This paper reports our experience with crowd monitoring technologies in the challenging real-world conditions of a modern, open-space museum. We seized the opportunity to use the NEMO science center as a testbed, and studied the effectiveness of neighborhood discovery and density estimation algorithms in a network formed by visitors wearing bracelets emitting RF beacons. The diverse set of conditions (flash crowds in open spaces vs. single person booths) revealed three interesting findings: (i) state-of-the-art density estimation fails in 80% of the cases, (ii) RSS-based classifiers fail too, because their underlying assumptions do not hold in many scenarios, and (iii) neighborhood discovery can obtain exact information in an energy-efficient way, provided that static and mobile nodes are differentiated to filter out “passers by” clobbering the true popularity of an exhibit. The overall lesson from the experiment is that today’s algorithms are quite far from the ideal of monitoring popularity in a privacy-preserving and energy-efficient way with minimal infrastructure across the set of heterogeneous conditions encountered in practice.
Original languageEnglish
Title of host publicationProceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, EWSN 2017
PublisherJunction Publishing
Pages78-83
Number of pages6
ISBN (Electronic)978-0-9949886-1-4
StatePublished - 2017
EventEWSN 2017 - Uppsala, Sweden
Duration: 20 Feb 201722 Feb 2017
http://www.ewsn2017.org/

Publication series

NameEWSN 8217;17
PublisherJunction Publishing

Conference

ConferenceEWSN 2017
Abbreviated titleEWSN'17
CountrySweden
CityUppsala
Period20/02/1722/02/17
Internet address

    Research areas

  • Crowd monitoring, Density estimation

ID: 35052790