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Overcrowding detection in indoor events using scalable technologies

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Abstract

The increase in the number of large-scale events held indoors (i.e., conferences and business events) opens new opportunities for crowd monitoring and access controlling as a way to prevent risks and provide further information about the event’s development. In addition, the availability of already connectable devices among attendees allows to perform non-intrusive positioning during the event, without the need of specific tracking devices. We present an algorithm for overcrowding detection based on passive Wi-Fi requests capture and a platform for event monitoring that integrates this algorithm. The platform offers access control management, attendees monitoring and the analysis and visualization of the captured information, using a scalable software architecture. In this paper, we evaluate the algorithm in two ways: First, we test its accuracy with data captured in a real event, and then we analyze the scalability of the code in a multi-core Apache Spark-based environment. The experiments show that the algorithm provides accurate results with the captured data, and that the code scales properly.

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Data from 4YFN 2015 Monday

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Data from 4YFN 2015 Tuesday

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Notes

  1. Cloudera—http://www.cloudera.com.

  2. Hue—http://gethue.com.

  3. Django—https://www.djangoproject.com.

  4. Sicafe stands for SIstema de Control de Acceso y Flujo a Eventos, the Spanish for Event flow and access control system.

  5. R-tree—https://pypi.python.org/pypi/Rtree.

  6. Shapely—https://pypi.python.org/pypi/Shapely.

  7. Leaflet— http://leafletjs.com.

  8. MaskCanvas— https://github.com/domoritz/leaflet-maskcanvas.

  9. 4YFN— https://4yfn.com.

  10. MWC— https://www.mobileworldcongress.com.

  11. http://aditium.com/en/success-cases/4yfn-2015-2.

  12. Pymobility— https://github.com/panisson/pymobility.

  13. http://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobs-part-1.

  14. collectd—https://collectd.org.

  15. Prometheus— https://prometheus.io.

  16. Grafana—http://grafana.org.

  17. Overcrowding algorithm source code— https://github.com/morelab/overcrowd-simulator.

  18. HDP— http://hortonworks.com/hdp.

  19. MapR — https://www.mapr.com.

  20. Elastic Stack—https://www.elastic.co.

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Acknowledgements

The authors would like to thank Dr. Carlos Pérez-Miguel for his aid in this work and the anonymous reviewers for their insightful comments.

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Authors and Affiliations

  1. Deusto Institute of Technology, DeustoTech, University of Deusto, Avda. de las Universidades 24, 48007, Bilbao, Spain

    Unai Lopez-Novoa, Unai Aguilera, Mikel Emaldi & Diego López-de-Ipiña

  2. Fon Labs, Avda. Las Arenas 7, 48930, Getxo, Spain

    Iker Pérez-de-Albeniz & David Valerdi

  3. Aditium, Abadetxe Kalea 9, 48180, Loiu, Spain

    Ibai Iturricha & Eneko Arza

Authors
  1. Unai Lopez-Novoa
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  2. Unai Aguilera
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  3. Mikel Emaldi
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  4. Diego López-de-Ipiña
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  5. Iker Pérez-de-Albeniz
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  6. David Valerdi
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  7. Ibai Iturricha
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  8. Eneko Arza
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Corresponding author

Correspondence to Unai Lopez-Novoa.

Additional information

This work has been partially supported by the Basque Country Government under the Gaitek funding program (IG-2014/00172) and the Spanish Ministry of Economy and Competitiveness (Grant Number TIN2013-47152-C3-3-R).

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Lopez-Novoa, U., Aguilera, U., Emaldi, M. et al. Overcrowding detection in indoor events using scalable technologies. Pers Ubiquit Comput 21, 507–519 (2017). https://doi.org/10.1007/s00779-017-1012-6

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