Cyberguide: a mobile context-aware tour guide
Wireless Networks - Special issue: mobile computing and networking: selected papers from MobiCom '96
Practical robust localization over large-scale 802.11 wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
WALRUS: wireless acoustic location with room-level resolution using ultrasound
Proceedings of the 3rd international conference on Mobile systems, applications, and services
The Horus WLAN location determination system
Proceedings of the 3rd international conference on Mobile systems, applications, and services
IEEE Transactions on Pattern Analysis and Machine Intelligence
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Symbolic object localization through active sampling of acceleration and sound signatures
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Indoor localization without infrastructure using the acoustic background spectrum
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Audio-based context recognition
IEEE Transactions on Audio, Speech, and Language Processing
SwordFight: enabling a new class of phone-to-phone action games on commodity phones
Proceedings of the 10th international conference on Mobile systems, applications, and services
On the (In-)Accuracy of GPS Measures of Smartphones: A Study of Running Tracking Applications
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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We present RoomSense, a new method for indoor positioning using smartphones on two resolution levels: rooms and within-rooms positions. Our technique is based on active sound fingerprinting and needs no infrastructure. Rooms and within-rooms positions are characterized by impulse response measurements. Using acoustic features of the impulse response and pattern classification, an estimation of the position is performed. An evaluation study was conducted to analyse the localization performance of RoomSense. Impulse responses of 67 within-rooms positions from 20 rooms were recorded with the hardware of a smartphone. In total 5360 impulse response measurements were collected. Our evaluation study showed that RoomSense achieves a room-level accuracy of 98% and a within-rooms positions accuracy of 96%. Additionally, the implementation of RoomSense as an Android App is presented in detail. The RoomSense App enables to identify an indoor location within one second.