Standard

An Open-Space Museum As a Testbed for Popularity Monitoring in Real-World Settings. / Cattani, Marco; Protonotarios, Ioannis; Martella, Claudio; van Velzen, Joost; Zuniga, Marco; Langendoen, Koen.

Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, EWSN 2017. Junction Publishing, 2017. p. 78-83 (EWSN 8217;17).

Research output: Scientific - peer-reviewConference contribution

Harvard

Cattani, M, Protonotarios, I, Martella, C, van Velzen, J, Zuniga, M & Langendoen, K 2017, An Open-Space Museum As a Testbed for Popularity Monitoring in Real-World Settings. in Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, EWSN 2017. EWSN 8217;17, Junction Publishing, pp. 78-83, EWSN 2017, Uppsala, Sweden, 20/02/17.

APA

Cattani, M., Protonotarios, I., Martella, C., van Velzen, J., Zuniga, M., & Langendoen, K. (2017). An Open-Space Museum As a Testbed for Popularity Monitoring in Real-World Settings. In Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, EWSN 2017 (pp. 78-83). (EWSN 8217;17). Junction Publishing.

Vancouver

Cattani M, Protonotarios I, Martella C, van Velzen J, Zuniga M, Langendoen K. An Open-Space Museum As a Testbed for Popularity Monitoring in Real-World Settings. In Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, EWSN 2017. Junction Publishing. 2017. p. 78-83. (EWSN 8217;17).

Author

Cattani, Marco ; Protonotarios, Ioannis ; Martella, Claudio ; van Velzen, Joost ; Zuniga, Marco ; Langendoen, Koen. / An Open-Space Museum As a Testbed for Popularity Monitoring in Real-World Settings. Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, EWSN 2017. Junction Publishing, 2017. pp. 78-83 (EWSN 8217;17).

BibTeX

@inbook{486ed43ba7714624b049255cea47a6fe,
title = "An Open-Space Museum As a Testbed for Popularity Monitoring in Real-World Settings",
abstract = "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.",
keywords = "Crowd monitoring, Density estimation",
author = "Marco Cattani and Ioannis Protonotarios and Claudio Martella and {van Velzen}, Joost and Marco Zuniga and Koen Langendoen",
year = "2017",
series = "EWSN 8217;17",
publisher = "Junction Publishing",
pages = "78--83",
booktitle = "Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, EWSN 2017",
address = "Canada",

}

RIS

TY - CHAP

T1 - An Open-Space Museum As a Testbed for Popularity Monitoring in Real-World Settings

AU - Cattani,Marco

AU - Protonotarios,Ioannis

AU - Martella,Claudio

AU - van Velzen,Joost

AU - Zuniga,Marco

AU - Langendoen,Koen

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Crowd monitoring

KW - Density estimation

UR - http://resolver.tudelft.nl/uuid:486ed43b-a771-4624-b049-255cea47a6fe

M3 - Conference contribution

T3 - EWSN 8217;17

SP - 78

EP - 83

BT - Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, EWSN 2017

PB - Junction Publishing

ER -

ID: 35052790