Reflective Network Tomography Based on Compressed Sensing

TL;DR

This paper introduces a novel reflective network tomography method that uses a single transceiver and compressed sensing to identify bottleneck links efficiently without requiring cooperation between separate transmitter and receiver nodes.

Contribution

It proposes a new reflective network tomography approach combined with compressed sensing, eliminating the need for transmitter-receiver cooperation and enabling efficient bottleneck link identification.

Findings

Effective in identifying bottleneck links in simulated networks

Eliminates the need for cooperation between transmitter and receiver

Computationally efficient path construction algorithm

Abstract

Network tomography means to estimate internal link states from end-to-end path measurements. In conventional network tomography, to make packets transmissively penetrate a network, a cooperation between transmitter and receiver nodes is required, which are located at different places in the network. In this paper, we propose a reflective network tomography, which can totally avoid such a cooperation, since a single transceiver node transmits packets and receives them after traversing back from the network. Furthermore, we are interested in identification of a limited number of bottleneck links, so we naturally introduce compressed sensing technique into it. Allowing two kinds of paths such as (fully) loopy path and folded path, we propose a computationally-efficient algorithm for constructing reflective paths for a given network. In the performance evaluation by computer simulation, we confirm the effectiveness of the proposed reflective network tomography scheme.