Home > REU > Passive, Crowd-Sourced WiFi Characterization

Passive, Crowd-Sourced WiFi Characterization

Dr. Striegel
Dr. Striegel
Principal Investigator: 

Dr. Striegel, Department of Computer Science and Engineering

Project Summary:

Although WiFi and cellular are nearly everywhere, the performance of said wireless technologies tends to vary dramatically both in time and space. While many of us have relied on Speedtest or other tools to help assess just how poor the nearby wireless performance tends to be, the challenge is that tools like Speedtest and others tend to be slow and take significant amounts of bandwidth to run. We have created a recent set of tools called Fast Mobile Network Characterization that lets us do equivalent tests roughly 100x faster and with 10-100x less bandwidth with approaches that both work by actively probing the network and approaches that are completely passive.  While we have largely demonstrated many of the tools in the lab, the next step is to explore how well the tools translate out into the real world.         

Student's Role:

The student is expected to help with Data gathering of common WiFi signals throughout campus undertaken roughly four hours for every two weeks consisting of traversing campus or the nearby Eddy Street Commons to gather data. The student would use a set of tools developed by Prof. Striegel's graduate students on Linux laptops to gather data across multiple 2.4 GHz and 5 GHz WiFi channels. Students would gain experience in configuring WiFi devices for monitor mode and validating correct operation. Some travel to downtown Chicago, gathering data on the South Shore line (which offers free WiFi) to construct longitudinal tests on a Raspberry Pi to periodically record WiFi data over time in a specific location. Use the provided evaluation tools then to plot and analyze (via Tableau) the stability of the data readings across a given week and discuss/analyze the stability of the data through basic tests. Working with d3.js, write basic tools to help visualize the data (via bar and line graphs). Work with the PI than to create several proofs of concept GUI skeletons for sharing data with the user.   Strong student programming skills are desirable.