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The UbiDCC Data Set: Collecting Wi-Fi and Bluetooth Scans During a Large-scale Conference

Published:01 November 2015Publication History

ABSTRACT

In this paper, we report on a large-scale Data Collection Campaign (DCC). It took place during the UbiComp/ISWC 2013 conference. Over 700 attendees were registered for this 3-days event. We contribute the UbiDCC data set that is publicly available. It contains (1) mobility traces of nearly 1200 devices at the venue extracted by processing the collected raw data, (2) Bluetooth and Wi-Fi raw data of 37 active participants, and (3) their demographic data. We show how we increased the quality of the data set by deploying static encounters and passively tracking users' devices. Finally, we describe and make publicly available the Android application that was used during the campaign and can be reused for further DCCs.

References

  1. N. Aharony, W. Pan, C. Ip, I. Khayal, and A. Pentland. Social fMRI: Investigating and Shaping Social Mechanisms in the Real World. Pervasive and Mobile Computing, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. P. Baumann, J. Klaus, and S. Santini. Locator : A Self-adaptive Framework for the Recognition of Relevant Places. In UbiComp'14 Adjunct, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. P. Baumann, W. Kleiminger, and S. Santini. The Influence of Temporal and Spatial Features on the Performance of Next-place Prediction Algorithms. In UbiComp'13, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Y. Chon, H. Shin, E. Talipov, and H. Cha. Evaluating mobility models for temporal prediction with high-granularity mobility data. In PerCom'12, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  5. Y. Chon, E. Talipov, H. Shin, and H. Cha. Mobility Prediction-based Smartphone Energy Optimization for Everyday Location Monitoring. In SenSys'11, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. T. M. T. Do and D. Gatica-Perez. Where and what: Using Smartphones to Predict Next Locations and Applications in Daily Life. Pervasive and Mobile Computing, 12:79--91, June 2014.Google ScholarGoogle ScholarCross RefCross Ref
  7. N. Eagle and A. S. Pentland. Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing, 10(4):255--268, Nov. 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. P. Hui, A. Chaintreau, J. Scott, R. Gass, J. Crowcroft, and C. Diot. Pocket Switched Networks and Human Mobility in Conference Environments. In WDTN'05, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Laurila, D. Gatica-Perez, and I. Aad. The Mobile Data Challenge: Big Data for Mobile Computing Research. In Pervasive'12, 2012.Google ScholarGoogle Scholar
  10. A. B. M. Musa and J. Eriksson. Tracking Unmodified Smartphones Using Wi-Fi Monitors. SenSys'12, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A.-K. Pietiläinen, E. Oliver, J. LeBrun, G. Varghese, and C. Diot. MobiClique: Middleware for Mobile Social Networking. In WOSN'09, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Rodrig, C. Reis, R. Mahajan, D. Wetherall, and J. Zahorjan. Measurement-based Characterization of 802.11 in a Hotspot Setting. In E-WIND'05, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. L. Song, D. Kotz, R. Jain, and X. He. Evaluating Next-cell Predictors with Extensive Wi-Fi Mobility Data. In INFOCOM'04, 2004.Google ScholarGoogle Scholar
  14. N. Taylor, T. Bartindale, J. Vines, and P. Olivier. Exploring Delegate Engagement with an Augmented Conference. In UbiComp'14, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. L. Vu, Q. Do, and K. Nahrstedt. Jyotish: A Novel Framework for Constructing Predictive Model of People Movement from Joint Wifi/Bluetooth Trace. In PerCom'11, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. L. Vu, P. Nguyen, K. Nahrstedt, and B. Richerzhagen. Characterizing and Modeling People Movement from Mobile Phone Sensing Traces. Pervasive and Mobile Computing, 17:220--235, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. D. Wagner, A. Rice, and A. Beresford. Device Analyzer: Understanding Smartphone Usage. In MOBIQUITOUS'13, 2013.Google ScholarGoogle Scholar

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  1. The UbiDCC Data Set: Collecting Wi-Fi and Bluetooth Scans During a Large-scale Conference

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      cover image ACM Conferences
      CSAR '15: Proceedings of the 1st Workshop on Context Sensing and Activity Recognition
      November 2015
      62 pages
      ISBN:9781450338424
      DOI:10.1145/2820716

      Copyright © 2015 ACM

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      Publication History

      • Published: 1 November 2015

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