Hybrid LSTM‐PCA‐Powered Renewable Energy‐Based Battery Life Prediction and Management for IoT Applications

Abstract

To ensure self-sustainable and long-term requirement of Internet of Things (IoT) operation, energy harvesting (EH) is a promising technique. In this work, battery prediction problems and joint access control are studied in a IoT cell system with a base station (BS) and EH user equipment (UE) with limited uplink access channels. Every UE present holds a limited capacity rechargeable battery. In order to address this issue, we take into consideration the uplink wireless system with “N” EH user equipment (UE) along with the base station. To build the UE uplink access control, the first step involves the application of hybrid Q network (HQN) with long short-term memory (LSTM) to tackle the access control issue. This technique maximizes the uplink transmission sum rate. The LSTM DQN network designed has two layers with battery prediction and joint access control solution. The first layer is developed to predict battery levels, while the next layer generates the access control information based on channel information along with the predicted values. This network is trained to decrease the discounted prediction loss and increase long-term discounted sum rate, at the same time by training the two layers jointly. In recent years, IoT sensors have played a major role in collecting and monitoring data gathered from real-world applications such as environmental monitoring, transportation, urban security, smart energy management, agriculture, and health care. Several of these appliances function with batteries. Hence, the primary task would be to predict the batter’s lifespan and provide a timeframe to replace or recharge the battery. The battery life used in an IoT network is predicted with the help of a hybrid random forest-principal component analysis (PCA) regression algorithm with machine learning.