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An Experimental Study for Tracking Crowd in Smart Cities
Ref: CISTER-TR-181201       Publication Date: 2018

An Experimental Study for Tracking Crowd in Smart Cities

Ref: CISTER-TR-181201       Publication Date: 2018

Abstract:
Knowledge about people density and mobility patterns is the key element towards efficient urban development in smart cities. The main challenges in large-scale people tracking are the recognition of people density in a specific area and tracking the people flow path. To address these challenges, we present SenseFlow, a lightweight people tracking system for smart cities. SenseFlow utilizes off-the-shelf sensors which sniff probe requests periodically polled by user's smartphones in a passive manner. We demonstrate the feasibility of SenseFlow by building a proof-of-concept prototype and undertaking extensive evaluations in real-world settings. We deploy the system in one laboratory to study office hours of researchers, a crowded public area in a city to evaluate the scalability and performance ''in the wild'', and four classrooms in the university to monitor the number of students. We also evaluate SenseFlow with varying walking speeds and different models of smartphones to investigate the people flow tracking performance.

Authors:
Kai Li
,
Chau Yuen
,
Salil S. Kanhere
,
Kun Hu
,
Wei Zhang
,
Fan Jiang
,
Xiang Liu


Published in IEEE Systems Journal, IEEE, pp 1-12.

DOI:10.1109/JSYST.2018.2880028.
ISSN: 1937-9234.

Notes: Early Access Article



Record Date: 3, Dec, 2018