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Mining crowd mobility and WiFi hotspots on a densely-populated campus

Published:11 September 2017Publication History

ABSTRACT

Understanding crowd activities at large-scale and diagnosing existing problems of planning on densely-populated campus are fundamentally hard through traditional ways of measurement and management. In this paper, we demonstrate how to collect data from ubiquitous WiFi networks (WLAN), and further to characterize the mobility of campus residents by exploring time-frequency patterns with spatial context. On the campus of Tsinghua University (where everyday nearly 60, 000 mobile devices appear in the public areas of more than 110 buildings), we obtain large-scale observations on physical activities, and provide insights for better diagnosing of WiFi hotspots.

References

  1. Chloë Brown, Christos Efstratiou, Ilias Leontiadis, Daniele Quercia, Cecilia Mascolo, James Scott, and Peter Key. 2014. The architecture of innovation: Tracking face-to-face interactions with ubicomp technologies. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 811--822. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Kasthuri Jayarajah, Archan Misra, Xiao-Wen Ruan, and Ee-Peng Lim. 2015. Event Detection: Exploiting Socio-Physical Interactions in Physical Spaces. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. ACM, 508--513. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Injong Rhee, Minsu Shin, Seongik Hong, Kyunghan Lee, Seong Joon Kim, and Song Chong. 2011. On the levy-walk nature of human mobility. IEEE/ACM transactions on networking (TON) 19, 3 (2011), 630--643. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Dashun Wang, Dino Pedreschi, Chaoming Song, Fosca Giannotti, and Albert-Laszlo Barabasi. 2011. Human mobility, social ties, and link prediction. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1100--1108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Mengyu Zhou, Kaixin Sui, Minghua Ma, Youjian Zhao, Dan Pei, and Thomas Moscibroda. 2016. MobiCamp: a Campus-wide Testbed for Studying Mobile Physical Activities. In Proceedings of the 3rd Workshop on Physical Analytics. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. Mining crowd mobility and WiFi hotspots on a densely-populated campus

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    • Published in

      cover image ACM Conferences
      UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
      September 2017
      1089 pages
      ISBN:9781450351904
      DOI:10.1145/3123024

      Copyright © 2017 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 September 2017

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