How to collaborate

Applications for SPRING 2025 ARE CLOSED.

Please come back in May for SUMMER 2025 and FALL 2025 opportunities.

We are excited to announce the list of collaboration opportunities in our Mon(IoT)r IoT lab. These can be  interesting opportunities to get exposed to cutting-edge IoT technologies and understand how they work.

If you are interested in any of these projects, you are a current active undergraduate or graduate student at Northeastern University, and you satisfy the prerequisites for the project you are interested in, please send an email to moniotr@ccs.neu.edu with subject “Spring 2025 Mon(IoT)r Lab collaboration” and a recent resume attached (with GPA), specifying the following:

  • What project you are interested in, specifying (a) why you are interested in that particular project, (b) why you are a fit for that project, (c) how you plan to use your existing experience to contribute to that project, (d) how collaborating to the project aligns with your career goals. If you are interested in more than one project, please rank them starting from the one you are interested the most.
  • The preferred start date and end date for the collaboration, and the total number of average hours you plan to spend per week on this project;
  • Your expected course load for the semester (list of classes and credits);
  • Any other time commitments you have during the semester, for example TA, RA, a co-op, side jobs, on campus / off campus activities, both paid and unpaid;
  • Your availability for a volunteer (unpaid) position. Note that we do not currently have any available paid or for-credits positions;
  • The Northeastern University campus location you are based on and the possibility or preference to work on site in Boston, remotely, or both.

Please note that, in general, we have a preference for projects that are on-site, that last a whole semester (15 weeks), and for a minimum of 10 hours per week.

We will start reviewing applications for Spring 2025 as soon as we receive them and keep the projects open until the positions are filled. After we start reviewing applications, it may take us up to two weeks to get back to you, so please be patient if you do not hear back by then. Also, for some projects, we may give you a take-home exercise related to the project before starting working with us.

If you are interested in applying for Summer 2025 or later, we cannot guarantee that this list of projects will still be valid. Therefore, we suggest waiting for the projects to be updated before applying, which typically within two weeks after the semester starts.

Spring 2025 projects

Project 1. Internet of Things Analysis From Network Traffic

Internet of Things (IoT) devices are increasingly found in homes, providing useful functionality for devices such as TVs, smart speakers, and video doorbells. Along with their benefits come potential risks, since these devices can communicate information (audio recordings, video recordings, television viewing habits) about their users to other parties over the Internet. However, understanding these risks is difficult due to heterogeneity in devices’ online behaviors. For example, smart speakers responding to voice commands send very different network traffic than a smart power plug that is activated via a companion app.

The goal of this project is to measure what IoT devices are doing, simply based on the network traffic they generate. For example, we would like to know if a smart speaker is recording audio from users when it should not, and we can automatically infer this if we have a good model and analysis of what normal, expected recording behavior looks like.

This project will have several outcomes, including published source code and data, published research papers in academic venues, and press articles about our findings through our journalist partners at the New York Times and other prominent venues.

In the Spring 2025 semester, this project will focus on IPv6 communication, the Matter IoT protocol, and/or longitudinal studies by comparing data we collected in our previous IoT publications and data from new experiments.

This research can only be performed on site in our Boston lab. No remote option available.

Prerequisites:

  1. Familiarity with the most important Internet and networking protocols and measurement tools (IPv4, IPv6, DNS, Wireshark/tshark, port scanning).
  2. Extensive programming experience (python recommended).
  3. Strong interest in network security (e.g., traffic filtering, man-in-the-middle, intrusion detection)

Project 2: IoT User Profiling and Data Sharing

IoT devices and companion apps are becoming increasingly common in our homes. While convenient, they raise privacy concerns as they are often always on, capable of recording environmental data, and sharing this information with advertisers and data brokers. For instance, smart TVs may share viewing habits for profiling or sale to third parties.
This project investigates how IoT device interactions contribute to user profiling, the risks involved (e.g., data sharing with advertisers), and whether these practices are disclosed in the devices’ privacy policies. For example, does naming a smart light “swimming pool” lead to that data being shared with platforms (e.g., Google Ads) to profile the user as high-net-worth?

To perform this investigation, the student will focus on a subset of IoT devices from the Mon(IoT)r Lab (e.g., home automation, smart gateways, smart cameras, etc.), accessed through their physical interfaces, companion apps, or voice assistant skills.

The project is organized into four research activities: (1) the analysis of privacy policies to assess disclosed potential for user profiling and data sharing; (2) the creation of user profiles for the devices, including interactions and configurations to simulate specific users; (3) probing the devices for profiling evidence through data access requests; (4) searching for profiling evidence beyond the devices, such as profiling and targeted ads from third-party services (e.g., Google Ads).

This research will be conducted in the Mon(IoT)r Lab (https://moniotrlab.khoury.northeastern.edu/), where the student will have access to all necessary IoT devices. No remote option available.

Basic knowledge of Python, Bash, and/or Selenium is recommended to automate tasks related to discovering user profiling and data sharing instances.

 

Project 3. Analysis of Transcription Errors in IoT and Social Media Platforms

Voice-controlled IoT (e.g., smart speakers) and social media platforms are increasingly becoming an important part of everyday life, and so is their functionality and need for accessibility. One of the features these platforms offer is the ability to automatically transcribe spoken material, which allows users to employ them for controlling their IoT devices, for remote work, for remote learning, or simply basic communication. As the adoption of these platforms increases and automatic transcriptions become a necessity, especially for those who are working in their second language or are affected by physical or hearing disabilities, it is important that the transcripts they generate are accurate, no matter the speaker’s gender, race, or other characteristics.

In this project, students will help analyzing IoT and social media platforms by curating and improving existing spoken material, designing and running experiments for selected platforms, and determining transcription errors.

Specifically, students will be offered the opportunity to participate in one or more of these research activities: (1) listening to the spoken material we already have (containing voices with different characteristics) and checking whether it is correct and matches the transcripts we have; (2) playing the spoken material to one or more selected IoT platforms (e.g., Google Assistant, Amazon Alexa, Apple Siri), social media platforms (e.g., YouTube, Facebook, Microsoft Stream), videoconferencing platforms (e.g., Zoom, Google Meet, BlueJeans, Webex) and retrieving generated transcripts; (3) measuring and comparing the transcription errors exhibited by the analyzed platform(s).

This research can be performed in our lab or remotely.

More information about our previous work on this project.

Prerequisites:

  1. Familiarity with command line interfaces and scripting (bash recommended).
  2. Some programming experience (python recommended).
  3. Willing to learn tools for splitting, merging, and playing audio files.