Time-based sampling strategies for multi-channel wireless traffic characterization in tactical cognitive networks

Abstract

Accurate characterization of wireless network traffic has many applications in military communication networks. However, the network traffic characterization is a challenging task in multi-channel wireless networks. Due to the presence of multiple channels, the existing count-based sampling methods demand continuous capture on each channel for selecting the desired packets of interest. Continuous traffic capture makes the cost of monitoring infrastructure very expensive and hence count-based sampling methods are not scalable. However, the time-based sampling methods which were considered inaccurate in wired network characterization, seem to offer a cost-effective and scalable solution. The contributions of this paper include the following: (i) proposal of a new metric, relative proportional inconsistency (RPI) for measuring the accuracy of sampling schemes, (ii) comparison of various time-based sampling strategies using RPI metric, (iii) studying the effect of sampling parameters on the accuracy of sampling, and (iv) preliminary results on characterization of wireless network data traffic from residential and campus environments.