Title: Measuring the similarity of PML documents with RFID-based sensors

Authors: Zhong-qin Wang; Ning Ye; Reza Malekian; Ting-ting Zhao; Ru-chuan Wang

Addresses: Institute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210003, China ' Institute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210003, China ' Advanced Sensor Networks Research Group, Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa ' Institute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210003, China ' Institute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210003, China

Abstract: The electronic product code (EPC) network is an important part of the internet of things. The physical mark-up language (PML) is to represent and describe data related to objects in EPC network. The PML documents of each component to exchange data in EPC network system are XML documents based on PML Core schema. For managing theses huge amount of PML documents of tags captured by radio frequency identification (RFID) readers, it is inevitable to develop the high-performance technology, such as filtering and integrating these tag data. So in this paper, we propose an approach for measuring the similarity of PML documents based on Bayesian network of several sensors. With respect to the features of PML, while measuring the similarity, we firstly reduce the redundancy data except information of EPC. On the basis of this, the Bayesian network model derived from the structure of the PML documents being compared is constructed.

Keywords: PML documents; physical mark-up language; Bayesian networks; EPC; electronic product code; RFID tags; radio frequency identification; internet of things; IoT; sensor networks; similarity measurement; document structure.

DOI: 10.1504/IJAHUC.2014.065764

International Journal of Ad Hoc and Ubiquitous Computing, 2014 Vol.17 No.2/3, pp.174 - 185

Received: 23 Apr 2013
Accepted: 10 Aug 2013

Published online: 19 Nov 2014 *

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