Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., 6 8m) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as 2m and 1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., $6\sim8m$) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m$, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

This work addresses the fundamental problem of distinguishing between a driver and passenger using a mobile phone, which is the critical input to enable numerous safety and interface enhancements. Our detection system leverages the existing car stereo infrastructure, in particular, the speakers and Bluetooth network. Our acoustic approach has the phone send a series of customized high frequency beeps via the car stereo. The beeps are spaced in time across the left, right, and if available, front and rear speakers. After sampling the beeps, we use a sequential change-point detection scheme to time their arrival, and then use a differential approach to estimate the phone's distance from the car's center. From these differences a passenger or driver classification can be made. To validate our approach, we experimented with two kinds of phones and in two different cars. We found that our customized beeps were imperceptible to most users, yet still playable and recordable in both cars. Our customized beeps were also robust to background sounds such as music and wind, and we found the signal processing did not require excessive computational resources. In spite of the cars' heavy multipath environment, our approach had a classification accuracy of over 90 percent, and around 95 percent with some calibrations. We also found, we have a low false positive rate, on the order of a few percent.

This work addresses the fundamental problem of distinguishing between a driver and passenger using a mobile phone, which is the critical input to enable numerous safety and interface enhancements. Our detection system leverages the existing car stereo infrastructure, in particular the speakers and Bluetooth network. Our acoustic approach has the phone send a series of customized high frequency beeps via the car stereo. The beeps are spaced in time across the left, right, and if available, front and rear speakers. After sampling the beeps, we use a sequential change-point detection scheme to time their arrival, and then use a differential approach to estimate the phone's distance from the car's center. From these differences a passenger or driver classification can be made. To validate our approach, we experimented with two kinds of phones and in two different cars. We found that our customized beeps were imperceptible to most users, yet still playable and recordable in both cars. Our customized beeps were also robust to background sounds such as music and wind, and we found the signal processing did not require excessive computational resources. In spite of the cars' heavy multi-path environment, our approach had a classification accuracy of over 90%, and around 95% with some calibrations. We also found we have a low false positive rate, on the order of a few percent.