A low-power context-aware system for smartphone using hierarchical modular bayesian networks

Jae Min Yu; Sung Bae Cho

doi:10.1007/978-3-319-19644-2_45

A low-power context-aware system for smartphone using hierarchical modular bayesian networks

Jae Min Yu, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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	https://doi.org/10.1007/978-3-319-19644-2_45
Publication status	Published - 2015
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/Territory	Spain
City	Bilbao
Period	15/6/22 → 15/6/24

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-19644-2_45

Cite this

Yu, J. M., & Cho, S. B. (2015). A low-power context-aware system for smartphone using hierarchical modular bayesian networks. In H. Quintián, E. Corchado, E. Onieva, I. Santos, & E. Osaba (Eds.), Hybrid Artificial Intelligent Systems - 10th International Conference, HAIS 2015, Proceedings (pp. 543-554). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 9121). Springer Verlag. https://doi.org/10.1007/978-3-319-19644-2_45

Yu, Jae Min ; Cho, Sung Bae. / A low-power context-aware system for smartphone using hierarchical modular bayesian networks. Hybrid Artificial Intelligent Systems - 10th International Conference, HAIS 2015, Proceedings. editor / Héctor Quintián ; Emilio Corchado ; Enrique Onieva ; Igor Santos ; Eneko Osaba. Springer Verlag, 2015. pp. 543-554 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).

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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 %.",

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Yu, JM & Cho, SB 2015, A low-power context-aware system for smartphone using hierarchical modular bayesian networks. in H Quintián, E Corchado, E Onieva, I Santos & E Osaba (eds), Hybrid Artificial Intelligent Systems - 10th International Conference, HAIS 2015, Proceedings. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 9121, Springer Verlag, pp. 543-554, 10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015, Bilbao, Spain, 15/6/22. https://doi.org/10.1007/978-3-319-19644-2_45

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

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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 %.

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Yu JM, Cho SB. A low-power context-aware system for smartphone using hierarchical modular bayesian networks. In Quintián H, Corchado E, Onieva E, Santos I, Osaba E, editors, Hybrid Artificial Intelligent Systems - 10th International Conference, HAIS 2015, Proceedings. Springer Verlag. 2015. p. 543-554. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)). doi: 10.1007/978-3-319-19644-2_45

A low-power context-aware system for smartphone using hierarchical modular bayesian networks

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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