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Overcrowding detection in indoor events using scalable technologies

Lopez-Novoa, Unai, Aguilera, Unai, Emaldi, Mikel, Lopez-de-Ipina, Diego, Perez-de-Albeniz, Iker, Valerdi, David, Iturricha, Ibai and Arza, Eneko 2017. Overcrowding detection in indoor events using scalable technologies. Personal and Ubiquitous Computing 21 (3) , pp. 507-519. 10.1007/s00779-017-1012-6

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer
ISSN: 1617-4909
Date of Acceptance: 30 December 2016
Last Modified: 25 Jun 2020 13:42

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