Efficiently managing uncertain data in RFID sensor networks

Jiangang Ma*, Quan Z. Sheng, Dong Xie, Jen Min Chuah, Yongrui Qin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)819-844
Number of pages26
JournalWorld Wide Web
Issue number4
Publication statusPublished - 27 Jul 2015
Externally publishedYes


  • Internet of things
  • RFID
  • Traceability networks
  • Uncertainty


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