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 %.
|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|
|Number of pages||12|
|Publication status||Published - 2015|
|Event||10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015 - Bilbao, Spain|
Duration: 2015 Jun 22 → 2015 Jun 24
|Name||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|Other||10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015|
|Period||15/6/22 → 15/6/24|
Bibliographical notePublisher Copyright:
© Springer International Publishing Switzerland 2015.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)