Incorporating Betweenness Centrality in Compressive Sensing for Congestion Detection

TitleIncorporating Betweenness Centrality in Compressive Sensing for Congestion Detection
Publication TypeConference Paper
Year of Publication2013
AuthorsAyatollahi, H. S., H. R. Rabiee, M. H. Rohban, and M. Salehi
Conference Name38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Date Published05/2013
Conference LocationVancouver, Canada
Accession Number13859838
KeywordsCompressive Sensing, Congestion detection, Network Tomography
AbstractThis 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.
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