Intelligent Energy Transfer and Node Localization in the Internet of Things Using Deep Reinforcement Learning

A novel idea to address energy transfer and node localization in the Internet of Things (IoT) using Deep Reinforcement Learning (DRL) techniques is provided in this paper. Leveraging DRL, we introduce a deep reinforcement approach that optimizes energy transfer and enhances node localization accuracy. The proposed DRL system model implements a data set consisting of thousands of IoT devices energy status and location information which is helpful for finding the IoT device localization based on energy levels. The action decisions for energy transfer and reward mechanisms are implemented in DRL. The results indicate that the suggested framework enhances energy efficiency.