Mu, M. (2023) Network as a Sensor for Smart Crowd Analysis and Service Improvement. IEEE Network. 37(2), 144 - 152. 0890-8044.
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Abstract:
With the growing availability of data processing and machine learning infrastructures, crowd analysis is becoming an important tool to tackle economic, social, and environmental challenges in smart communities. The heterogeneous crowd movement data captured by IoT solutions can inform policy-making and quick responses to community events or incidents. However, conventional crowd-monitoring techniques using video cameras and facial recognition are intrusive to everyday life. This article introduces a novel non-intrusive crowd monitoring solution which uses 1,500+ software-defined networks (SDN) assisted WiFi access points as 24/7 sensors to monitor and analyze crowd information. Prototypes and crowd behavior models have been developed using over 900 million WiFi records captured on a university campus. We use a range of data visualization and time-series data analysis tools to uncover complex and dynamic patterns in large-scale crowd data. The results can greatly benefit organizations and individuals in smart communities for data-driven service improvement.
Additional Information:
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Creators:
Mu, M.
Date:
1 March 2023
Date Type:
Publication
Page Range:
144 - 152
Journal or Publication Title:
IEEE Network
Volume:
37
Number:
2
Number of Pages:
2421221
Language:
English
ISSN:
0890-8044
Status:
Published / Disseminated
Refereed:
Yes
Related URLs:
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