Reducing RFID Data Uncertainty using Mean Field Variational Inference

In recent years, the RFID applications that are used for tracing and tracking the objects have been increased in many fields due to low cost of RFID tags. However, the raw data produced from RFID tags is redundant and noisy. Therefore, recent approaches apply a preprocessing step to clean raw data from redundant and noisy data. In this paper, a new approach for cleaning RFID data using variational inference technique is proposed. Our approach utilizes the data redundancy and prior knowledge together to improve the data quality. Moreover, it considers the physical constraints of the employed application to improve the data accuracy. Our approach is applied to the three-state RFID detection model. The results prove that our approach efficiently manages the uncertainty of the RFID data in large scale traceability networks.