Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/97430
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Type: Journal article
Title: A cloud-friendly RFID trajectory clustering algorithm in uncertain environments
Author: Wu, Y.
Shen, H.
Sheng, Q.Z.
Citation: IEEE Transactions on Parallel and Distributed Systems, 2015; 26(8):2075-2088
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Issue Date: 2015
ISSN: 1045-9219
1558-2183
Statement of
Responsibility: 
Yanbo Wu, Hong Shen, and Quan Z. Sheng
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 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.
Keywords: Internet of Things; cloud computing; clustering algorithm; radio frequency identification (RFID); uncertainty
Description: Date of Publication: 13 August 2014
Rights: © 2014 IEEE
RMID: 0030032790
DOI: 10.1109/TPDS.2014.2347286
Appears in Collections:Computer Science publications

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