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Increasing Energy Efficiency on Smartphones through Data forecashing

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conference contribution
posted on 09.12.2016, 15:34 by Jiuyun Xu, Dan Yang, Jiazhen Wang, Chao Guan, Stephan Reiff-Marganiec, Huilin Shen
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.

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 on

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Source

5th IEEE International Conference on Mobile Services, 2016, San Francisco, USA

Version

AM (Accepted Manuscript)

Published in

Mobile Services (MS)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Acceptance date

20/05/2016

Available date

09/03/2017

Publisher version

http://ieeexplore.ieee.org/abstract/document/7787067/

Temporal coverage: start date

27/06/2016

Temporal coverage: end date

02/07/2016

Language

en