Opportunistic crowd sensing in WiFi-enabled indoor areas

Abstract : Crowd sensing in indoor areas is becoming more and more fundamental for flow management, security and surveillance, or building usage statistics. This paper deals with a simple crowd sensing approach, which opportunistically exploits the already deployed WiFi networks, thus avoiding dedicated wiring and installations. The proposed algorithm is based on a two-step procedure that first applies a Wavelet decomposition of the signal strength data and then exploits the obtained coefficients to learn the unknown relation between crowd presence and signal changes. To this end, a customized learning-by-example (LBE) algorithm is trained for successive real-time crowd detection. The results of the experimental validation are presented to assess system potentialities and current limitations.
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https://hal.archives-ouvertes.fr/hal-01264445
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Submitted on : Friday, January 29, 2016 - 12:16:46 PM
Last modification on : Thursday, April 26, 2018 - 4:03:58 PM

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Fabrizio Robol, Federico Viani, Alessandro Polo, Enrico Giarola, Paola Garofalo, et al.. Opportunistic crowd sensing in WiFi-enabled indoor areas. 2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Jul 2015, Vancouver, Canada. ⟨10.1109/APS.2015.7304523⟩. ⟨hal-01264445⟩

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