How to collaborate

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.

For the Spring 2022 term we are following Northeastern University regulations for on-site and remote work, this means that, unless something changes, students can conduct their research on-site in our lab under direct supervision of our team.

If you are interested in any of these projects, you are a current active student at Northeastern University, and you satisfy the prerequisites for the project you are interested in, please send an email to with subject “Spring 2022 Mon(IoT)r Lab collaboration” and a recent resume attached (with GPA), specifying the following:

  • What project you are interested in, why you are interested in that particular project, why you are a fit for that project, how you plan to use your existing experience to contribute to that project, how collaborating to the project aligns with your career goals. If you are interested in more than a 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 (number of classes and credits)
  • Any other time commitments you have during the semester, for example TA, RA in another group, or a co-op;
  • Your availability for a volunteer position, or for a paid/for credits position.
  • The possibility or preference to work on site, remotely, or both.

Please note that, in general, we have a preference for projects that are on-site, that last the whole semester, and for a minimum of 10 hours per week. Also, for first-time collaborations in our lab we usually prefer to start with a volunteer position first, and then consider other types of positions if the initial volunteer project is successful.

We will be reviewing applications for Spring 2022 as soon as we receive them until the positions are filled. Sometimes 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.

If you are interested to apply for Summer 2022 or later, we cannot guarantee that this list of projects will still be valid, and therefore we suggest to wait for the projects to be updated before applying (usually one month before the semester starts).


Current open 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. 

To achieve this goal, we will explore ways to trigger, analyze, and visualize IoT devices behaviors. For example, in the case of voice assistants (e.g., Amazon Echo and Google Home), we may play voice recordings and use network traffic analysis to determine whether the device is sharing conversations over the Internet or not.

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.

Some available research directions for this project that a student can choose are:

  • PROJECT 1A (new): Detect the presence and analyze the network traffic that groups of IoT devices exchange with each other.
  • PROJECT 1B (new): Analyze if an IoT device behaves differently when deployed on an IPv6 network with respect to an IPv4 network.
  • PROJECT 1C (new): Analyze if some IoT devices may influence each other when they are not explicitly connected together. En example is the Amazon sidewalk feature.


  1. Familiarity with the most important Internet and networking protocols and measurement tools (Ethernet, TCP/IP, DNS, Wireshark/tshark).
  2. Extensive programming experience (python recommended).
  3. Strong interest in one or more data analytics technologies (e.g., classification, clustering, statistical inference, data visualization).
  4. Strong interest in network security (e.g., traffic filtering, man-in-the-middle, intrusion detection).

Project 2. Internet of Things Dark Patterns and Voice Assistant Profiling

Dark patterns is a term that refers to any product design choices that make users do things that they might not normally want to do, typically creating value for a company with limited benefit (or at the expense) for their users. Dark patterns have been present and studied in the context of web and mobile apps. During our experience in running an IoT lab with hundreds of devices, we have noticed that many devices tend to exhibit some suspicious behavior such as trying to force the users into giving up unnecessary data, subscribing to additional services, or presenting the users with default settings that are not in their interests.
Among all the categories of IoT devices we have analyzed, we have seen a significant presence of dark patterns for devices that provide access to voice assistants (i.e., they can be operated with voice interactions). For example, they often try to propose “suggestions” to install specific skills or to buy a particular product. They even detect the presence of other IoT devices without explicit user consent, and they customize their recommendations based on that. Finally, some devices have the potential to profile their users based on their voice or their online activities, and provide biased answers or recommendations based on that.
In this project, we will analyze some of the IoT devices in our Mon(IoT)r Lab, with particular focus on voice-assistant enabled devices (e.g., Amazon Echo, Google Nest, Apple Homepod) to search for any evidence of user profiling or any other suspicious behavior that might fit the dark pattern definition.
The outcomes of this project will be: (1) A methodology for finding dark patterns and/or evidence of user profiling for the devices we analyze. (2) The compliance of identified dark patterns and user profiling with privacy policies and regulations. (3) Mitigation strategies to protect the users against these dark patterns and user profiling.

Some available research directions for this project that a student can choose are:

  • PROJECT 2A (ongoing): Focus on IoT dark patterns.
  • PROJECT 2B (ongoing): Focus on Voice Assistant profiling.
  1. Basic knowledge of python and bash.

Project 3. Visualization of Internet of Things traffic (backend or frontend)

The goal of this project is to visualize traffic statistics of IP-based IoT devices both in real time and offline. For example, visualizing statistics about incoming and outgoing traffic for each IoT device on a local network on a responsive web application and/or phone app. Statistics include traffic averages, traffic spikes, amount of traffic by device and by destination. The purpose is to give a tool to visualize what is happening / has happened on an IoT network.

To achieve this goal, the student will be given access to a tool we have previously developed, and will be responsible for learning how it works and adding improvement to its frontend or backend components.

Some available research directions for this project that a student can choose are:

  • PROJECT 3A (ongoing): Focus on backend.
  • PROJECT 3B (ongoing): Focus on frontend.


  1. For the frontend, knowledge of modern website design, especially Javascript-based web technologies
  2. For the backend, familiarity with Node.JS, in-memory (e.g., Redis) and noSQL (e.g., MongoDB) databases
  3. For both: extensive programming experience.

Project 4. Analysis of Automated Transcription Systems

As remote work and learning increase in popularity, people who are deaf or hard of hearing may depend on automated transcriptions to participate in business, school, entertainment or basic communication. Many others might also use these tools as an important convenience.
It’s important that these products be able to accurately transcribe speech, no matter the race or ethnicity of the speaker. Research has shown that digital assistants make more errors when attempting to recognize the speech of black users compared to white users. A similar disparity may exist in speech-to-text products that are widely used in videoconferencing and social media applications.
For a set of leading videoconferencing and social media products, as well as IoT voice assistants, compare word error rates by speaker race, gender and regional dialect.
This is an ongoing project. The student will determine the word error rate of some common videoconferencing and social media applications. They will then analyze the variation in error rates by race, gender, regional dialect, and other relevant factors.
This is an ongoing project.
  1. Extensive programming experience (python recommended).
  2. Experience in analyzing and plotting data.