Evaluation of Two RFID Traffic Models with Potential in Anomaly Detection
Document Type
Conference Proceeding
Publication Date
10-1-2018
Abstract
The use of Knuth's Rule and Bayesian Blocks constant piecewise models for characterization of RFID traffic has been proposed already. This study presents an evaluation of the application of those two modeling techniques for various RFID traffic patterns. The data sets used in this study consist of time series of binned RFID command counts. More specifically., we compare the shape of several empirical plots of raw data sets we obtained from experimental RIFD readings., against the constant piecewise graphs produced as an output of the two modeling algorithms. One issue limiting the applicability of modeling techniques to RFID traffic is the fact that there are a large number of various RFID applications available. We consider this phenomenon to present the main motivation for this study. The general expectation is that the RFID traffic traces from different applications would be sequences with different histogram shapes. Therefore., no modeling technique could be considered universal for modeling the traffic from multiple RFID applications., without first evaluating its model performance for various traffic patterns. We postulate that differences in traffic patterns are present if the histograms of two different sets of RFID traces form visually different plot shapes.
Publication Title
Conference Proceedings - IEEE SOUTHEASTCON
Volume
2018-April
Digital Object Identifier (DOI)
10.1109/SECON.2018.8478877
ISSN
07347502
ISBN
9781538661338
Citation Information
A. Alkadi, H. Chi, Z. G. Prodanoff and P. Kreidl, "Evaluation of Two RFID Traffic Models with Potential in Anomaly Detection," SoutheastCon 2018, 2018, pp. 1-5, doi: 10.1109/SECON.2018.8478877.