Real-Time Tracking of Packet-Pair Dispersion Nodes Using the Kernel-Density and Gaussian-Mixture Models

摘要

A brief simulation study of real-time packet dispersion mode-tracking using the Gaussian-mix model (originally devised for real-time background classification in moving pictures) and an adaptation of the kernel-density estimator is presented. The simulated environment consisted of two FIFO store-and-forward nodes where the probe packets interact with Poisson and Pareto-generated cross-traffic with a range of packet sizes. The two models produced broadly similar results, able to track node activity under the dynamically changing conditions associated with the Pareto cross-traffic. The Gaussian model sometimes replaced the primary mode with a double peak, which disappeared when some of the modelpsilas parameters were changed.