Name
Abstract | ||
|---|---|---|
Recently, the radio frequency identification (RFID) technology has been widely used in many kinds of applications. However, RFID data streams contain false negative reads and false positive reads leading to the location uncertainty of RFID tags. In view of these problems, we propose a method of cleaning RFID data streams based on Naive Bayes classifier, which could detect effectively tags of false negative reads and false positive reads in RFID data streams. Firstly, we construct a model of a RFID data stream. Then we divide the method into three phases, i.e., preparation phase, training classifier phase and application phase. At last, the result of experiments illustrates our method based on Naive Bayes classifier could acquire the lower percentage of false negative reads and the higher percentage of false positive reads than SMURF algorithm with the increase of the size of sliding window. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1504/IJAHUC.2016.076359 | INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING |
Keywords | Field | DocType |
data stream cleaning, Naive Bayes classifier, false negative reads, false positive reads, RFID, radio frequency identification | Data mining,Data stream mining,Sliding window protocol,Naive Bayes classifier,Pattern recognition,Data stream,Computer science,Artificial intelligence,Classifier (linguistics),Radio-frequency identification | Journal |
Volume | Issue | ISSN |
21 | 4 | 1743-8225 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiaomin Lin | 1 | 2 | 1.43 |
| Yan Xiao | 2 | 0 | 0.34 |
| Ning Ye | 3 | 64 | 7.52 |
| Ruchuan Wang | 4 | 414 | 64.49 |