Locator: a self-adaptive framework for the recognition of relevant places
Pages 9 - 12
Abstract
A high number of algorithms for the recognition of users' relevant places exist. However, none of them provide an optimal solution across all users and scenarios. We present a preliminary design of Locator -- a self-adaptive framework for recognizing users' relevant places. Locator learns for different contextual situations, combinations of algorithms and location sensor data that achieve the best performance in recognizing relevant places. We conducted a 5-weeks study and collected sensor and ground-truth data from 6 users. Our preliminary results indicate the shortcomings of relying on one algorithm and sensor for recognizing places and thus motivates the rational behind our approach.
References
[1]
D. Kim et al. Discovering Semantically Meaningful Places from Pervasive RF-Beacons. In UbiComp (2009).
[2]
J. Hightower et al. Learning and Recognizing the Places We Go. In UbiComp (2005).
[3]
J. Scott et al. PreHeat: Controlling Home Heating Using Occupancy Prediction. In UbiComp (2011).
[4]
N. Aharony et al. Social fMRI: Investigating and Shaping Social Mechanisms in the Real World. Pervasive and Mobile Computing (2011).
[5]
P. Baumann et al. The Influence of Temporal and Spatial Features on the Performance of Next-place Prediction Algorithms. In UbiComp (2013).
[6]
R. Montoliu et al. Discovering Places of Interest in Everyday Life from Smartphone Data. Multimedia Tools and Applications 62 (2013), 179--207.
[7]
S. Patel et al. Farther Than You May Think: An Empirical Investigation of the Proximity of Users to their Mobile Phones. In UbiComp (2006).
[8]
Y. Chon et al. Mobility prediction-based smartphone energy optimization for everyday location monitoring. In SenSys (2011).
Index Terms
- Locator: a self-adaptive framework for the recognition of relevant places
Recommendations
Context Awareness for Self-Adaptiveness in Smart Terminals
TRUSTCOM '11: Proceedings of the 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and CommunicationsNowadays smart terminals become increasingly multitudinous and popular. And because various sensors are embedded in them, the variety of applications in smart terminals is largely expanded. The information is widely used and valued in serious systems, ...
Privacy-aware contextual localization using network traffic analysis
The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery to users wireless devices based on her location. Since the sharing precise location remains a major privacy concern among ...
LOCATOR: location estimation system For wireless LANs
WMASH '04: Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspotsWith the wide spread growth of mobile computing devices and local area wireless networks, wireless network providers have started to target the users with value-added services based on the users' location information. Thus, location awareness and user ...
Comments
Information & Contributors
Information
Published In
September 2014
1409 pages
ISBN:9781450330473
DOI:10.1145/2638728
- General Chairs:
- AJ Brush,
- Adrian Friday,
- Program Chairs:
- Julie Kientz,
- James Scott,
- Junehwa Song
Copyright © 2014 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 September 2014
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing
September 13 - 17, 2014
Washington, Seattle
Acceptance Rates
Overall Acceptance Rate 764 of 2,912 submissions, 26%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 145Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 11 Aug 2024
Other Metrics
Citations
Cited By
View allView Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in