The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
SurroundSense: mobile phone localization via ambience fingerprinting
Abstract
A growing number of mobile computing applications are centered around the user's location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone's camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87% when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization.
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RADAR: an in-building RF-based user location and tracking system
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An efficient k-means clustering algorithm: analysis and implementation
Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017616 · Cited by 5,660
Cited by
A survey of mobile phone sensing
Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine · doi:10.1109/mcom.2010.5560598 · Cited by 2,292
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons
He, S., & Chan, S.-H. G. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2464084 · Cited by 1,355
Electronic frog eye: Counting crowd using WiFi
Xi, W., Zhao, J., Li, X.-Y., Zhao, K., Tang, S., Liu, X., & Jiang, Z. (2014) · IEEE INFOCOM 2014 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2014.6847958 · Cited by 398
Accurate WiFi based localization for smartphones using peer assistance
Liu, H., Yang, J., Sidhom, S., Wang, Y., Chen, Y., & Ye, F. (2014) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2013.140 · Cited by 151
Low cost crowd counting using audio tones
Kannan, P. G., Venkatagiri, S. P., Chan, M. C., Ananda, A. L., & Peh, L.-S. (2012) · Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems · doi:10.1145/2426656.2426673 · Cited by 97
Metadata
- Authors
- Azizyan, M., Constandache, I., & Roy Choudhury, R.
- Venue
- Proceedings of the 15th annual international conference on Mobile computing and networking
- Publisher
- ACM
- Published
- 2009-09-20
- Pages
- 261-272
- ISSN
- 1543-5679 (ISSN-L)
- OpenAlex
- W2142216726
- Cited by
- 723
- References
- 3
- FWCI
- 73.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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