An In-Home IoT Architecture for Epidemiological Deployments

P. Lundrigan, K. Min, N. Patwari, S. K. Kasera, K. Kelly, J. Moore, M. Meyer, S. C. Collingwood, F. Nkoy, B. Stone, and K. Sward

IEEE Workshop on Practical Issues in Building Sensor Network Applications (SenseApp), 2018


We design and build EpiFi, a novel architecture for in-home sensor networks which allows epidemiologists to easily design and deploy exposure sensing systems in homes. We work collaboratively with pediatric asthma researchers to design multiple studies and deploy EpiFi in homes. Here, we report on experiences from two years of deployments in 15 homes, of two different types of studies, including many deployments continuously monitored over the past 11 months. Based on lessons learned from these deployments and researchers, we develop a new mechanism for sensors to bootstrap their connectivity to a subject's home WiFi router and implement data reliability mechanisms to minimize loss in the network through a long-term deployment.


Managing in-home environments through sensing, annotating, and visualizing air quality data

J. Moore, P. Goffin, M. Meyer, P. Lundrigan, N. Patwari, K. Sward, and J. Wiese

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (Ubicomp '18), September 2018


Air quality is important, varies across time and space, and is largely invisible. Pioneering past work deploying air quality monitors in residential environments found that study participants improved their awareness of and engagement with air quality. However, these systems fielded a single monitor and did not support user-specified annotations, inhibiting their utility. We developed MAAV– a system to Measure Air quality, Annotate data streams, and Visualize real-time PM2.5 levels – to explore how participants engage with an air quality system addressing these challenges. MAAV supports collecting data from multiple air quality monitors, annotating that data through multiple modalities, and sending text message prompts when it detects a PM2.5 spike. MAAV also features an interactive tablet interface for displaying measurement data and annotations. Through six long-term field deployments (20-47 weeks, mean 37.7 weeks), participants found these system features important for understanding the air quality in and around their homes. Participants gained new insights from between-monitor comparisons, reflected on past PM2.5 spikes with the help of their annotations, and adapted their system usage as they familiarized themselves with their air quality data and MAAV. These results yield important insights for designing residential sensing systems that integrate into users’ everyday lives.


Smart home air filtering system: A randomized controlled trial for performance evaluation

Kyeong T. Min, Philip Lundrigan, Neal Patwari, Katherine Sward, and Scott C. Collingwood

3rd IEEE / ACM Conference on Connected Health: Applications, Systems, and Engineering (CHASE 2018)


STRAP: Secure TRansfer of Association Protocol

P. Lundrigan, N. Patwari, S. K. Kasera

The 27th International Conference on Computer Communications and Networks (ICCCN), 2018


When several internet-of-things devices are required to be installed in a smart home, significant effort is required to provide each device with the association information for the home's wireless router. We design and build a novel protocol called Secure Transfer of Association Protocol (STRAP), which securely bootstraps connectivity between a set of deployed WiFi devices and a home's wireless router. We show that STRAP works in a variety of environments and is faster than conventional methods for connecting WiFi devices to home wireless routers.


An Infrastructure for Generating Exposomes: Initial Lessons from the Utah PRISMS Platform

Katherine Sward, Philip Lundrigan, Neal Patwari, Ram Gouripeddi, Albert Lund, Julio Facelli

Presentation and Demonstration at the 27th Annual Meeting of the International Society of Exposure Science (ISES), October 2017

Demo abstract: IASA - indoor air quality sensing and automation

Kyeong T. Min, Philip Lundrigan, Neal Patwari

ACM/IEEE Information Processing in Sensor Networks 2017, April 2017