IEICE TRANSACTIONS on Communications
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Heterogeneous Delay Tomography for Wide-Area Mobile Networks
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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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E102-B No.8 pp.1607-1616
- Publication Date
- 2019/08/01
- Publicized
- 2019/02/06
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2018EBP3056
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Hideaki KINSHO
NTT Corporation
Rie TAGYO
NTT Corporation
Daisuke IKEGAMI
NTT Corporation
Takahiro MATSUDA
Tokyo Metropolitan University
Jun OKAMOTO
NTT Corporation
Tetsuya TAKINE
Osaka University
Keyword
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Hideaki KINSHO, Rie TAGYO, Daisuke IKEGAMI, Takahiro MATSUDA, Jun OKAMOTO, Tetsuya TAKINE, "Heterogeneous Delay Tomography for Wide-Area Mobile Networks" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 8, pp. 1607-1616, August 2019, doi: 10.1587/transcom.2018EBP3056.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3056/_p
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@ARTICLE{e102-b_8_1607,
author={Hideaki KINSHO, Rie TAGYO, Daisuke IKEGAMI, Takahiro MATSUDA, Jun OKAMOTO, Tetsuya TAKINE, },
journal={IEICE TRANSACTIONS on Communications},
title={Heterogeneous Delay Tomography for Wide-Area Mobile Networks},
year={2019},
volume={E102-B},
number={8},
pages={1607-1616},
abstract={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.},
keywords={},
doi={10.1587/transcom.2018EBP3056},
ISSN={1745-1345},
month={August},}
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TY - JOUR
TI - Heterogeneous Delay Tomography for Wide-Area Mobile Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 1607
EP - 1616
AU - Hideaki KINSHO
AU - Rie TAGYO
AU - Daisuke IKEGAMI
AU - Takahiro MATSUDA
AU - Jun OKAMOTO
AU - Tetsuya TAKINE
PY - 2019
DO - 10.1587/transcom.2018EBP3056
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2019
AB - 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.
ER -
English
