Paper4MobileEnergy.pdf (506.78 kB)
Increasing Energy Efficiency on Smartphones through Data forecashing
conference contribution
posted on 2016-12-09, 15:34 authored by Jiuyun Xu, Dan Yang, Jiazhen Wang, Chao Guan, Stephan Reiff-Marganiec, Huilin ShenSmartphones 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.
Funding
The paper is fully supported by a grant from the Fundamental Research Funds for the Central Universities (Project No. 13CX06009A and No. 14CX06007A). This work is a partial result of Jiuyun’s visit to the University of Leicester supported by China Scholarship Council.
History
Citation
Mobile Services (MS), 2016 IEEE International Conference onAuthor affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer ScienceSource
5th IEEE International Conference on Mobile Services, 2016, San Francisco, USAVersion
- AM (Accepted Manuscript)