WiFi Fingerprint Clustering for Urban Mobility Analysis

WiFi Fingerprint Clustering for Urban Mobility Analysis

In this paper, we present an unsupervised learning approach to identify the user points of interest (POI) by exploiting WiFi measurements from smartphone application data. Due to the lack of GPS positioning accuracy in indoor, sheltered, and high rise building environments, we rely on widely availab...

Full description

Bibliographic Details
Main Authors: Sumudu Hasala Marakkalage, Billy Pik Lik Lau, Yuren Zhou, Ran Liu, Chau Yuen, Wei Quin Yow, Keng Hua Chong
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
POI extraction
clustering
data fusion
mobility analysis
unsupervised learning
Online Access:https://ieeexplore.ieee.org/document/9422709/

Internet

https://ieeexplore.ieee.org/document/9422709/

Similar Items