Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities \u2013 interactions with the device and the applications used \u2013 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities \u2013 interactions with the device and the applications used \u2013 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 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