# False Positive RFID Detection Using Classification Models
## 2 Related Work
### False Positive Detection
However, this approach requires installing additional reader(s) and antenna(s), hence significantly increasing hardware costs. Another popular method to detect false positive RFID is by analyzing tag RSS. Several previous studies have considered RSS information for WMSs. [PERSON] et al. (2010) investigated static and moved tags characteristics from low level reader data, including RSS [20]. They investigated the optimal threshold derived from the required information gain to separate static (false positive) and moved (true positive) tags. A classifier was developed based on the single RSS feature, with accuracy performance as high as 95.69%. Similar studies have utilized multiple feature classifiers to detect false positives. [PERSON] et al. (2015) considered RSS, SinceStart, and Antenna information [21]. RSS attributes provide the distance between tag and antenna, while SinceStart considers the timeframes between the beginning of gathering cycle and time the tag was read. Antenna information attributes reveal the information of antenna that read tags during gathering cycle. They employed single (decision stump) and multiple feature classifiers (i.e., logistic regression (LR), multilayer perceptron (MLP), and decision tree (DT)) to distinguish between static and moved tags. In addition, the study also proposed a rule-based classifier to improve classification accuracy. [PERSON] et al. (2018) recently considered false positive detection based on RSS [22]. They utilized several machine learning algorithms (DT, support vector machines (SVMs), and LR) to classify RFID readings, using features related to RSS and phase shift. The SVM based approach outperformed all other models, providing the highest accuracy (up to 95.5%). The phase and doppler are the other methods that can be utilized to identify the moving tags. Phase shift is generated when the wave travels from the reader antenna to the tag antenna given distance, while the doppler is retrieved when there is a relative motion between reader and a tag. [PERSON] and [PERSON] (2014) utilized classification model to differentiate between the moving tags on the conveyor and static tags [42]. The phase variation history from moving and static tags are gathered and finally the peak position of tags at reference time can be retrieved. The K-means is utilized as a classifier with input features such as matching function peak and actual speed of the tags. The result showed that the proposed model successfully discriminates moving and static tags with an overall accuracy greater than 96%. Furthermore, the improved model has been presented by considering nomad tags (randomly moving tags) and static tags as false positive readings [43]. The peak position and cross-correlation coefficient are used as input features. The rule-based model has been introduced and by employing a wide beam reader antenna it showed high accuracy, as much as 99.9%. Finally, the doppler-based method is utilized for item sorting and indoor localization system (ILS). [PERSON] et al. (2018) proposed a sorting algorithm for moving tags on the conveyor belt [44]. The proposed study utilized RSSI, phase, and doppler frequency of moving tags. The result showed that by integrating the RSSI and the doppler method together, the proposed model performs better in the actual conveyor belt. [PERSON] et al. (2015) proposed RFID indoor localization based on the doppler effect to determine the position of the reader antenna [45]. Two scenarios are considered for moving tags, slow speed and fast speed tags. The slow speed tags generate small variation, while fast speed generates larger variation in doppler frequency. The result showed that the proposed model generated a low mean absolute error (MAE) as compared to other models. Previous studies [20; 21; 22] classified static tags as false positives, with moved tags being considered true positive readings. Static tags occurred when the tags were located within the configured read range, whereas moved tags are tags that were moved through the reader gate.