Hierarchical modular Bayesian networks for low-power context-aware smartphone

Sung-Bae Cho, Jae Min Yu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Nowadays, smartphone has a tremendous number of applications using sensors and devices for several applications such as healthcare and game. However, serious consideration to increase the duration of battery use of phone is required because of the limited battery capacity. In this paper, we propose a hybrid system to increase the longevity of phone with hierarchical modular Bayesian networks that recognize the user's contexts, and device management rules that infer the unnecessary devices in smartphone. Inferring the user's contexts with sensor data and considering the device status, the context inferred and user's tendency, we determine the superfluous devices that are consuming the battery as dispensable. The experiments with the real log data collected from 28 people for 6 months verify that the proposed system performs the accuracy of 85.68% and the reduction of battery consumption of about 6%.

Original languageEnglish
Pages (from-to)100-109
Number of pages10
JournalNeurocomputing
Volume326-327
DOIs
Publication statusPublished - 2019 Jan 31

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Smartphones
Bayesian networks
Equipment and Supplies
Sensors
Hybrid systems
Experiments
Smartphone
Delivery of Health Care

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

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Hierarchical modular Bayesian networks for low-power context-aware smartphone. / Cho, Sung-Bae; Yu, Jae Min.

In: Neurocomputing, Vol. 326-327, 31.01.2019, p. 100-109.

Research output: Contribution to journalArticle

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