  Smartphones and smartphone apps have undergone an explosive growth in the
past decade. However, smartphone battery technology hasn't been able to keep
pace with the rapid growth of the capacity and the functionality of smartphones
and apps. As a result, battery has always been a bottleneck of a user's daily
experience of smartphones. An accurate estimation of the remaining battery life
could tremendously help the user to schedule their activities and use their
smartphones more efficiently. Existing studies on battery life prediction have
been 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 machine
learning models for battery life prediction, based on comprehensive and
real-time usage traces collected from smartphones. The proposed method is the
first that identifies and addresses the severe data missing problem in this
context, 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 including
system status, sensor indicators, system events, and app status. We find that
the remaining battery life of a smartphone can be accurately predicted based on
how the user uses the device at the real-time, in the current session, and in
history. The machine learning models successfully identify predictive features
for battery life and their applicable scenarios.
