Network as a Sensor for Smart Crowd Analysis and Service Improvement

Mu Mu

doi:10.1109/MNET.001.2200345

Network as a Sensor for Smart Crowd Analysis and Service Improvement

Mu Mu

Technology

Research output: Contribution to Journal › Article › peer-review

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.

Original language	English
Pages (from-to)	144 - 152
Number of pages	9
Journal	IEEE Network
Volume	37
Issue number	2
DOIs	https://doi.org/10.1109/MNET.001.2200345
Publication status	Published - 1 Mar 2023

Bibliographical note

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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10.1109/MNET.001.2200345

Mu_Mu_IEEE_2023_Network_as_a_Sensor_for_Smart_Crowd_Analysis_and_Service_ImprovementAccepted author manuscript, 2.31 MBLicence: CC BY

https://ieeexplore.ieee.org/document/10239367

Cite this

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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.",

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Network as a Sensor for Smart Crowd Analysis and Service Improvement

Abstract

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