This use case collects data from commercial Wi-Fi probe-detection scanners and infers flow densities and direction of transit of people on city streets. The scanners detect the probe-request messages sent periodically by all the active Wi-Fi interfaces and thus by the mobile devices, typically, smartphones, carried by the people moving in the area. The smartphone address, even if randomised and anonymised, is used as a personal “fingerprint” of the device and allows us to infer the mobility of its owner. The use case is hosted in Turin.
The Pilot architecture comprises:
The area covered by the testbed includes the area between Politecnico di Torino and Porta Susa Train Station, one of the main transport backbones from the campus area and one of the main train stations in Turin. 6 Wi-Fi scanners are active in the area.
In mobility detection services, high accuracy is achieved by increasing the density of sensors, whose coverage is limited to a few tens-hundreds meters in an urban area. This has the side effect of increasing the computation burden, and thus a centralised solution cannot scale at all in a large urban area. Fortunately, flow densities and directions are local properties of a given area, and thus their detection can be distributed. 5G enables the adoption of MEC, by which the computation is distributed across the mobile infrastructure, allowing to scale the approach and its accuracy to large urban area.
Location: Turin, Italy
Partners involved: Nokia, Greece; OTE.
EC funding reference: Horizon 2020; H2020-ICT-2018-1; ICT-17-2018 - 5G End-to-End Facility
Funding cycle: July 2018-June 2021
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