Loc{lib,trace,eva,ana}: research tools for 802.11-based positioning systems
Proceedings of the second ACM international workshop on Wireless network testbeds, experimental evaluation and characterization
CILoS: a CDMA indoor localization system
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
GammaSense: Infrastructureless Positioning Using Background Radioactivity
EuroSSC '08 Proceedings of the 3rd European Conference on Smart Sensing and Context
A taxonomy for radio location fingerprinting
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
A grid-based algorithm for on-device GSM positioning
Proceedings of the 12th ACM international conference on Ubiquitous computing
Collaborative cellular-based location system
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
BLINK: a high throughput link layer for backscatter communication
Proceedings of the 10th international conference on Mobile systems, applications, and services
LocateMe: Magnetic-fields-based indoor localization using smartphones
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Barometric phone sensors: more hype than hope!
Proceedings of the 15th Workshop on Mobile Computing Systems and Applications
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When a mobile user dials 911, a key to arriving to the emergency scene promptly is knowing the location of the mobile user. This paper presents SkyLoc, a GSM fingerprinting-based localization system that runs on a mobile phone and identifies the current floor of a user in tall multi-floor buildings. Knowing the floor in a tall building significantly reduces the area that emergency service personnel have to canvas to locate the individuals in need. We evaluated our system in three multi-floor buildings located in Washington DC, Seattle and Toronto. Our system identifies the floor correctly in up to 73% of the cases and is within 2 floors in 97% of the cases. The system is robust as it works for different network operators, when the training and testing sets were collected with different hardware and up to one month apart. In addition, we show that feature selection techniques that select a subset of highly relevant radio sources for fingerprint matching nearly double the localization accuracy of our system.