Personalized Network Activity Aware Approach to Reducing Radio Energy Consumption of Smartphone

The radio energy consumption takes a large portion of the total energy consumption in smartphones. However, a significant portion of radio energy is wasted in a special waiting interval, known as the tail time after a transmission is completed while waiting for a subsequent transmission. In order to reduce the wasted energy in the tail time, the fast dormancy feature allows a quick release of a radio connection in the tail time. For supporting the fast dormancy efficiently, it is important to accurately predict whether a subsequent transmission will occur in the tail time. In this paper, we show that there are strong personal characteristics on how user interacts with a radio network within the tail time. Based on these observations, we propose a novel personalized network activity-aware predictive dormancy technique, called Personalized Diapause (pD). By automatically identifying user-specific tail-time transmission characteristics for various network activities, our proposed technique takes advantages of personalized high-level network usage patterns in deciding when to release radio connections. Our experimental results using real network usage logs from 25 users show that pD can reduce the amount of the wasted tail time energy by 51 percent on average, thus saving the total radio energy consumption by 23 percent with less than 10 percent reconnection increase.