Managing power conservation in wireless networks

Kongluan Lin; John Debenham; Simeon Simoff

doi:10.1007/978-3-642-17313-4_31

Managing power conservation in wireless networks

Kongluan Lin, John Debenham, Simeon Simoff

School of Computer, Data & Mathematical Sciences

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

1 Citation (Scopus)

Abstract

A major project is investigating methods for conserving power in wireless networks. A component of this project addresses methods for predicting whether the user demand load in each zone of a network is increasing, decreasing or approximately constant. These predictions are then fed into the power regulation system. This paper describes a real-time predictive model of network traffic load which is derived from experiments on real data. This model combines a linear regression based model and a highly reactive model that are applied to real-time data that is aggregated at two levels of granularity. The model gives excellent performance predictions when applied to network traffic load data.

Original language	English
Title of host publication	Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings. Part II
Publisher	Springer
Pages	314-325
Number of pages	10
ISBN (Print)	9783642173134
DOIs	https://doi.org/10.1007/978-3-642-17313-4_31
Publication status	Published - 2010
Event	Advanced Data Mining and Applications - Duration: 1 Jan 2010 → …

Conference

Conference	Advanced Data Mining and Applications
Period	1/01/10 → …

Keywords

data mining
energy conservation
network analysis
wireless communication systems

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10.1007/978-3-642-17313-4_31

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title = "Managing power conservation in wireless networks",

abstract = "A major project is investigating methods for conserving power in wireless networks. A component of this project addresses methods for predicting whether the user demand load in each zone of a network is increasing, decreasing or approximately constant. These predictions are then fed into the power regulation system. This paper describes a real-time predictive model of network traffic load which is derived from experiments on real data. This model combines a linear regression based model and a highly reactive model that are applied to real-time data that is aggregated at two levels of granularity. The model gives excellent performance predictions when applied to network traffic load data.",

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note = "Advanced Data Mining and Applications ; Conference date: 01-01-2010",

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T1 - Managing power conservation in wireless networks

AU - Lin, Kongluan

AU - Debenham, John

AU - Simoff, Simeon

PY - 2010

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AB - A major project is investigating methods for conserving power in wireless networks. A component of this project addresses methods for predicting whether the user demand load in each zone of a network is increasing, decreasing or approximately constant. These predictions are then fed into the power regulation system. This paper describes a real-time predictive model of network traffic load which is derived from experiments on real data. This model combines a linear regression based model and a highly reactive model that are applied to real-time data that is aggregated at two levels of granularity. The model gives excellent performance predictions when applied to network traffic load data.

KW - data mining

KW - energy conservation

KW - network analysis

KW - wireless communication systems

UR - http://handle.uws.edu.au:8081/1959.7/513012

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SN - 9783642173134

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BT - Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings. Part II

PB - Springer

T2 - Advanced Data Mining and Applications

Y2 - 1 January 2010

ER -

Managing power conservation in wireless networks

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