Together or Alone: Detecting Group Mobility with Wireless Fingerprints

TL;DR

This paper introduces a wireless fingerprint-based method to detect group and individual mobility, achieving high accuracy in real-world office experiments, enabling social network analysis from mobility data.

Contribution

It presents a novel wireless fingerprinting approach for detecting group and individual mobility, with real-world validation and social network generation capabilities.

Findings

95% space correlation accuracy

79% group mobility detection accuracy

Effective social network generation from mobility data

Abstract

This paper proposes a novel approach for detecting groups of people that walk "together" (group mobility) as well as the people who walk "alone" (individual movements) using wireless signals. We exploit multiple wireless sniffers to pervasively collect human mobility data from people with mobile devices and identify similarities and the group mobility based on the wireless fingerprints. We propose a method which initially converts the wireless packets collected by the sniffers into people's wireless fingerprints. The method then determines group mobility by finding the statuses of people at certain times (dynamic/static) and the space correlation of dynamic people. To evaluate the feasibility of our approach, we conduct real world experiments by collecting data from 10 participants carrying Bluetooth Low Energy (BLE) beacons in an office environment for a two-week period. The proposed approach captures space correlation with 95% and group mobility with 79% accuracies on average. With the proposed approach we successfully 1) detect the groups and individual movements and 2) generate social networks based on the group mobility characteristics.