Energy Harvesting and Machine Learning Techniques for Internet of Things Applications: A Review

The powerful platform that connects both the physical and digital realms is called the Internet of Things, or IoT. The standard of life has increased due to the current developments in Internet of Things (IoT) technologies, which have also enhanced human-technology engagement. The low expenses for positioning, remote availability, and intelligent operation of IoT nodes and sensors are the most important characteristics that propel the IoT's broad acceptance and rapid expansion in the ecological, health, and commercial domains. To solve the matter of energy shortages, energy harvesters, or EHs, are necessary for self-sustaining Internet of Things nodes. In this paper, an extensive analysis of machine learning (ML) techniques used in conjunction with EH-powered Internet of Things (IoT) devices to estimate and manage energy availability. The authors examined the machine learning techniques based on its applications, advantages and limitations on the basis of recent literature.