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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. |
