## 2 Literature Review  
### Tag Movement and Direction  
RFID technology can be applied in a warehouse to identify items with RFID tags within the reader's range. While RFID readers can be used to track products as they are loaded onto trucks for transportation to other supply chain partners, the reader cannot differentiate between tags that are intentionally moving through the gate and those that happen to be in the reading range by chance. Several techniques have been suggested to tackle this problem, including using RSS and timestamps. Previous studies have examined the use of RSS information to detect false positive tags. [PERSON] et al. (2010) looked at the characteristics of both static and moved tags from low-level reader data, including RSS. They explored the optimal threshold for separating static (false positive) and moved (true positive) tags. They developed a classifier based on a single RSS feature and achieved an accuracy of 95.69%. [PERSON] et al. (2011) recently addressed false positive detection based on RSS. They used 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, achieving the highest accuracy (up to 95.5%). [PERSON] et al. (2012) propose an algorithm to address the problem of false positive readings from supply chain radio frequency identification (RFID) systems. They extend the scenario to more complex access control systems, where detecting false-positive and false-negative data is critical to improving security and user satisfaction. The RFID data was divided into 70% training and 30% test data. Four training methods were used: ASOINN, SVM, LR, and DT, and the results show that all methods performed well. To improve prediction accuracy, [PERSON] et al. (2013) proposed false positive detection based on RF and Inter Quartile Range (IQR) outlier detection. By removing the outlier, the prediction accuracy can be improved. The result shows that the integrated model successfully detects moving tags up to 97.496. Furthermore, [PERSON] et al. (2014) proposed a model to classify the tags that move statically and pass-through portals. Sample data collected from an RFID reader and an array antenna are classified using SVM and LSTM. The results showed that SVM has an accuracy of more than 99%, while LSTM has an accuracy of around 95%. This result can be applied to RFID readers at the entrance of fashion stores as anti-theft systems. Previous studies on false positive detection have not fully evaluated various types of tag movement that may occur in real-world scenarios. To improve the efficiency of RFID gates, it is necessary to use machine-learning models to identify tag direction while taking into account different types of tag movement. [PERSON] et al. (2015) presented a classification method for monitoring forklifts in Industry 4.0 scenarios. The proposed method uses machine learning techniques to analyze sensor data from forklifts and accurately classify their actions, such as moving, loading, and unloading. The implementation uses a UHF-RFID Smart Gate with a single reader antenna and asymmetrical deployment, thus allowing the correct action classification with reduced infrastructure complexity and cost. The results show that the proposed method improves efficiency and safety in warehouse operations by enabling real-time monitoring and intervention when necessary. In addition, movement and direction detection for objects attached with RFID tags using machine learning was also presented in ([PERSON] et al., 2016). The detection of the position and orientation of moving objects was modeled as a classification problem and solved using a Light Gradient Boosting Machine. The proposed method was verified to have high accuracy in detecting the position and orientation of moving target boxes on a conveyor belt. Further experiments were conducted utilizing the robot to grip the moving object with a high success rate as the results. [PERSON] et al.