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Cascadia: A System for Specifying, Detecting, and Managing RFID Events
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International Conference On Mobile Systems, Applications And Services archive
Proceeding of the 6th international conference on Mobile systems, applications, and services table of contents
Breckenridge, CO, USA
SESSION: Context monitoring table of contents
Pages: 281-294  
Year of Publication: 2008
ISBN:978-1-60558-139-2
Authors
Evan Welbourne  University of Washington, Seattle, WA, USA
Nodira Khoussainova  University of Washington, Seattle, WA, USA
Julie Letchner  University of Washington, Seattle, WA, USA
Yang Li  University of Washington, Seattle, WA, USA
Magdalena Balazinska  University of Washington, Seattle, WA, USA
Gaetano Borriello  University of Washington, Seattle, WA, USA
Dan Suciu  University of Washington, Seattle, WA, USA
Sponsors
ACM: Association for Computing Machinery
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

Cascadia is a system that provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data. Cascadia provides three important services. First, it allows application developers and even users to specify events using either a declarative query language or an intuitive visual language based on direct manipulation. Second, it provides an API that facilitates the development of applications which rely on RFID-based events. Third, it automatically detects the specified events, forwards them to registered applications and stores them for later use (e.g., for historical queries).

We present the design and implementation of Cascadia along with an evaluation that includes both a user study and measurements on traces collected in a building-wide RFID deployment. To demonstrate how Cascadia facilitates application development, we built a simple digital diary application in the form of a calendar that populates itself with RFID-based events. Cascadia copes with ambiguous RFID data and limitations in an RFID deployment by transforming RFID readings into probabilistic events. We show that this approach outperforms deterministic event detection techniques while avoiding the need to specify and train sophisticated models.


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
Nodira Khoussainova: colleagues
Julie Letchner: colleagues
Yang Li: colleagues
Magdalena Balazinska: colleagues
Gaetano Borriello: colleagues
Dan Suciu: colleagues