An improved energy management model for IoT devices using deep learning

The Internet of Things (IoT) is booming, with research showing that billions of connected devices gather data, communicate, and automate. The widespread adoption of IoT devices has been hampered by one major challenge: limited energy resources that require recharging or replacing often. Energy management strategies on these devices are essential to improve battery life. This paper presents an enhanced version of the energy management model for IoT devices using deep learning techniques in technical abstracts. The aim is to reduce energy consumption by predicting and configuring real-time device settings based on user behavior trends. A deep neural network is used as the model to learn and tailor its response variably based on different user conditions like temperature, light, and usage patterns. The model can analyze this data to predict energy demand and programmatically adjust device settings.