A Cloud-Friendly RFID Trajectory Clustering Algorithm in Uncertain Environments

Yanbo Wu, Hong Shen, Quan Z. Sheng

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


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 mining useful information from RFID trajectories. RFID data usually contain a considerable degree of uncertainty caused by various factors such as hardware flaws, transmission faults and environment instability. In this paper, we propose an efficient clustering algorithm that is much less sensitive to noise and outliers than the existing methods. To better facilitate the emerging cloud computing resources, our algorithm is designed cloud-friendly so that it can be easily adopted in a cloud environment. The scalability and efficiency of the proposed algorithm are demonstrated through an extensive set of experimental studies.

Original languageEnglish
Article number6877686
Pages (from-to)2075-2088
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number8
Publication statusPublished - 1 Aug 2015
Externally publishedYes


  • cloud computing
  • clustering algorithm
  • Internet of Things
  • radio frequency identification (RFID)
  • uncertainty


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