Incorporating Betweenness Centrality in Compressive Sensing for Congestion Detection

TL;DR

This paper introduces a novel compressive sensing approach that incorporates betweenness centrality to improve network congestion detection, reducing measurements needed and increasing accuracy compared to existing methods.

Contribution

The paper proposes a new CS scheme that integrates betweenness centrality into the LASSO objective, enhancing congestion detection performance.

Findings

Outperforms state-of-the-art CS methods in F-Score

Requires fewer measurements to detect congested links

Validated on real network datasets

Abstract

This paper presents a new Compressive Sensing (CS) scheme for detecting network congested links. We focus on decreasing the required number of measurements to detect all congested links in the context of network tomography. We have expanded the LASSO objective function by adding a new term corresponding to the prior knowledge based on the relationship between the congested links and the corresponding link Betweenness Centrality (BC). The accuracy of the proposed model is verified by simulations on two real datasets. The results demonstrate that our model outperformed the state-of-the-art CS based method with significant improvements in terms of F-Score.