Presumably Simple: Monitoring Crowds Using WiFi

Abstract

Monitoring crowds is receiving much attention. An increasingly popular technique is to scan mobile devices, notably smartphones. We take a look at scanning such devices based on transmitted WiFi messages. Although research on capturing crowd patterns using WiFi detections has been done, there are not many published results when it comes to tracking movements. This is not surprising when realizing that the data provided by WiFi scanners is susceptible to many seemingly erroneous and missed detections, caused by the use of randomized network addresses, overlap between scanners, high variance in WiFi detection ranges, among other sources. In this paper, we investigate various techniques for cleaning up sets of raw detections to sets that can subsequently be used for crowd analytics. To this end, we introduce two different quality metrics to measure the effects of applying various data filters. We test our approach using a data set collected from 27 WiFi scanners spread across the downtown area of a Dutch city where at that time a 3-day multi-stage festival took place attended by some 130,000 people.

CrowdEstimator

“…Wi-Fi-based approaches [7][2] [19][16] [28] by analyzing wireless packets provide more flexible and low-cost options to perform crowd detection and human mobility monitoring in large-scale environments. The work in [7] proposes some filtering algorithms to handle uncertain and noisy data from Wi-Fi sniffers due to MAC address randomization, overlapping coverage between Wi-Fi sniffers, and high variance in Wi-Fi sensing ranges.…”

“…Wi-Fi-based approaches [7][2] [19][16] [28] by analyzing wireless packets provide more flexible and low-cost options to perform crowd detection and human mobility monitoring in large-scale environments. The work in [7] proposes some filtering algorithms to handle uncertain and noisy data from Wi-Fi sniffers due to MAC address randomization, overlapping coverage between Wi-Fi sniffers, and high variance in Wi-Fi sensing ranges. In [2], Wi-Fi sniffers are deployed in an industrial exhibition to capture the Wi-Fi probes from mobile devices of attendees, and mobility patterns in each monitored zone are analyzed such as the number of unique MAC addresses, the number of Wi-Fi probes, and the brand statistics of mobile devices in each zone.…”

Raspberry Pi as an Inexpensive Platform for Real-Time Traffic Jam Analysis on the Road

“…Chilipirea et al [3] performed experiments on WiFi tracking of pedestrians. They could improve the quality of the data sets by various data filters.…”

“…[2], starting from 2014. Unfortunately, recent publications [3] [4] show clearly that attacking privacy has only become a little bit more difficult, but by far not impossible as initially claimed by mobile devices companies.…”

Design and simulation of sensor networks for tracking Wifi users in outdoor urban environments

“…Using WiFi signals to track individuals is a feasible alternative which has recently received much attention ( [1], [2], [3], [4]). With the exploding popularity of smart phones, the percentage of individuals who produce WiFi signals is quite large.…”