We introduce a new technique for determining a mobile phone's indoor location even when Wi-Fi infrastructure is unavailable or sparse. Our technique is based on a new ambient sound fingerprint called the Acoustic Background Spectrum (ABS). An ABS serves well as a room fingerprint because it is compact, easily computed, robust to transient sounds, and surprisingly distinctive. As with other fingerprint-based localization techniques, location is determined by measuring the current fingerprint and then choosing the "closest" fingerprint from a database. An experiment involving 33 rooms yielded 69% correct fingerprint matches meaning that, in the majority of observations, the fingerprint was closer to a previous visit's fingerprint than to any fingerprints from the other 32 rooms. An implementation of ABS-localization called Batphone is publicly available for Apple iPhones. We used Batphone to show the benefit of using ABS-localization together with a commercial Wi-Fi-based localization method. In this second experiment, adding ABS improved room-level localization accuracy from 30% (Wi-Fi only) to 69% (Wi-Fi and ABS). While Wi-Fi-based localization has difficulty distinguishing nearby rooms, Batphone performs just as well with nearby rooms; it can distinguish pairs of adjacent rooms with 92% accuracy.

We demonstrate an indoor localization technique to be presented as a full paper at this MobiSys conference. In that paper, we introduce a new technique for determining mobile phone's indoor location even when Wi-Fi infrastructure is unavailable or sparse. Our technique is based on a new ambient sound fingerprint called the Acoustic Background Spectrum (ABS). This demonstration has two components. First, it shows attendees a live view of the ABS in the demonstration hall. This allows attendees to test the claim that the ABS is stable and robust to transient noise. Attendees can speak or make noise and observe the consequent effect on ABS. In the second demonstration, short sound recordings from various rooms are played on a set of headphones. Simultaneously, a photograph of the room and a plot of the ABS is shown. This demonstrates the ambient sound variations present in a set of sample rooms and allows attendees to test their own ability to distinguish locations based on sound.

We describe a technique to detect the presence of computer users. This technique relies on sonar using hardware that already exists on commodity laptop computers and other electronic devices. It leverages the fact that human bodies have a different effect on sound waves than air and other objects. We conducted a user study in which 20 volunteers used a computer equipped with our ultrasonic sonar software. Our results show that it is possible to detect the presence or absence of users with near perfect accuracy after only ten seconds of measurement. We find that this technique can differentiate varied user positions and actions, opening the possibility of future use in estimating attention level.