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.

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 using the contact information of the project of your choice with subject “Fall 2026 Mon(IoT)r Lab collaboration PROJECT NAME” (mandatory to receive an answer!) 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, clubs, 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 Fall 2026 as soon as we receive them and will be back to you within two weeks, 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 a later term (e.g., Spring 2027), 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 happens within two weeks after the Spring or Fall semester starts. We are not currently offering summer projects.

Fall 2026 projects

Project “IoT Experiments with SPHERE”

The Internet of Things (IoT) has become a fixture of modern homes, with devices such as smart speakers, video doorbells, and connected appliances offering convenience while introducing new privacy and security risks. Studying these devices at scale requires shared research infrastructure that lets researchers interact with many heterogeneous devices and observe their network behavior under controlled conditions. SPHERE is an NSF Mid-Scale Research Infrastructure project that provides exactly this: a testbed enabling remote interaction with more than 300 IoT devices and capture of the network traffic they generate.

The goal of this project is to conduct hands-on IoT experiments using the SPHERE testbed, exercising its capabilities across a diverse set of devices and experimental scenarios. Students will use the testbed to interact with IoT devices, capture and inspect their network traffic, and confirm that the platform reliably supports the kinds of experiments the research community depends on. The primary goal is to systematically verify the platform functionalities across devices and use cases, characterizing which experimental workflows the testbed supports well and documenting behavior under real research conditions.

As secondary goals, students may optionally use the testbed to reproduce results from existing IoT research (see our previous papers), and to work alongside our development team to identify, report, and help resolve issues discovered during experimentation. This feedback loop directly strengthens the testbed as a research tool for the broader community.

This research can be performed on site and/or remotely.

Prerequisites:

  1. Programming experience in Python (required).
  2. Familiarity with networking protocols and traffic analysis tools (e.g., TLS, DNS, Wireshark/tshark) is a nice to have.
  3. Strong interest in cybersecurity, privacy, and IoT.
  4. Willingness to learn new technical skills and work with an evolving research platform.

Contact information: Daniel Dubois (d.dubois@northeastern.edu), specifying “Fall 2026 Mon(IoT)r Lab collaboration IoT EXPERIMENTS WITH SPHERE” in the subject line.

Project “Automating Physical IoT Interaction”

The Internet of Things (IoT) encompasses a wide range of devices that respond to physical input: buttons on smart speakers, touchscreens on smart watches, switches on connected appliances, and more. Studying these devices at scale requires the ability to interact with them automatically and repeatably, without a human physically present for every action. SPHERE, an NSF Mid-Scale Research Infrastructure project, provides a testbed enabling remote interaction with more than 300 IoT devices, and reliable automated actuation is central to making such a platform useful for research.

The goal of this project is to research and develop solutions for automating physical interactions with IoT devices on the SPHERE testbed. Students will work alongside our SPHERE team to design, build, and improve mechanisms that trigger device actions remotely and reliably. Example problems include making mechanical button pushers more consistent, enabling remote control of a smart watch, and devising approaches for actuating devices that lack convenient software interfaces.

Depending on the type of IoT device chosen, this project may involve working with microcontrollers for sensing and actuation and designing 3D printed mounts to hold devices and actuators in place.

This research is on-site only.

Prerequisites:

  1. Programming experience in Python (required).
  2. Familiarity with 3D printing (required).
  3. Familiarity with microcontrollers for sensing and actuation (required).
  4. Willingness to learn new technical skills and work with an evolving research platform.

Contact information: Daniel Dubois (d.dubois@northeastern.edu), specifying “Fall 2026 Mon(IoT)r Lab collaboration AUTOMATING PHYSICAL IoT INTERACTION” in the subject line.

Project “Analysis of LLM-based Voice Assistants”

Voice assistants are increasingly common in devices such as smart speakers, smart TVs, and home automation systems. Traditional systems like Amazon Alexa and Google Assistant have been widely studied and behave according to predefined rules with limited responses. A new generation is now powered by large language models (LLMs), such as Amazon Alexa+ and Google Gemini, enabling more flexible, conversational interactions and tighter integration with user data and IoT devices. These capabilities also introduce new privacy and safety risks, such as unintended disclosure of sensitive information and susceptibility to jailbreaks and prompt manipulation.

In this project, the student will investigate how LLM-based voice assistants handle personal data, model users, and control smart home devices, and will evaluate their robustness against adversarial inputs. The work will involve voice assistants available in the Mon(IoT)r and SPHERE Labs, spanning both traditional and LLM-based systems.

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.

This research can be performed on site and/or remotely.

Prerequisites:

  1. Experience with Python.
  2. Experience using LLMs.
  3. Experience using voice assistants such as Alexa, Alexa+, Google Assistant, Google Gemini, or Siri.

Contact information: Daniel Dubois (d.dubois@northeastern.edu), specifying “Fall 2026 Mon(IoT)r Lab collaboration ANALYSIS OF LLM-BASED VOICE ASSISTANTS” in the subject line.

Project “Surveillance Pricing”: Understanding Personalization and Dynamic Pricing Patterns on Consumer Platforms

A recent Consumer Reports investigation found that Instacart showed different prices for the same grocery items to different shoppers: nearly three-quarters of tested items varied, with some prices differing by as much as 23% per item. When such prices are based on information gathered about consumers, known as “surveillance pricing,” people can easily get ripped off. There are important fairness concerns when prices are based on sensitive traits – like income, age, zip code, and family status. However, little is publicly known about companies’ surveillance pricing practices.
The goal of this project is to empirically measure how consumer platforms personalize prices and price-adjacent experiences using controlled online experiments. Rather than relying on platform disclosures, we will build a measurement framework that can (1) simulate diverse user “personas,” (2) collect time-series snapshots of prices/fees/discounts across treatments, and (3) analyze whether observed differences are consistent, explainable (e.g., surge in demand, inventory, current events), or suggestive of differential treatment.
This project will focus on consumer e-commerce environments where personalization is plausible and highly impactful. Our current targets are food and grocery delivery platforms (i.e., Grubhub, UberEats, DoorDash, Instacart), where users may see differences in categories such as delivery and service fees, recommended items, or promotion eligibility. The resulting evidence can help inform debates around algorithmic fairness and consumer protection.
In the Spring 2026 semester, this project will focus on (a) building reliable, cross-platform scraping and data-collection infrastructure, (b) defining and validating a set of behavioral and contextual signals that personas can express (e.g., filtering by deals, searching for expensive options, time-of-day ordering patterns), and (c) executing pilot studies across a curated set of services (i.e., restaurant ordering, grocery delivery) to test for systematic differences across treatments.
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.
This research can be performed on site and/or remotely.

Prerequisites:

  1. Broad interest in consumer protection research, marketplace integrity, and the societal impacts of algorithmic decision-making
  2. Able to contribute in at least one of the following areas: experimental design, web automation, data collection, and analysis
  3. Programming experience (JavaScript experience highly recommended; familiarity with browser automation tools like Puppeteer/Selenium is a plus)
  4. Comfort working with structured datasets (CSV/JSON) and evolving codebases with many moving parts
  5. Willingness to learn new technical skills.

Contact information: Prof. David Choffnes (d.choffnes@northeastern.edu), specifying “Fall 2026 Mon(IoT)r Lab collaboration SURVEILLANCE PRICING” in the subject line.