Energy Management Based On Charging Behavior Prediction

As mobile applications transition into providing richer content and supporting more use-cases, it has become increasingly apparent that smart-phones are constrained by battery life. Industry has put great effort into energy-aware hardware platforms, but little attention has been given to designing energy efficiency around user charging behavior. If smart-phones were to become capable of accurately predicting the time and duration of charging events of specific users, task scheduling could be more intelligently catered towards the user. Our project examines whether machine learning can be reliably implemented on smart-phones to infer aspects of charging behavior, as well as the appropriate classification schemes required to accurately predict future charging events.