Deep Neural Networks Based Approach for Battery Life Prediction  

作　　者：Sweta Bhattacharya Praveen Kumar Reddy Maddikunta Iyapparaja Meenakshisundaram Thippa Reddy Gadekallu Sparsh Sharma Mohammed Alkahtani Mustufa Haider Abidi 

机构地区：[1]School of Information Technology and Engineering,Vellore Institute of Technology,Vellore,India [2]Department of Computer Science and Engineering,Baba Ghulam Shah Badshah University,Rajouri,India [3]Industrial Engineering Department,College of Engineering,King Saud University,Riyadh,Saudi Arabia [4]Raytheon Chair for Systems Engineering,Advanced Manufacturing Institute,King Saud University,Riyadh,Saudi Arabia

出　　处：《Computers, Materials & Continua》2021年第11期2599-2615,共17页计算机、材料和连续体（英文）

基　　金：The authors are grateful to the Raytheon Chair for Systems Engineering for funding.

摘　　要：The Internet of Things(IoT)and related applications have witnessed enormous growth since its inception.The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain.Although the applicability of these applications are predominant,battery life remains to be a major challenge for IoT devices,wherein unreliability and shortened life would make an IoT application completely useless.In this work,an optimized deep neural networks based model is used to predict the battery life of the IoT systems.The present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed methodology.The dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical format.This processed data is normalized using the Standard Scaler technique.Moth Flame Optimization(MFO)Algorithm is then implemented for selecting the optimal features in the dataset.These optimal features are finally fed into the DNN model and the results generated are evaluated against the stateof-the-art models,which justify the superiority of the proposed MFO-DNN model.

关 键 词：Battery life prediction moth flame optimization one-hot encoding standard scaler 

分 类 号：TN9[电子电信—信息与通信工程]