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Smartphones and smartphone apps have undergone an explosive growth in thepast decade. However, smartphone battery technology hasn't been able to keeppace with the rapid growth of the capacity and the functionality of smartphonesand apps. As a result, battery has always been a bottleneck of a user's dailyexperience of smartphones. An accurate estimation of the remaining battery lifecould tremendously help the user to schedule their activities and use theirsmartphones more efficiently. Existing studies on battery life prediction havebeen primitive due to the lack of real-world smartphone usage data at scale.This paper presents a novel method that uses the state-of-the-art machinelearning models for battery life prediction, based on comprehensive andreal-time usage traces collected from smartphones. The proposed method is thefirst that identifies and addresses the severe data missing problem in thiscontext, using a principled statistical metric called the concordance index.The method is evaluated using a dataset collected from 51 users for 21 months,which covers comprehensive and fine-grained smartphone usage traces includingsystem status, sensor indicators, system events, and app status. We find thatthe remaining battery life of a smartphone can be accurately predicted based onhow the user uses the device at the real-time, in the current session, and inhistory. The machine learning models successfully identify predictive featuresfor battery life and their applicable scenarios.

Predicting Smartphone Battery Life based on Comprehensive and Real-time Usage Data