Abstract
In this paper, we propose to learn LIfestyles of mobile users via mobile Phone Sensing (LIPS), and we develop a system and algorithms to realize this idea. First, we present the workflow and architecture of our system, LIPS. Combining both unsupervised and supervised learning, we propose a hybrid scheme for lifestyle learning, which consists of two parts: characterization and prediction. Specifically, we present a two-stage algorithm to characterize the lifestyle of a mobile user using Places of Interest (PoIs), which leverages two different algorithms for coarse- grained and fine-grained clustering in two stages respectively. Based on discovered PoIs, we present a method to build a model to predict his/her future activities using a supervised classification algorithm. In addition, we present an adaptive sampling algorithm for improving energy efficiency, which leverages both the discovered PoIs and the lifestyle model for adaptively controlling the sampling rate. We implemented the proposed system and algorithms based on the Android platform. We have validated and evaluated LIPS via extensive field tests carried out for over 1.5 months in 6 cities of USA. The experimental results show that LIPS can 1) well discover PoIs of mobile users, 2) precisely predict their future activities, and 3) achieve significant energy savings (compared to periodic sampling).
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509013289 |
| DOIs | |
| State | Published - Feb 2 2017 |
| Event | 59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States Duration: Dec 4 2016 → Dec 8 2016 |
Other
| Other | 59th IEEE Global Communications Conference, GLOBECOM 2016 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 12/4/16 → 12/8/16 |
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Keywords
- Energy Efficiency
- Human-Centric Sensing
- Mobile Computing
- Mobile Phone Sensing
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Networks and Communications
- Hardware and Architecture
- Safety, Risk, Reliability and Quality
Cite this
LIPS : Lifestyle learning via mobile phone sensing. / Sheng, Xiang; Tang, Jian; Wang, Jing; Li, Teng; Xue, Guoliang; Yang, Dejun.
2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 7841959.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - LIPS
T2 - Lifestyle learning via mobile phone sensing
AU - Sheng, Xiang
AU - Tang, Jian
AU - Wang, Jing
AU - Li, Teng
AU - Xue, Guoliang
AU - Yang, Dejun
PY - 2017/2/2
Y1 - 2017/2/2
N2 - In this paper, we propose to learn LIfestyles of mobile users via mobile Phone Sensing (LIPS), and we develop a system and algorithms to realize this idea. First, we present the workflow and architecture of our system, LIPS. Combining both unsupervised and supervised learning, we propose a hybrid scheme for lifestyle learning, which consists of two parts: characterization and prediction. Specifically, we present a two-stage algorithm to characterize the lifestyle of a mobile user using Places of Interest (PoIs), which leverages two different algorithms for coarse- grained and fine-grained clustering in two stages respectively. Based on discovered PoIs, we present a method to build a model to predict his/her future activities using a supervised classification algorithm. In addition, we present an adaptive sampling algorithm for improving energy efficiency, which leverages both the discovered PoIs and the lifestyle model for adaptively controlling the sampling rate. We implemented the proposed system and algorithms based on the Android platform. We have validated and evaluated LIPS via extensive field tests carried out for over 1.5 months in 6 cities of USA. The experimental results show that LIPS can 1) well discover PoIs of mobile users, 2) precisely predict their future activities, and 3) achieve significant energy savings (compared to periodic sampling).
AB - In this paper, we propose to learn LIfestyles of mobile users via mobile Phone Sensing (LIPS), and we develop a system and algorithms to realize this idea. First, we present the workflow and architecture of our system, LIPS. Combining both unsupervised and supervised learning, we propose a hybrid scheme for lifestyle learning, which consists of two parts: characterization and prediction. Specifically, we present a two-stage algorithm to characterize the lifestyle of a mobile user using Places of Interest (PoIs), which leverages two different algorithms for coarse- grained and fine-grained clustering in two stages respectively. Based on discovered PoIs, we present a method to build a model to predict his/her future activities using a supervised classification algorithm. In addition, we present an adaptive sampling algorithm for improving energy efficiency, which leverages both the discovered PoIs and the lifestyle model for adaptively controlling the sampling rate. We implemented the proposed system and algorithms based on the Android platform. We have validated and evaluated LIPS via extensive field tests carried out for over 1.5 months in 6 cities of USA. The experimental results show that LIPS can 1) well discover PoIs of mobile users, 2) precisely predict their future activities, and 3) achieve significant energy savings (compared to periodic sampling).
KW - Energy Efficiency
KW - Human-Centric Sensing
KW - Mobile Computing
KW - Mobile Phone Sensing
UR - http://www.scopus.com/inward/record.url?scp=85015385843&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015385843&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2016.7841959
DO - 10.1109/GLOCOM.2016.7841959
M3 - Conference contribution
BT - 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -