Crowd Monitoring in Smart Destinations Based on GDPR-Ready Opportunistic RF Scanning and Classification of WiFi Devices to Identify and Classify Visitors’ Origins

Abstract

Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination.

A Tutorial on Privacy, RCM and Its Implications in WLAN

“…Despite the increased level of randomization, an efficient crowd-monitoring system was developed by the authors of [68], which is based on the passive detection of Probe Requests. The algorithm uses a statistical estimator to count devices by measuring the rate of Probe Requests received in 10 ms. Another solution for crowd monitoring is presented in [69], which uses the SSID information of the preferred Wi-Fi APs included in Probe Requests, in combination with the information of existing Wi-Fi APs, to determine the daily number of visitors at different locations through postprocessing. A comparable methodology is adopted in [70], where the objective was to de-anonymize the origin of individuals attending a specific event.…”

Conceptual Framework and Prospective Analysis of EU Tourism Data Spaces

“…[66]. These initiatives have ranged from implementing smart signage systems and interactive mobile applications to data management platforms and IoT sensors for environmental and visitor flow monitoring [67][68][69].…”

Tools for Ground-Truth-Free Passive Client Density Mapping in MAC-Randomized Outdoor WiFi Networks

“…As mentioned in Section 1.4, a way around this problem that has garnered considerable attention is the so-called de-randomization approach for PRs. In this approach [14][15][16][17][18][19][20][21][22][23][24][25][26], information from digital frame content such as the frame Sequence Number, particular Information Elements (IE), Preferred Network List, and certain time delay measurements derived from these quantities can be analyzed statistically to group together PRs arising from a unique client, thus undoing the effect of the MAC randomization. Though rigorous and often quite effective, a recognized drawback of this approach is that wireless device manufacturers are perpetually on the lookout for such security loopholes, which the next generation of devices is likely to close.…”