine-learning applications in energy optimization for IoT networks. This review emphasizes the potential of machine learning (ML) models to reduce energy consumption in IoT networks. It provides researchers and practitioners with insight into this field under real-time scenarios.