Journal
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Volume 26, Issue 8, Pages 2075-2088Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2014.2347286
Keywords
Internet of Things; radio frequency identification (RFID); uncertainty; clustering algorithm; cloud computing
Funding
- 985 Project of Sun Yat-Sen University
- National Science Foundation of China [61170232]
- State Key Laboratory of Rail Traffic Control and Safety Research [RS2012K011]
- Australian Research Council (ARC) [LP100200114, DP130104614]
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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.
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