Accurate And Energy-Efficient Gps-Less Outdoor Localization

Location-based services have become an important part of our daily lives. However, such services require continuous user tracking while preserving the scarce cell-phone battery resource. In this article, we present Dejavu, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization. Dejavu is capable of localizing and navigating both pedestrian and in-vehicle users in real time. Our analysis shows that, whether walking or in-vehicle, when the user encounters a road landmark such as going inside a tunnel, ascending a staircase, or even moving over a bump, all these different landmarks affect the inertial sensors on the phone in a unique pattern. Dejavu employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered virtual landmarks, to reset the dead-reckoning accumulated error and achieve accurate localization. To maintain a lowenergy profile, Dejavu uses only energy-efficient sensors or sensors that are already running for other purposes. Moreover, Dejavu provides a localization confidence measure along with its predicted location. This improves the usability of the predicted location from end users' perspective.We present the design of Dejavu and how it leverages crowd-sourcing to automatically learn virtual landmarks and their locations. Our evaluation results from implementation on different Android devices using different testbeds showing that Dejavu can localize cell-phones in vehicles with a median error of 8.4m in city roads and 16.6m on highways and can localize cell-phones carried by pedestrians with a median error of 3.0m. Moreover, compared to the global position system (GPS) and other state-of-the-art systems, Dejavu can extend the battery lifetime by up to 347%, while achieving even better localization results than GPS in the more challenging in-city areas. In addition, Dejavu estimates the localization confidence measure accurately with a median error of 2.3m and 31cm for in-vehicle and pedestrian users, respectively.