首页|Cleaning RFID data streams based on K-means clustering method
Cleaning RFID data streams based on K-means clustering method
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NETL
NSTL
万方数据
维普
Currentlyradio frequency identification (RFID) technology has been widely used in many kinds of applications.Store retailers use RFID readers with multiple antennas to monitor all tagged items.However,because of the interference from environment and limitations of the radio frequency technology,RFID tags are identified by more than one RFID antenna,leading to the false positive readings.To address this issue,we propose a RFID data stream cleaning method based on K-means to remove those false positive readings within sampling time.First,we formulate a new data stream model which adapts to our cleaning algorithm.Then we present the preprocessing method of the data stream model,including sliding window setting,feature extraction of data stream and normalization.Next,we introduce a novel way using K-means clustering algorithm to clean false positive readings.Last,the effectiveness and efficiency of the proposed method are verified by experiments.It achieves a good balance between performance and price.
false positive readingdata streamK-meansRFID
Lin Qiaomin、Fa Anqi、Pan Min、Xie Qiang、Du Kun、Sheng Michael
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College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
College of Education Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Department of Computing, Macquarie University, Sydney 2109, Australia
College of Overseas Education, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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This work was supported by National Natural Science Foundation of ChinaThis work was supported by National Natural Science Foundation of ChinaScientific Research Foundation of Jiangsu High Technology Research Key Laboratory for Wireless Sensor NetworksJiangsu Government Scholarship for Studying Abroad.
NETL
NSTL
万方数据
维普
