Increasing Energy Efficiency on Smartphones through Data forecashing

Smartphones are widely used in daily life to access services and various functions require continuous communication, which leads to increased energy consumption. However, the development of battery and related energy saving technology can not meet the demand for energy consumption. Much of current research work focuses on energy models caring much about energy consumption of every single application. In this paper, we propose a data forecasting-based strategy for increasing energy efficiency on smartphones based on the predictability of data to be accessed. To achieve this, a combination of Collaborative filtering with the k-means algorithm categorize users with similar user groups and speculate use increased for the data users will access. With this model, we also adopt data pre-storing model and dynamic updating model. The simulation results illustrate that our approach is leading to energy saving.