EE/CSE - Best Wi-Fi AP Project

With the explosion of wireless devices, the radio spectrum has become a highly constrained resource. We bridge the gap between wireless engineering and public policy through real-world spectrum measurements. By analyzing actual usage patterns and performance limits, our lab provides the critical data that policymakers and industry leaders need to build a faster, more accessible wireless future.

We are seeking motivated students interested in real-world spectrum measurement and wireless networking, with a specific focus on Wi-Fi systems. This project investigates the key parameters that drive a device's choice of Access Point (AP) and evaluates how the selected AP performs relative to nearby alternatives. Students will design experimental protocols and write automation scripts on a Raspberry Pi to capture indoor signal data, then process and analyze the resulting datasets using Python tools such as Jupyter, Pandas, and NumPy.

Name of research group, project, or lab
GHOSH Lab
Why join this research group or lab?

This project gives students hands-on experience with wireless research and analysis using real-world Wi-Fi datasets. Students will gain experience with end-to-end data cycle: retrieving real-world data, processing it for analysis (cleaning, transforming, and engineering features), as well as data analysis and modeling. This project will also be published, giving students exposure to technical writing and academic publishing process.

Logistics Information:
Project categories
Computer Science & Engineering
Electrical Engineering
Student ranks applicable
Sophomore
Junior
Senior
Student qualifications
  • Strong interest in data science or technology policy.
  • Basic understanding in wireless technologies, particularly Wi-Fi.
  • Proficiency with (or a strong willingness to learn) Linux/Python scripting and SBC development. 
  • Proficiency with (or a strong willingness to learn) Python-based data analysis framework. Familiarity with other analytical frameworks (e.g., machine learning/AI) is also welcome.
  • Mobility is required to participate in the experiments. Students will be expected to wheel carts around indoor building (e.g., Fitz-Cush) on multiple floors.
Hours per week
1 credit / 3-6 hours
2 credits / 6-12 hours
Compensation
Research for Credit
Number of openings
2
Techniques learned
  • Linux/Python scripting, as well as Jupyter-based data processing and visualization.
  • Deeper understanding of real-world Wi-Fi implementations.
  • Technical writing and preparation of results for publication in a research conference or journal.
Project start
2026 Fall Semester
Contact Information:
Mentors
mrochman@nd.edu
Postdoc Researcher
mghosh3@nd.edu
mghosh3
ecase@nd.edu
Name of project director or principal investigator
Monisha Ghosh
Email address of project director or principal investigator
mghosh3@nd.edu
2 sp. | 1 appl.
Hours per week
1 credit / 3-6 hours (+1)
1 credit / 3-6 hours2 credits / 6-12 hours
Project categories
Computer Science & Engineering (+1)
Computer Science & EngineeringElectrical Engineering