  Context information brings new opportunities for efficient and effective
applications and services on mobile devices. A wide range of research has
exploited context dependency, i.e., the relations between context(s) and the
outcome, to achieve significant, quantified, performance gains for a variety of
applications. These works often have to deal with the challenges of multiple
sources of context that can lead to a sparse training data set, and the
challenge of energy hungry context sensors. Often, they address these
challenges in an application specific and ad-hoc manner. We liberate mobile
application designers and researchers from these burdens by providing a
methodical approach to these challenges. In particular, we 1) define and
measure the context-dependency of three fundamental types of mobile usage in an
application agnostic yet practical manner, which can provide clear insight into
the performance of potential ap-plication. 2) Address the challenge of data
sparseness when dealing with multiple and different sources of context in a
systematic manner. 3) Present SmartContext to address the energy challenge by
automatically selecting among context sources while ensuring the minimum
accuracy for each estimation event is met.
  Our analysis and findings are based on usage and context traces collected in
real-life settings from 24 iPhone users over a period of one year. We present
findings regarding the context dependency of the three principal types of
mobile usage; visited websites, phone calls, and app usage. Yet, our
methodology and the lessons we learn can be readily extended to other
context-dependent mobile usage and system resources as well. Our findings guide
the development of context aware systems, and highlight the challenges and
expectations regarding the context dependency of mobile usage.
