Room-Level Localization and Automated Contact Tracing via Internet of Things (IoT) Nodes and Machine Learning Algorithm

Contact tracing has become a vital practice in reducing the spread of COVID-19 among staff in all industries, especially those in high-risk occupations such as healthcare workers. Our research team has investigated how wearable IoT devices can alleviate this problem by utilizing 802.11 wireless beacon frames broadcasted from pre-existing access points in a building to achieve room-level localization. Notable improvements to this low-cost localization technique’s accuracy are achieved via machine learning by implementing the random forest algorithm. Using random forest, historical data can train the model and make more informed decisions while tracking other nodes in the future. In this project, employees’ and patients’ locations while in a building (e.g., a healthcare facility) can be time-stamped and stored in a database. With this data available, contact tracing can be automated and accurately conducted, allowing those who have been in contact with a confirmed positive COVID-19 case to be notified and quarantined immediately. This paper presents the application of the random forest algorithm on broadcast frame data collected in February of 2020 at Sentara RMH in Harrisonburg, Virginia, USA. Our research demonstrates the combination of affordability and accuracy possible in an IoT beacon frame-based localization system that allows for historical recall of room-level localization data.