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Longitudinal study of a building-scale RFID ecosystem
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International Conference On Mobile Systems, Applications And Services archive
Proceedings of the 7th international conference on Mobile systems, applications, and services table of contents
Kraków, Poland
SESSION: Experimental platforms table of contents
Pages 69-82  
Year of Publication: 2009
ISBN:978-1-60558-566-6
Authors
Evan Welbourne  University of Washington, Seattle, WA, USA
Karl Koscher  University of Washington, Seattle, WA, USA
Emad Soroush  University of Washington, Seattle, WA, USA
Magdalena Balazinska  University of Washington, Seattle, WA, USA
Gaetano Borriello  University of Washington, Seattle, WA, 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
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ABSTRACT

Radio Frequency IDentification (RFID) deployments are becoming increasingly popular in both industrial and consumer-oriented settings. To effectively exploit and operate such deployments, important challenges must be addressed, from managing RFID data streams to handling limitations in reader accuracy and coverage. Furthermore, deployments that support pervasive computing raise additional issues related to user acceptance and system utility. To better understand these challenges, we conducted a four-week study of a building-scale EPC Class-1 Generation-2 RFID deployment, the "RFID Ecosystem", with 47 readers (160 antennas) installed throughout an 8,000 square meter building. During the study, 67 participants having over 300 tags accessed the collected RFID data through applications including an object finder and a friend tracker and several tools for managing personal data. We found that our RFID deployment produces a very manageable amount of data overall, but with orders of magnitude difference among various participants and objects. We also find that the tag detection rates tend to be low with high variance across the type of tag, participant and object. Users need expert guidance to effectively mount their tags and are encouraged by compelling applications to wear tags more frequently. Finally, probabilistic modeling and inference techniques promise to enable more complex applications by smoothing over gaps and errors in the data, but must be applied with care as they add significant computational and storage overhead.


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.

 
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Collaborative Colleagues:
Evan Welbourne: colleagues
Karl Koscher: colleagues
Emad Soroush: colleagues
Magdalena Balazinska: colleagues
Gaetano Borriello: colleagues