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 language | English |
|---|---|
| Pages (from-to) | 100-109 |
| Number of pages | 10 |
| Journal | Neurocomputing |
| Volume | 326-327 |
| DOIs | |
| Publication status | Published - 2019 Jan 31 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
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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 journal › Article
TY - JOUR
T1 - Hierarchical modular Bayesian networks for low-power context-aware smartphone
AU - Cho, Sung-Bae
AU - Yu, Jae Min
PY - 2019/1/31
Y1 - 2019/1/31
N2 - 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%.
AB - 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%.
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UR - http://www.scopus.com/inward/citedby.url?scp=85030756493&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2017.01.124
DO - 10.1016/j.neucom.2017.01.124
M3 - Article
VL - 326-327
SP - 100
EP - 109
JO - Neurocomputing
T2 - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -