An improved SMURF scheme for cleaning RFID data. (2018)

• Record Type: Journal Article
• Authors: Xu, He; Ding, Jie; Li, Peng; Sgandurra, Daniele; Wang, Ruchuan
• Is Part Of: International journal of grid and utility computing. Volume 9:Number 2(2018)
• Page Start: 170
• Page End: 178
• Publication Date: 2018
• Subjects: RFID -- data cleaning -- Internet of Things -- sliding window; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

Abstract

RFID technology is widely used in the Internet of Things (IoT) environment for object tracking. With the expansion of its application areas, the demand for reliability of business data is increasingly important. In order to fulfil the needs of upper-level 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 is only aimed at constant speed data flow during the process of data cleaning. 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 considers the influence of data redundancy. The experiments verify that the improved scheme is reasonable in dynamic settings of sliding window, and the accuracy is improved as well.