The Evolution of Intelligent Classifiers into an Integrated Approach to Correct RFID Anomalies

The Evolution of Intelligent Classifiers into an Integrated Approach to Correct RFID Anomalies

Peter Darcy (Institute of Integrated and Intelligent Systems, Griffith University, Australia), Bela Stantic (Institute of Integrated and Intelligent Systems, Griffith University, Australia) and Abdul Sattar (Institute of Integrated and Intelligent Systems, Griffith University, Australia)
Copyright: © 2013 |Pages: 33
DOI: 10.4018/978-1-4666-2080-3.ch003
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Abstract

Radio Frequency Identification (RFID) refers to wireless technology that is used to seamlessly and automatically track various amounts of items around an environment. This technology has the potential to improve the efficiency and effectiveness of tasks such as shopping and inventory saving commercial organisations both time and money. Unfortunately, the wide scale adoption of RFID systems have been hindered due to issues such as false-negative and false-positive anomalies that lower the integrity of captured data. In this chapter, we propose the utilisation three highly intelligent classifiers, specifically a Bayesian Network, Neural Network and Non-Monotonic Reasoning, to handle missing, wrong and duplicate observations. After discovering the potential from using Bayesian Networks, Neural Networks and Non-Monotonic Reasoning to correct captured data, we decided to improve upon the original approach by combining the three methodologies into an integrated classifier. From our experimental evaluation, we have shown the high results obtained from cleaning both false-negative and false-positive anomalies using each of our concepts, and the potential it holds to enhance physical RFID systems.
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Background

RFID systems refer to a system that automatically identifies multiple amounts of tagged objects within a certain proximity to a reader. Although the potential benefits of cheap and durable passive tagging architectures are great, ambiguous anomalies such as missed readings hinder the world-wide adoption of this technology. High level intelligence engines, such as Bayesian Networks, Neural Networks, or Non-Monotonic reasoning, may be harnessed to provide the resolution to highly ambiguous scenarios such as missing data to increase the integrity of the data set.

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