Increasing Energy Efficiency on Smartphones through Data forecashing
Jiuyun Xu
Dan Yang
Jiazhen Wang
Chao Guan
Stephan Reiff-Marganiec
Huilin Shen
2381/38885
https://figshare.le.ac.uk/articles/conference_contribution/Increasing_Energy_Efficiency_on_Smartphones_through_Data_forecashing/10121276
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.
2016-12-09 15:34:13
energy saving
Data Forecasting
Similarity
k-means Algorithm
Collaborative filtering recommendation
Algorithm
data pre-storing model