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Incorporating betweenness centrality in compressive sensing for congestion detection

Ayatollahi Tabatabaii, H. S ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP.2013.6638515
  3. Publisher: 2013
  4. Abstract:
  5. 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
  6. Keywords:
  7. Compressive sensing ; Network tomography ; Prior knowledge ; Betweenness centrality ; Congested links ; Congestion detection ; Objective functions ; Real data sets ; Computer simulation ; Knowledge based systems ; Signal reconstruction
  8. Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; 2013 , Pages 4519-4523 ; 15206149 (ISSN); 9781479903566 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6638515