Abstract
Various applications using sensors and devices on smartphone are being developed. However, since limited battery capacity does not allow to utilize the phone all the time, studies to increase use-time of phone are very active. In this paper, we propose a hybrid system to increase the longevity of phone. User's context is recognized through hierarchical modular Bayesian networks, and unnecessary devices are inferred through device management rules. Inferring the user's context using sensor data, and considering device status, context inferred and user's tendency, we determine the device which is consuming the battery most. In the experiments with the real log data collected from 28 people for six months, we evaluated the proposed system resulting in the accuracy of 85.68 % and the improvement of battery consumption of about 6 %.
| Original language | English |
|---|---|
| Title of host publication | Hybrid Artificial Intelligent Systems - 10th International Conference, HAIS 2015, Proceedings |
| Editors | Héctor Quintián, Emilio Corchado, Enrique Onieva, Igor Santos, Eneko Osaba |
| Publisher | Springer Verlag |
| Pages | 543-554 |
| Number of pages | 12 |
| ISBN (Electronic) | 9783319196435 |
| DOIs | |
| Publication status | Published - 2015 Jan 1 |
| Event | 10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015 - Bilbao, Spain Duration: 2015 Jun 22 → 2015 Jun 24 |
Publication series
| Name | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
|---|---|
| Volume | 9121 |
| ISSN (Print) | 0302-9743 |
Other
| Other | 10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015 |
|---|---|
| Country | Spain |
| City | Bilbao |
| Period | 15/6/22 → 15/6/24 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
A low-power context-aware system for smartphone using hierarchical modular bayesian networks. / Yu, Jae Min; Cho, Sung Bae.
Hybrid Artificial Intelligent Systems - 10th International Conference, HAIS 2015, Proceedings. ed. / Héctor Quintián; Emilio Corchado; Enrique Onieva; Igor Santos; Eneko Osaba. Springer Verlag, 2015. p. 543-554 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 9121).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A low-power context-aware system for smartphone using hierarchical modular bayesian networks
AU - Yu, Jae Min
AU - Cho, Sung Bae
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Various applications using sensors and devices on smartphone are being developed. However, since limited battery capacity does not allow to utilize the phone all the time, studies to increase use-time of phone are very active. In this paper, we propose a hybrid system to increase the longevity of phone. User's context is recognized through hierarchical modular Bayesian networks, and unnecessary devices are inferred through device management rules. Inferring the user's context using sensor data, and considering device status, context inferred and user's tendency, we determine the device which is consuming the battery most. In the experiments with the real log data collected from 28 people for six months, we evaluated the proposed system resulting in the accuracy of 85.68 % and the improvement of battery consumption of about 6 %.
AB - Various applications using sensors and devices on smartphone are being developed. However, since limited battery capacity does not allow to utilize the phone all the time, studies to increase use-time of phone are very active. In this paper, we propose a hybrid system to increase the longevity of phone. User's context is recognized through hierarchical modular Bayesian networks, and unnecessary devices are inferred through device management rules. Inferring the user's context using sensor data, and considering device status, context inferred and user's tendency, we determine the device which is consuming the battery most. In the experiments with the real log data collected from 28 people for six months, we evaluated the proposed system resulting in the accuracy of 85.68 % and the improvement of battery consumption of about 6 %.
UR - http://www.scopus.com/inward/record.url?scp=84932120677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84932120677&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19644-2_45
DO - 10.1007/978-3-319-19644-2_45
M3 - Conference contribution
AN - SCOPUS:84932120677
T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
SP - 543
EP - 554
BT - Hybrid Artificial Intelligent Systems - 10th International Conference, HAIS 2015, Proceedings
A2 - Quintián, Héctor
A2 - Corchado, Emilio
A2 - Onieva, Enrique
A2 - Santos, Igor
A2 - Osaba, Eneko
PB - Springer Verlag
ER -