ACM Home Page
Please provide us with feedback. Feedback
Advancing wireless link signatures for location distinction
Full text PdfPdf (509 KB)
Source
International Conference on Mobile Computing and Networking archive
Proceedings of the 14th ACM international conference on Mobile computing and networking table of contents
San Francisco, California, USA
SESSION: Spectrum sensing and management table of contents
Pages 26-37  
Year of Publication: 2008
ISBN:978-1-60558-096-8
Authors
Junxing Zhang  University of Utah, Salt Lake City, UT, USA
Mohammad H. Firooz  University of Utah, Salt Lake City, UT, USA
Neal Patwari  University of Utah, Salt Lake City, UT, USA
Sneha K. Kasera  University of Utah, Salt Lake City, UT, USA
Sponsors
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 17,   Downloads (12 Months): 296,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1409944.1409949
What is a DOI?

ABSTRACT

Location distinction is the ability to determine when a device has changed its position. We explore the opportunity to use sophisticated PHY-layer measurements in wireless networking systems for location distinction. We first compare two existing location distinction methods - one based on channel gains of multi-tonal probes, and another on channel impulse response. Next, we combine the benefits of these two methods to develop a new link measurement that we call the complex temporal signature. We use a 2.4 GHz link measurement data set, obtained from CRAWDAD [10], to evaluate the three location distinction methods. We find that the complex temporal signature method performs significantly better compared to the existing methods. We also perform new measurements to understand and model the temporal behavior of link signatures over time. We integrate our model in our location distinction mechanism and significantly reduce the probability of false alarms due to temporal variations of link signatures.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
P. Bahl and V. N. Padmanabhan. RADAR: an in-building RF-based user location and tracking system. In IEEE INFOCOM 2000, pages 775--784, 2000.
 
3
V. Bose, M. Ismert, M. Wellborn, and J. Guttag. Virtual radios. IEEE JSAC, 17(4):591--602, April 1999.
4
 
5
6
 
7
 
8
J. Mitola. The software radio architectuer. IEEE Communications Magzine, 33(5):26--38, May 1995.
 
9
 
10
N. Patwari and S. K. Kasera. CRAWDAD utah CIR measurements. http://crawdad.cs.dartmouth.edu/meta.php? name=utah/CIR.
11
 
12
J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319--2323, Dec 2000.
13
14
 
15
K. Yao and F. Lorenzelli. Localization in sensor networks. ST Journal of Research, 4(1):80--96, 2007.

Collaborative Colleagues:
Junxing Zhang: colleagues
Mohammad H. Firooz: colleagues
Neal Patwari: colleagues
Sneha K. Kasera: colleagues