WiFi positioning and Big Data to monitor flows of people on a wide scale

Alessandrini, Alfredo; Gioia, Ciro; Sermi, Francesco; Sofos, Ioannis; Tarchi, D.; Vespe, Michele

doi:10.1109/euronav.2017.7954224

Cited by 30 publications

(25 citation statements)

References 5 publications

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“…In this section, the experimental setup is briefly described; a more comprehensive description of the experiment is available in Alessandrini et al (2017).…”

Section: Methodsmentioning

confidence: 99%

“…Starting from these two assumptions, the WiFi positioning and tracking exploit the fact that smartphones repeatedly broadcast probe requests to identify known networks. The probe request contains the device unique identifier: the media access control (MAC) address (Alessandrini et al 2017). Therefore, in order to be detected, a smartphone does not have to be connected to a WiFi network, but it just needs to be within the range of a WiFi node, when the probe request is sent.…”

Section: Introductionmentioning

confidence: 99%

“…Privacy-related aspects are a fundamental topic, when it comes to locate people through WiFi. Some relevant considerations about this subject have been made in Alessandrini et al (2017), but the discourse on privacy and personal data has definitely grown up within and outside the EU, since the enforcement of the General Data Protection Regulation (GDPR) in April 2018. How does the GDPR affect the use of WiFi data?…”

Section: Introductionmentioning

confidence: 99%

“…This paper focuses on both cleaning and user positioning procedures, starting from the data collection of the 2016 Open Day of the Joint Research Centre (JRC) in Ispra (Italy) (Alessandrini et al 2017) and comparing different approaches on the basis of the results described in Gioia et al (2017). Data cleaning is conducted following different approaches: a first screening of the device can be made using the number of times a device is registered; the second criterion is based on the number of base stations registering the presence of the user; and a different approach to identify real user is developed considering the dispersion of the estimate user position.…”

Section: Introductionmentioning

confidence: 99%

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On cleaning strategies for WiFi positioning to monitor dynamic crowds

et al. 2019

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Monitoring open crowded areas is fundamental for policy makers to set up the proper measures for people security and safety. Different techniques have been developed to tackle this issue. The most relevant approaches, which are currently available to estimate the number of attenders, are based on the size of the area hosting the event, the count of people passing through specific points, and the employment of satellite images. All these techniques roughly estimate static crowds, but they may be limited by different factors such as the availability of satellite images, the cost of dedicated unmanned vehicles, and the capability to set up multiple counting points. In order to fill this gap, a tool to monitor dynamic crowds, based on WiFi positioning, is presented. The tool allows not only to assess the total number of people attending an event, but also to monitor their spatiotemporal distribution. In particular, the impact of the cleaning strategies on both the estimated number of participants and their spatiotemporal distribution is analyzed. The proposed approach is demonstrated using real data collected during the JRC Open Day 2016. From the results, the need of a clear strategy to identify real users in order to avoid misleading results emerges. Moreover, a proper setting of the thresholds used for the identification criteria is required. Such thresholds need to be set according to the dimension of the site, the geography of the WiFi network, and the duration of the event.

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“…In this section, the experimental setup is briefly described; a more comprehensive description of the experiment is available in Alessandrini et al (2017).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On cleaning strategies for WiFi positioning to monitor dynamic crowds

et al. 2019

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“…In [23] , an early experiment in tracking people flow by exploiting Wi-Fi data has been reported exploiting direct MAC address tracking. In [2] , a small scale experiment has been performed for tracking a limited number of people (80 0 0) in well-known and restricted area with 20 AP. The effective precision and assessment was not provided.…”

Section: Introductionmentioning

confidence: 99%

Wi-Fi based city users’ behaviour analysis for smart city

Bellini

Cenni

Nesi

et al. 2017

Journal of Visual Languages & Computing

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a b s t r a c tMonitoring, understanding and predicting city user behaviour (hottest places, trajectories, flows, etc.) is one the major topics in the context of Smart City management. People flow surveillance provides valuable information about city conditions, useful not only for monitoring and controlling the environmental conditions, but also to optimize the deliverying of city services (security, clean, transport,..). In this context, it is mandatory to develop methods and tools for assessing people behaviour in the city. This paper presents a methodology to instrument the city via the placement of Wi-Fi Access Points, AP, and to use them as sensors to capture and understand city user behaviour with a significant precision rate (the understanding of city user behaviour is concretized with the computing of heat-maps, origin destination matrices and predicting user density). The first issue is the positioning of Wi-Fi AP in the city, thus a comparative analyses have been conducted with respect to the real data (i.e., cab traces) of the city of San Francisco. Several different positioning methodologies of APs have been proposed and compared, to minimize the cost of AP installation with the aim of producing the best origin destination matrices. In a second phase, the methodology was adopted to select suitable AP in the city of Florence (Italy), with the aim of observing city users behaviour. The obtained instrumented Firenze Wi-Fi network collected data for 6 months. The data has been analysed with data mining techniques to infer similarity patterns in AP area and related time series. The resulting model has been validated and used for predicting the number of AP accesses that is also related to number of city users. The research work described in this paper has been conducted in the scope of the EC funded Horizon 2020 project Resolute ( http://www.resolute-eu.org ), for early warning and city resilience.

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