An improved SMURF scheme for cleaning RFID data

Abstract

With the increasing usage of Internet of Things' devices, our daily life is facing Big Data. RFID technology enables the reading over a long distance, provides high storage capacity and is widely used in the Internet of Things environmental supply chain management for object tracking and tracing. With the expansion of the RFID technology application areas, the demand for reliability of business data is increasingly important. In order to fulfil the needs of upper applications, data cleaning is essential and directly affects the correctness and completeness of the business data, so it needs to filter and handle RFID data. The traditional statistical smoothing for unreliable RFID data (SMURF) algorithm dynamically adjusts the size of window according to tags' average reading rate of sliding window during the process of data cleaning. To some extent, SMURF overcomes the disadvantages of fixed sliding window size; however, SMURF algorithm is only aimed at constant speed data flow in ideal situations. In this paper, we overcome the shortage of SMURF algorithm, and an improved SMURF scheme in two aspects is proposed. The first one is based on dynamic tags, and the second one is the RFID data cleaning framework, which considers the influence of data redundancy. The experiments verify that the improved scheme is reasonable in dynamic settings of sliding window, and the accuracy of cleaning effect is improved as well.