The Horus WLAN location determination system
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Accuracy characterization for metropolitan-scale Wi-Fi localization
Proceedings of the 3rd international conference on Mobile systems, applications, and services
The pothole patrol: using a mobile sensor network for road surface monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proceedings of the 6th ACM conference on Embedded network sensor systems
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
CODES/ISSS '10 Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Place lab: device positioning using radio beacons in the wild
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
No need to war-drive: unsupervised indoor localization
Proceedings of the 10th international conference on Mobile systems, applications, and services
CrowdInside: automatic construction of indoor floorplans
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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We present Dejavu, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization suitable for car navigation. Our analysis shows that different road landmarks have a unique signature on cell-phone sensors; For example, going inside tunnels, moving over bumps, going up a bridge, and even potholes all 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 abundant virtual landmarks, to reset the accumulated error and achieve accurate localization. To maintain a low energy profile, Dejavu uses only energy-efficient sensors or sensors that are already running for other purposes. 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 in both city and highway driving show that Dejavu can localize cell phones to within 8.4 m median error in city roads and 16.6 m on highways. Moreover, compared to GPS and other state-of-the-art systems, Dejavu can extend the battery lifetime by 347%, achieving even better localization results than GPS in the more challenging in-city driving conditions.