An adaptive compressive sensing scheme for network tomography based fault localization

Network Tomography Using Routing Probability for Undeterministic Routing

“…The same as our proposal, many network tomography methods using a compressed sensing (CS) approach have been proposed [21]. Bandara et al [22] and Chen et al [23] proposed algorithms to estimate congested or defective links by using CS. Fan et al [24] estimate link delays considering clock synchronization error between origin and destination nodes using a CS approach.…”

“…2, since existing methods that use undetermined route information are difficult to compare with our proposed method due to the difference in the assumptions, we use a conventional method with determined route information. Specifically, we use a CS approach [15], [22], [23] for comparison, which estimates x with CS by using a determined routing matrix, which represents through which link the flow passed, and the element of the matrix corresponding to link through which the flow passes is 1, otherwise the element is 0. Two algorithms are used as the conventional method.…”

“…We exclude links known to be nondegraded from the estimation target links in advance because non-degraded links do not need to be estimated. The operations related to this first part were introduced by Bandara et al [22] and Chen et al [23]. The second part is for estimating x.…”

A Review of Advanced Algebraic Approaches Enabling Network Tomography for Future Network Infrastructures

“…In [57], the authors propose a scalable (the total number of measurements scales logarithmically with the number of links) adaptive CS based tomographic scheme for fault localization. The proposed approach consists of two stages.…”

“…Fragouli et al [27] binary trees Sattari et al [28] m-ary trees, M-by-N DAGs Jithin et al [30] M-by-N DAGs, asynchronous sources Yao et al [31] RLNC, NRSC, passive tomography Mohammad et al [33] RLNC, congested link location Gui et al [34] mesh DAG topologies Gui et al [35] mesh DAG topologies, minimum probe size Shah-Mansouri et al [36] WSN, RLNC, subspace property, virtual sources Sattari et al [37] trees and general topologies Sattari et al [38] trees with multiple sources Fan et al [52] weighted 1 minimization, expander graphs Chen et al [53] link congestion probabilities, greedy iterative algorithm Takemoto et al [54] measurement paths construction, low-quality link detection Morita et al [56] wireless multihop networks, GFT, spatially dependent channels Bandara et al [57] scalable, adaptive fault localization Firooz et al [41] k-identifiability, expander graphs Fattaholmanan et al [43] collaborative distributed framework, individual matrix for every node Wang et al [44] expander graphs, 1 minimization Fan et al [45] synchronization errors, constrained 1 − 2 optimization Nakanishi et al [46] no clock synchronization, reflective NT Nakanishi et al [47] synchronization errors, differential routing matrix Kinsho et al [48] mobile networks, GFT & passive measurements Wei et al [49] dynamic networks, line graph model Table 2. Overview of key points, advantages, and performance of selected tomographic methods.…”