# WiFi Fingerprint Clustering for Urban Mobility Analysis

arXiv: https://arxiv.org/abs/2105.01274 · HTML: https://mlsanity.com/paper/2105.01274/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 6 of 6)

- [Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications](https://mlsanity.com/paper/2003.02449/) (2020)
- [A Survey of Data Fusion in Smart City Applications](https://mlsanity.com/paper/1905.11933/) (2019)
- [Collaborative SLAM based on Wifi Fingerprint Similarity and Motion Information](https://mlsanity.com/paper/2001.02759/) (2019)
- [Extracting Point of Interest and Classifying Environment for Low Sampling Crowd Sensing Smartphone Sensor Data](https://mlsanity.com/paper/1701.03379/) (2017)
- [Crowd-sensing Simultaneous Localization and Radio Fingerprint Mapping based on Probabilistic Similarity Models](https://mlsanity.com/paper/1904.11712/) (2019)
- [Identifying Indoor Points of Interest via Mobile Crowdsensing: An Experimental Study](https://mlsanity.com/paper/1908.08929/) (2019)

## Built on by (most cited first, 1 of 1)

- [Clustering and Analysis of GPS Trajectory Data using Distance-based Features](https://mlsanity.com/paper/2212.00206/) (2022)

## Abstract (arXiv)

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 available WiFi access points (AP) in contemporary urban areas to accurately identify POI and mobility patterns, by comparing the similarity in the WiFi measurements. We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights, namely the indoor POI within a building, neighbourhood activity, and micro-mobility of the users. Our results show that it is possible to identify the aforementioned insights, with the fusion of WiFi and GPS, which are not possible to identify by only using GPS.