| Title |
IoT-based Mobility Tracking for Smart City Applications |
| ID_Doc |
37935 |
| Authors |
Gebru, K; Casetti, C; Chiasserini, CF; Giaccone, P |
| Title |
IoT-based Mobility Tracking for Smart City Applications |
| Year |
2020 |
| Published |
|
| DOI |
10.1109/eucnc48522.2020.9200941 |
| Abstract |
The proliferation of IoT devices and the growing deployment of 5G networks combine to provide the perfect ecosystem for advanced smart city use cases. In this paper, we address the possibility of detecting and quantifying flows of people on city streets thanks to deployment of commercial sensors, connected to the 5G network, that capture WiFi probes transmitted by people's smartphones. We first outline the motivation and challenges of such a scenario. Then, we illustrate our approach and present results derived from live measurements in a testbed deployed in the city of Turin within the 5G-EVE project. We show that we can quite accurately estimate transit flows by simply collecting anonymized MAC addresses and timestamps from smartphones of passers-by. |
| Author Keywords |
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| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000632590800061 |
| WoS Category |
Computer Science, Information Systems; Telecommunications |
| Research Area |
Computer Science; Telecommunications |
| PDF |
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