KLEAP: an efficient cleaning method to remove cross-reads in RFID streams

Authors:
Guoqiong Liao;Jing Li;Lei Chen;Changxuan Wan
Affiliations:
Jiangxi University of Finance and Economics, Nanchang, China;Jiangxi University of Finance and Economics, Nanchang, China;Hong Kong University of Science and Technology, Hong Kong, China;Jiangxi University of Finance and Economics, Nanchang, China
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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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.