Diversity in smartphone usage

TLDR

The study aims to characterize intentional smartphone activities and their impact on network and energy usage, and to demonstrate how adapting to individual user behavior can improve future energy consumption predictions. Using detailed usage traces from 255 users, the authors conduct a comprehensive analysis of intentional interactions and application use, and develop an adaptive model to forecast energy drain. The analysis reveals vast, order‑of‑magnitude diversity in user interactions and data consumption, yet identifies consistent patterns such as exponential app‑popularity distributions, and shows that an adaptive prediction model halves the 90th‑percentile error compared to average‑behavior baselines.

Abstract

Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.