Measuring the similarity of PML documents with RFID-based sensors

TL;DR

This paper proposes a Bayesian Network-based approach to measure the similarity of PML documents in EPC networks, focusing on reducing redundancy and improving data management for RFID-tagged objects.

Contribution

It introduces a novel similarity measurement method for PML documents using Bayesian Networks, enhancing data filtering and integration in RFID-based EPC systems.

Findings

Effective reduction of redundant data in PML documents

Successful modeling of PML structure with Bayesian Networks

Improved data management in RFID EPC networks

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 de-scribe 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 technol-ogy, such as filtering and integrating these tag data. So in this paper, we propose an approach for meas-uring 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.