Probabilistic Inference over RFID Streams in Mobile Environments

Thanh Tran, C. Sutton, R. Cocci, Yanming Nie, Yanlei Diao, P. Shenoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobility, increased noise, and incomplete data. In this paper, we address the problem of translating noisy, incomplete raw streams from mobile RFID readers into clean, precise event streams with location information. Specifically we propose a probabilistic model to capture the mobility of the reader, object dynamics, and noisy readings. Our model can self-calibrate by automatically estimating key parameters from observed data. Based on this model, we employ a sampling-based technique called particle filtering to infer clean, precise information about object locations from raw streams from mobile RFID readers. Since inference based on standard particle filtering is neither scalable nor efficient in our settings, we propose three enhancements-particle factorization, spatial indexing, and belief compression-for scalable inference over large numbers of objects and high-volume streams. Our experiments show that our approach can offer 49% error reduction over a state-of-the-art data cleaning approach such as SMURF while also being scalable and efficient.
Original languageEnglish
Title of host publicationData Engineering, 2009. ICDE '09. IEEE 25th International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1096-1107
Number of pages12
ISBN (Electronic)978-0-7695-3545-6
ISBN (Print)978-1-4244-3422-0
DOIs
Publication statusPublished - 2009

Keywords

  • inference mechanisms
  • mobile computing
  • mobile handsets
  • parameter estimation
  • particle filtering (numerical methods)
  • probability
  • radiofrequency identification
  • RFID streams
  • SMURF
  • belief compression
  • data cleaning
  • mobile RFID readers
  • particle factorization
  • particle filtering
  • probabilistic inference
  • probabilistic model
  • reader mobility
  • sampling-based technique
  • spatial indexing
  • stream processing
  • Information filtering
  • Information filters
  • Large-scale systems
  • Medical services
  • Parameter estimation
  • Pharmaceutical technology
  • Radiofrequency identification
  • Supply chain management
  • Technological innovation
  • Working environment noise

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