Radio Frequency Identication (RFID) technology refers to the use of tags with unique identiers being attached to various items which are scanned without a line of sight and then recorded into a database. Current integrations of this technology include baggage tracking at airports, pet owner identication and tagging objects in stores to enforce security by alerting management when an item has left the facility without the tag being deactivated. Despite the wide-scale adoption and advantages of RFID, several issues exist that introduce a level of unreliability resulting in the technology only being used in a fraction of its potential applications. Persistent anomalies that exist in the captured data sets can be classied into either false-positive or false-negative readings. Several methodologies have been presented in the past to correct RFID anomalies, however, due to a lack of intelligence or necessary information, the maximum integrity is not always possible to achieve by currently used techniques. To enhance the overall accuracy of RFID systems, this research proposes a means to correct stored RFID data based on both intelligent analysis and classiers employed at a deferred stage of the data capturing process. We have investigated three classiers due to their impressive performance and novelty to conduct our investigation: a Bayesian Network, Neural Network and Non-Monotonic Reasoning. The main contribution of this thesis involves applying the Bayesian Network [Darcy et al., 2009b], Neural Network [Darcy et al., 2010b] and Non-Monotonic Reasoning [Darcy et al., 2009a, Darcy et al., 2010c] classiers to clean both false-negative and false-positive [Darcy et al., 2011c, Darcy et al., 2012a] anomalies. From our ndings [Darcy et al., 2011a], our proposed methodologies have improved the cleaning accuracy of existing state-of-the-art techniques and have been found to be statistically signicant [Darcy et al., 2007, Darcy et al., 2009c]. We have also proposed further extensions of our approach to be applied to other domains by integrating intrusion detection into our concept [Darcy et al., 2010a], clean common hospital scenario-driven anomalies [Darcy et al., 2010f, Darcy et al., 2010e] then eectively transform low level observations into high level meaningful events [Darcy et al., 2010d, Darcy et al., 2011d], and modied the classiers to include a second level of intelligence to integrate the determination of the three classiers [Darcy et al., 2011b, Darcy et al., 2012b].
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