Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/94309
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Type: Journal article
Title: Efficiently managing uncertain data in RFID sensor networks
Author: Ma, J.
Sheng, Q.
Xie, D.
Chuah, J.
Qin, Y.
Citation: World Wide Web, 2015; 18(4):819-844
Publisher: Springer
Issue Date: 2015
ISSN: 1386-145X
1573-1413
Statement of
Responsibility: 
Jiangang Ma, Quan Z. Sheng, Dong Xie, Jen Min Chuah, Yongrui Qin
Abstract: The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Networked RFID (radio frequency identification), which uses the Internet to connect otherwise isolated RFID systems and software, is an emerging technology to support traceability applications. Despite its promising benefits, there remain many challenges to be overcome before these benefits can be realized. One significant challenge centers around dealing with uncertainty of raw RFID data. In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks. The framework consists of a global object tracking model and a local RFID data cleaning model. In particular, we propose a Markov-based model for tracking objects globally and a particle filter based approach for processing noisy, low-level RFID data locally. Our implementation validates the proposed approach and the experimental results show its effectiveness.
Keywords: RFID; internet of things; uncertainty; traceability networks
Rights: © Springer Science+Business Media New York 2014
RMID: 0030007509
DOI: 10.1007/s11280-014-0283-3
Grant ID: http://purl.org/au-research/grants/arc/DP0878917
http://purl.org/au-research/grants/arc/LP100200114
Appears in Collections:Computer Science publications

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