Publication
Loss tomography aims at inferring the loss rate of links in a network from end-to-end measurements. Previous work in [1] has developed optimal maximum likelihood estimators (MLEs) for link loss rates in a single-source multicast tree. However, only sub-optimal algorithms have been developed for multiple-source loss tomography [2]–[5]. In this paper, we revisit multiple-source loss tomography in tree networks with multicast and network coding capabilities, and we provide, for the first time, low-complexity MLEs for the link loss rates. We also derive the rate of convergence of the estimators.
Annalisa Buffa, Denise Grappein, Rafael Vazquez Hernandez, Ondine Gabrielle Chanon
Michel Bierlaire, Timothy Michael Hillel, Janody Pougala