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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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Related in: MedlinePlus

Accuracy comparison under different missing rates. (a) missing rate = 10%; (b) missing rate = 20%; (c) missing rate = 30%; (d) missing rate = 40%; (e) missing rate = 50%; (f) missing rate = 60%; (g) missing rate = 70%; (h) missing rate = 80%.
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f4-sensors-12-10196: Accuracy comparison under different missing rates. (a) missing rate = 10%; (b) missing rate = 20%; (c) missing rate = 30%; (d) missing rate = 40%; (e) missing rate = 50%; (f) missing rate = 60%; (g) missing rate = 70%; (h) missing rate = 80%.

Mentions: In the experiment, we compare the accuracy of data filled by BBS (with n = 3, n = 7 and n = 11, respectively), SMURF, and sliding-windows methods (with different window size: 5 epoch, 20 epoch and 35 epoch) under different missing rate (from 10% to 80%). The other experimental parameters of BBS are set as follows: m = 7, w0 = 1 and w1 = 2. We clean the same raw data with different methods. Comparing the corresponding cleaning result with real data, we can get the error rate of each method. As shown in Figure 4, the error rate of BBS is lower than that of sliding windows methods in all cases. We found that the choice of the parameter n will have some impact on the experimental results when the missing rate is greater than 70%. Therefore, in practical applications, for optimal cleaning results we should set parameters n, m, w0 and w1 with appropriate values in accordance with the actual needs. Usually, the more unstable the read rate sequence, the larger the value of n should be set; the higher the missing rate, the larger the value of m should be set.


Behavior-based cleaning for unreliable RFID data sets.

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

Accuracy comparison under different missing rates. (a) missing rate = 10%; (b) missing rate = 20%; (c) missing rate = 30%; (d) missing rate = 40%; (e) missing rate = 50%; (f) missing rate = 60%; (g) missing rate = 70%; (h) missing rate = 80%.
© Copyright Policy
Related In: Results  -  Collection

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

f4-sensors-12-10196: Accuracy comparison under different missing rates. (a) missing rate = 10%; (b) missing rate = 20%; (c) missing rate = 30%; (d) missing rate = 40%; (e) missing rate = 50%; (f) missing rate = 60%; (g) missing rate = 70%; (h) missing rate = 80%.
Mentions: In the experiment, we compare the accuracy of data filled by BBS (with n = 3, n = 7 and n = 11, respectively), SMURF, and sliding-windows methods (with different window size: 5 epoch, 20 epoch and 35 epoch) under different missing rate (from 10% to 80%). The other experimental parameters of BBS are set as follows: m = 7, w0 = 1 and w1 = 2. We clean the same raw data with different methods. Comparing the corresponding cleaning result with real data, we can get the error rate of each method. As shown in Figure 4, the error rate of BBS is lower than that of sliding windows methods in all cases. We found that the choice of the parameter n will have some impact on the experimental results when the missing rate is greater than 70%. Therefore, in practical applications, for optimal cleaning results we should set parameters n, m, w0 and w1 with appropriate values in accordance with the actual needs. Usually, the more unstable the read rate sequence, the larger the value of n should be set; the higher the missing rate, the larger the value of m should be set.

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