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 int...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.展开更多
基金supported by National Natural Science Foundation of China(61907025,61807020)Scientific Research Foundation of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks(WSNLBZY201512)Jiangsu Government Scholarship for Studying Abroad。
文摘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.
