Spatially dependent loss tomography for multihop wireless networks

Morita, Tatsuya; Matsuda, Takahiro; Takine, Tetsuya

doi:10.1109/icoin.2016.7427083

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…A Graph Fourier Transform (GFT) based tomography approach for the estimation of packet loss rates at nodes in wireless multihop networks with spatially dependent channels is presented in [56]. The basic idea is similar to the previously described delay tomography method of [48] (see Section 4.1).…”

Section: Loss Estimationmentioning

confidence: 99%

“…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.…”

Section: Network Compressed Loss Delay Topology Comments Coding Sensingmentioning

confidence: 99%

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

et al. 2020

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Network tomography has emerged as one of the lean approaches for efficient network monitoring, especially aiming at addressing the ever-increasing requirements for scaling and efficiency in modern network architectures and infrastructures. In this paper, we explore network coding and compressed sensing as enabling technologies in the context of network tomography. Both approaches capitalize on algebraic tools for achieving accuracy while allowing scaling of operation as the size of the monitored network increases. Initially, a brief overview of the tomographic problems and the related classification of methods is provided to better comprehend the problems encountered and solutions provided to date. Subsequently, we present representative approaches that employ either one of the aforementioned technologies and we comparatively describe their fundamental operation. Eventually, we provide a qualitative comparison of features and approaches that can be used for further research and technology development for network monitoring in future Internet infrastructures.

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Section: Loss Estimationmentioning

confidence: 99%

Section: Network Compressed Loss Delay Topology Comments Coding Sensingmentioning

confidence: 99%

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

et al. 2020

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“…CS is applied to both loss tomography [6], [7], [10] and delay tomography [4], [8]. Delay tomography schemes with CS aim at estimating average delays, rather than their distributions.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Delay Tomography for Wide-Area Mobile Networks

Hideaki

Tagyo

Ikegami

et al. 2019

IEICE Trans. Commun.

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In this paper, we consider network monitoring techniques to estimate communication qualities in wide-area mobile networks, where an enormous number of heterogeneous components such as base stations, routers, and servers are deployed. We assume that average delays of neighboring base stations are comparable, most of servers have small delays, and delays at core routers are negligible. Under these assumptions, we propose Heterogeneous Delay Tomography (HDT) to estimate the average delay at each network component from end-to-end round trip times (RTTs) between mobile terminals and servers. HDT employs a crowdsourcing approach to collecting RTTs, where voluntary mobile users report their empirical RTTs to a data collection center. From the collected RTTs, HDT estimates average delays at base stations in the Graph Fourier Transform (GFT) domain and average delays at servers, by means of Compressed Sensing (CS). In the crowdsourcing approach, the performance of HDT may be degraded when the voluntary mobile users are unevenly distributed. To resolve this problem, we further extend HDT by considering the number of voluntary mobile users. With simulation experiments, we evaluate the performance of HDT.

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