| In this dissertation we derive location-related context like mobility-states, co-mobility, speed and decelerations directly from the wireless signal strength information. The key insight is that the time-series of signal strength is robust to environmental factors that typically negatively affect the RSS-based localization systems. Therefore, inferring these physical properties directly from the time-series of wireless signal strength is more accurate than deriving them from location estimates.;We apply correlation and time warping algorithms to the time series of wireless signals to infer these properties. Our trace-driven experimental approach shows that our inference techniques can work with minimal infrastructure, are computationally efficient, requires no explicit user participation and can produce higher accuracies than location-based systems. We have also experimentally identified the factors that limit the accuracy of indoor localization and have proved the existing assumptions behind theoretical lower bounds of indoor localization incorrect.;Our results will enable new context aware applications, because accurate estimates of co-mobility and speed offer a richer set of primitives available to applications. Such applications can derive user mobility states like walking, running, driving or social states, such as if a user is in a meeting or alone. |
