Incorporating Betweenness Centrality in Compressive Sensing for Congestion Detection
This paper presents a new Compressive Sensing (CS) scheme for detecting network congested links. The authors focus on decreasing the required number of measurements to detect all congested links in the context of network tomography. They 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 their model outperformed the state-of-the-art CS based method with significant improvements in terms of F-Score.