Improving Bluetooth-beacon-based Localization with Survival Analysis



We have been developing a location infrastructure for infection control of COVID-19 on the university campus. Our system deploys Bluetooth beacons to each room and estimates the room that the user is staying in based on the detected beacon signals. However, we noticed that solely using the physical parameters such as signal strength often causes room estimation errors. To overcome this challenge, we proposed a classification algorithm based on survival analysis, leveraging the user habits that people’s stay time within the room and the room type strongly correlates with the probability of moving to different rooms.

Riku Yamashita, Yoshihiro Kawahara, Yuuki Nishiyama