Title: An improved SMURF scheme for cleaning RFID data

Authors: He Xu; Jie Ding; Peng Li; Daniele Sgandurra; Ruchuan Wang

Addresses: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China ' School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China ' School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China ' School of Mathematics and Information Security, Royal Holloway, University of London, Surrey TW20 0EX, UK ' School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China

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.

Keywords: RFID; data cleaning; Internet of Things; sliding window.

DOI: 10.1504/IJGUC.2018.091723

International Journal of Grid and Utility Computing, 2018 Vol.9 No.2, pp.170 - 178

Received: 24 Oct 2016
Accepted: 27 Mar 2017
Published online: 14 May 2018 *

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