Harnessing traffic uncertainties in wireless mesh networks-A stochastic optimization approach

Yang Song; Chi Zhang; Yuguang Fang

doi:10.1007/s11036-008-0137-2

Harnessing traffic uncertainties in wireless mesh networks-A stochastic optimization approach

Yang Song, Chi Zhang, Yuguang Fang

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

7 Citations (Scopus)

Abstract

In this paper, we investigate the routing optimization problem in wireless mesh networks. While existing works usually assume static and known traffic demand, we emphasize that the actual traffic is time-varying and difficult to measure. In light of this, we alternatively pursue a stochastic optimization framework where the expected network utility is maximized. For multi-path routing scenario, we propose a stochastic programming approach which requires no priori knowledge on the probabilistic distribution of the traffic. For the single-path routing counterpart, we develop a learning-based algorithm which provably converges to the global optimum solution asymptotically. © 2009 Springer Science+Business Media, LLC.

Original language	English
Pages (from-to)	124-133
Journal	Mobile Networks and Applications
Volume	14
Issue number	2
DOIs	https://doi.org/10.1007/s11036-008-0137-2
Publication status	Published - Apr 2009
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Network utility maximization
Routing
Wireless mesh networks

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abstract = "In this paper, we investigate the routing optimization problem in wireless mesh networks. While existing works usually assume static and known traffic demand, we emphasize that the actual traffic is time-varying and difficult to measure. In light of this, we alternatively pursue a stochastic optimization framework where the expected network utility is maximized. For multi-path routing scenario, we propose a stochastic programming approach which requires no priori knowledge on the probabilistic distribution of the traffic. For the single-path routing counterpart, we develop a learning-based algorithm which provably converges to the global optimum solution asymptotically. {\textcopyright} 2009 Springer Science+Business Media, LLC.",

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AU - Zhang, Chi

AU - Fang, Yuguang

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N2 - In this paper, we investigate the routing optimization problem in wireless mesh networks. While existing works usually assume static and known traffic demand, we emphasize that the actual traffic is time-varying and difficult to measure. In light of this, we alternatively pursue a stochastic optimization framework where the expected network utility is maximized. For multi-path routing scenario, we propose a stochastic programming approach which requires no priori knowledge on the probabilistic distribution of the traffic. For the single-path routing counterpart, we develop a learning-based algorithm which provably converges to the global optimum solution asymptotically. © 2009 Springer Science+Business Media, LLC.

AB - In this paper, we investigate the routing optimization problem in wireless mesh networks. While existing works usually assume static and known traffic demand, we emphasize that the actual traffic is time-varying and difficult to measure. In light of this, we alternatively pursue a stochastic optimization framework where the expected network utility is maximized. For multi-path routing scenario, we propose a stochastic programming approach which requires no priori knowledge on the probabilistic distribution of the traffic. For the single-path routing counterpart, we develop a learning-based algorithm which provably converges to the global optimum solution asymptotically. © 2009 Springer Science+Business Media, LLC.

KW - Network utility maximization

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DO - 10.1007/s11036-008-0137-2

M3 - RGC 21 - Publication in refereed journal

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EP - 133

JO - Mobile Networks and Applications

JF - Mobile Networks and Applications

IS - 2

ER -

Harnessing traffic uncertainties in wireless mesh networks-A stochastic optimization approach

Abstract

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Research Keywords

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