2010

Since the emergence of Radio Frequency Identification technology (RFID), the community has been promised a cost effective and efficient means of identifying and tracking large sums of items with relative ease. Unfortutely, due to the unreliable ture of the passive architecture, the RFID revolution has been reduced to a fraction of its intended audience due to anomalies such as missed readings. Previous work within this field of study have focused on restoring the data at the recording phase which we believe does not allow enough evidence for consecutive missed readings to be corrected. In this study, we propose a methodology of intelligently imputing missing observations through the use of an \emph{Artificial Neural Network} (ANN) in a static environment. Through experimentation, we discover the most effective algorithm to train the network and establish that the ANN restores a cleaner data set than other intelligent classifier methodologies.

Presented at Conferences

  • The Thirty-Third Australasian Computer Science Conference (2010)

    Brisbane, Australia