skip to main content
10.1145/2517351.2517421acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article

Using unlabeled Wi-Fi scan data to discover occupancy patterns of private households

Published:11 November 2013Publication History

ABSTRACT

This poster presents the homeset algorithm, a lightweight approach to estimate occupancy schedules of private households. The algorithm relies on the mobile phones of households' occupants to collect Wi-Fi scans. The scans are then used to determine if occupants are at home or not. The algorithm operates in an autonomous fashion using only information available locally on the mobile phones. We validate our approach using a data set from the Nokia Lausanne Data Collection Campaign.

References

  1. P. Baumann, W. Kleiminger, and S. Santini. The Influence of Temporal and Spatial Features on the Performance of Next-place Prediction Algorithms. In Proc. of the 2013 ACM Intl. Joint Conf. on Pervasive and Ubiquitous Computing (UbiComp'13), Sept. 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. W. Kleiminger, C. Beckel, A. Dey, and S. Santini. Inferring Household Occupancy Patterns from Unlabelled Sensor Data. Technical Report 795, ETH Zurich, Department of Computer Science, Sept. 2013.Google ScholarGoogle Scholar
  3. J. Krumm and A. J. B. Brush. Learning Time-based Presence Probabilities. In Proc. of the 9th Intl. Conf. on Pervasive Computing (Pervasive'11), June 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. K. Laurila, D. Gatica-Perez, I. Aad, J. Blom, O. Bornet, T. Do, O. Dousse, J. Eberle, and M. Miettinen. The Mobile Data Challenge: Big Data for Mobile Computing Research. In Proc. of the Mobile Data Challenge by Nokia Workshop (co-located with Pervasive'12), June 2012.Google ScholarGoogle Scholar
  5. R. Montoliu, J. Blom, and D. Gatica-Perez. Discovering Places of Interest in Everyday Life from Smartphone Data. Multimedia Tools and Applications, 62(1):179--207, Jan. 2013.Google ScholarGoogle ScholarCross RefCross Ref
  6. S. Scellato, M. Musolesi, C. Mascolo, V. Latora, and A. T. Campbell. NextPlace: A Spatio-Temporal Prediction Framework for Pervasive Systems. In Proc. of the 9th Intl. Conference on Pervasive Computing (Pervasive'11), June 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Conferences
    SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
    November 2013
    443 pages
    ISBN:9781450320276
    DOI:10.1145/2517351

    Copyright © 2013 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.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 11 November 2013

    Check for updates

    Qualifiers

    • research-article

    Acceptance Rates

    SenSys '13 Paper Acceptance Rate21of123submissions,17%Overall Acceptance Rate174of867submissions,20%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader