Guoqiong Liao
Lei Chen
ABSTRACT
Recently, the RFID technology has been widely used in many kinds of applications. However, because of the interference from environmental factors and limitations of the radio frequency technology, the data streams collected by the RFID readers are usually contain a lot of cross-reads. To address this issue, we propose a KerneL dEnsity-bAsed Probability cleaning method (KLEAP) to remove cross-reads within a sliding window. The method estimates the density of each tag using a kernel-based function. The reader corresponding to the micro-cluster with the largest density will be regarded as the position that the tagged object should locate in current window, and the readings derived from other readers will be treated as the cross-reads. Experiments verify the effectiveness and efficiency of the proposed method.
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Digital Library
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- S. R. Jeffery, M. Garofalakis, and M. J. Franklin. Adaptive Cleaning for RFID Data Streams. In Proc. of VLDB (Seoul, Korea, September 12--15, 2006). VLDB'06, 163--174. Google ScholarDigital Library
- J. Xie, J. Yang, Y. Chen, et al. A Sampling-Based Approach to Information Recovery. In Proc. of ICDE (Cancún, México, April 7--12, 2008). ICDE'08, 476--485. Google ScholarDigital Library
- Haiquan Chen and Wei-Shinn Ku and Haixun Wang. Leveraging Spatio-Temporal Redundancy for RFID Data Cleansing. In Proc. of SIGMOD (Indianapolis, Indiana, USA, June 6--11, 2010). SIGMOD'10, 51--62. Google ScholarDigital Library
- Y. Bai, F. Wang, and P. Liu. Efficiently Filtering RFID Data Streams. In Proc. VLDB Workshop on Clean Databases(Seoul, Korea, September 12--15, 2006), 50--57.Google Scholar
Index Terms
- KLEAP: an efficient cleaning method to remove cross-reads in RFID streams
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