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Incorporating Betweenness Centrality in Compressive Sensing for Congestion Detection
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