Modeling object flows from distributed and federated RFID data streams for efficient tracking and tracing

Yanbo Wu, Quan Z. Sheng, Hong Shen, Sherali Zeadally

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

In the emerging environment of the Internet of things (IoT), through the connection of billions of radio frequency identification (RFID) tags and sensors to the Internet, applications will generate an unprecedented number of transactions and amount of data that require novel approaches in RFID data stream processing and management. Unfortunately, it is difficult to maintain a distributed model without a shared directory or structured index. In this paper, we propose a fully distributed model for federated RFID data streams. This model combines two techniques, namely, tilted time frame and histogram to represent the patterns of object flows. Our model is efficient in space and can be stored in main memory. The model is built on top of an unstructured P2P overlay. To reduce the overhead of distributed data acquisition, we further propose several algorithms that use a statistically minimum number of network calls to maintain the model. The scalability and efficiency of the proposed model are demonstrated through an extensive set of experiments.

Original languageEnglish
Article number6494565
Pages (from-to)2036-2045
Number of pages10
JournalIEEE Transactions on Parallel and Distributed Systems
Volume24
Issue number10
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • Internet of things
  • object flow pattern
  • Radio frequency identification
  • RFID data streams
  • scalability

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