Dynamic threshold based sliding-window filtering technique for RFID data

Abstract

RFID (radio frequency identification) technology uses radio waves to transfer data between readers and movable tagged objects. In a networked environment of RFID readers, enormous data is generated from the proliferation of RFID readers. In RFID environment, the database becomes more pervasive, therefore, various data quality issues regarding data legacy, data uniformity and data duplication arise. The raw data generated from the readers can't be directly used by the application. Thus, the RFID data repositories must cope with a number of quality issues. These data quality issues include data redundancy, noise removal and synonymy, to name a few. Therefore, data generated in large volume has to be automatically filtered, processed and transformed. In this paper, we have investigated the existing literature on filtering techniques. Finally, we have proposed a dynamic threshold based sliding-window filtering technique for data generated from RFID networked reader. We have presented a scenario where the raw data occurs less than the defined threshold value and noise occurs more than the threshold. In this case, the existing filtering technique recognizes noise as a RFID data and discards the real raw RFID data. Therefore, we have proposed the updation of threshold value periodically and examination of EPC data format and associate values (header information).