Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data

In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a building via GPS signal changes. Second, we use a barometer equipped smartphone to measure the change in barometric pressure from the entrance of the building to the victim's indoor location. Unlike impractical previous approaches, our system is the first that does not require the use of beacons, prior knowledge of the building infrastructure, or knowledge of user behavior. We demonstrate real-world feasibility through 63 experiments across five different tall buildings throughout New York City where our system predicted the correct floor level with 100% accuracy.Comment: International Conference on Learning Representations (ICLR 2018

A report on personally identifiable sensor data from smartphone devices

An average smartphone is equipped with an abundance of sensors to provide a variety of vital functionalities and conveniences. The data from these sensors can be collected in order to find trends or discover interesting correlations in the data but can also be used by nefarious entities for the purpose of revealing the identity of the persons who generated this data.In this paper, we seek to identify what types of sensor data can be collected on a smartphone and which of those types can pose a threat to user privacy by looking into the hardware capabilities of modern smartphone devices and how smartphone data is used in the literature. We then summarize some implications that this information could have on the GDPR.Comment: 17 pages, 5 tables, parts of this paper were used in the PhD thesis by the same author available at https://archive-ouverte.unige.ch/unige:11286