[u' @article{lopez-novoa_overcrowding_2017, title = {Overcrowding detection in indoor events using scalable technologies}, volume = {21}, issn = {1617-4909}, shorttitle = {{PUC}}, url = {https://link.springer.com/article/10.1007/s00779-017-1012-6}, doi = {10.1007/s00779-017-1012-6}, 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\u2019s 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.}, number = {3}, journal = {Personal and Ubiquitous Computing}, author = {Lopez-Novoa, Unai and Aguilera, Unai and Emaldi, Mikel and L\xf3pez-de-Ipi\xf1a, Diego and P\xe9rez-de-Albeniz, Iker and Valerdi, David and Iturricha, Ibai and Arza, Eneko}, month = feb, year = {2017}, note = {00001}, keywords = {Cloud-based computing, Indoor Positioning, Q2, jcr1.924}, pages = {507--519} }']