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Behavior-based cleaning for unreliable RFID data sets.

Fan H, Wu Q, Lin Y - Sensors (Basel) (2012)

Bottom Line: Radio Frequency IDentification (RFID) technology promises to revolutionize the way we track items and assets, but in RFID systems, missreading is a common phenomenon and it poses an enormous challenge to RFID data management, so accurate data cleaning becomes an essential task for the successful deployment of systems.Moreover, a Reverse Order Filling Mechanism is proposed to ensure a more complete access to get the movement behavior characteristics of tag.Finally, we validate our solution with a common RFID application and demonstrate the advantages of our approach through extensive simulations.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, National University of Defense Technology, Changsha 410073, China. huafan@nudt.edu.cn

ABSTRACT
Radio Frequency IDentification (RFID) technology promises to revolutionize the way we track items and assets, but in RFID systems, missreading is a common phenomenon and it poses an enormous challenge to RFID data management, so accurate data cleaning becomes an essential task for the successful deployment of systems. In this paper, we present the design and development of a RFID data cleaning system, the first declarative, behavior-based unreliable RFID data smoothing system. We take advantage of kinematic characteristics of tags to assist in RFID data cleaning. In order to establish the conversion relationship between RFID data and kinematic parameters of the tags, we propose a movement behavior detection model. Moreover, a Reverse Order Filling Mechanism is proposed to ensure a more complete access to get the movement behavior characteristics of tag. Finally, we validate our solution with a common RFID application and demonstrate the advantages of our approach through extensive simulations.

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f6-sensors-12-10196: Analysis in detail.

Mentions: Furthermore, we analyze the case in one time period in detail (missing rate = 50%, and the length of time is 1,000 epochs). As shown in Figure 6, Reality refers to readings that would have been produced by a perfect reader without missreadings. Raw means the raw data that the reader actually read and while the others refer to the data filled with four kinds of data cleaning methods (BBS, SMURF, 5 epoch sliding-window, and 35 epoch sliding-window). The bold horizontal lines indicate the tag is present/read, and vice versa. The line at the bottom of Figure 6 is the real data of read rate without miss reading, and another line above it is the estimate of read rate by BBS (n = 7, w0 = 1 and w1 = 2). Obviously, compared with sliding window methods, BBS greatly improved the accuracy of RFID data cleaning. In particular, our BBS method not only accurately draws whether the tag is in the read range of the reader, but also can give the read rate of each epoch. So BBS make it possible to get a more exact position of the tag.


Behavior-based cleaning for unreliable RFID data sets.

Fan H, Wu Q, Lin Y - Sensors (Basel) (2012)

Analysis in detail.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472823&req=5

f6-sensors-12-10196: Analysis in detail.
Mentions: Furthermore, we analyze the case in one time period in detail (missing rate = 50%, and the length of time is 1,000 epochs). As shown in Figure 6, Reality refers to readings that would have been produced by a perfect reader without missreadings. Raw means the raw data that the reader actually read and while the others refer to the data filled with four kinds of data cleaning methods (BBS, SMURF, 5 epoch sliding-window, and 35 epoch sliding-window). The bold horizontal lines indicate the tag is present/read, and vice versa. The line at the bottom of Figure 6 is the real data of read rate without miss reading, and another line above it is the estimate of read rate by BBS (n = 7, w0 = 1 and w1 = 2). Obviously, compared with sliding window methods, BBS greatly improved the accuracy of RFID data cleaning. In particular, our BBS method not only accurately draws whether the tag is in the read range of the reader, but also can give the read rate of each epoch. So BBS make it possible to get a more exact position of the tag.

Bottom Line: Radio Frequency IDentification (RFID) technology promises to revolutionize the way we track items and assets, but in RFID systems, missreading is a common phenomenon and it poses an enormous challenge to RFID data management, so accurate data cleaning becomes an essential task for the successful deployment of systems.Moreover, a Reverse Order Filling Mechanism is proposed to ensure a more complete access to get the movement behavior characteristics of tag.Finally, we validate our solution with a common RFID application and demonstrate the advantages of our approach through extensive simulations.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, National University of Defense Technology, Changsha 410073, China. huafan@nudt.edu.cn

ABSTRACT
Radio Frequency IDentification (RFID) technology promises to revolutionize the way we track items and assets, but in RFID systems, missreading is a common phenomenon and it poses an enormous challenge to RFID data management, so accurate data cleaning becomes an essential task for the successful deployment of systems. In this paper, we present the design and development of a RFID data cleaning system, the first declarative, behavior-based unreliable RFID data smoothing system. We take advantage of kinematic characteristics of tags to assist in RFID data cleaning. In order to establish the conversion relationship between RFID data and kinematic parameters of the tags, we propose a movement behavior detection model. Moreover, a Reverse Order Filling Mechanism is proposed to ensure a more complete access to get the movement behavior characteristics of tag. Finally, we validate our solution with a common RFID application and demonstrate the advantages of our approach through extensive simulations.

Show MeSH
Related in: MedlinePlus