ω-LLC: Weighted Low-Energy Localized Clustering for embedded networked sensors

Joongheon Kim; Wonjun Lee; Eunkyo Kim; Choonhwa Lee

doi:10.1007/11540007_152

ω-LLC: Weighted Low-Energy Localized Clustering for embedded networked sensors

Joongheon Kim, Wonjun Lee, Eunkyo Kim, Choonhwa Lee

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

This paper addresses a weighted dynamic localized clustering unique to a hierarchical sensor network structure, while reducing the energy consumption of cluster heads and as a result prolonging the network lifetime. Low-Energy Localized Clustering, our previous work, dynamically regulates the radii of clusters to minimize energy consumption of cluster heads while the network field is being covered. We present weighted Low-Energy Localized Clustering (ω-LLC), which consumes less energy than LLC with weight functions.

Original language	English
Pages (from-to)	1162-1165
Number of pages	4
Journal	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	3614
Issue number	PART II
DOIs	https://doi.org/10.1007/11540007_152
Publication status	Published - 2005
Event	Second International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/11540007_152

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title = "ω-LLC: Weighted Low-Energy Localized Clustering for embedded networked sensors",

abstract = "This paper addresses a weighted dynamic localized clustering unique to a hierarchical sensor network structure, while reducing the energy consumption of cluster heads and as a result prolonging the network lifetime. Low-Energy Localized Clustering, our previous work, dynamically regulates the radii of clusters to minimize energy consumption of cluster heads while the network field is being covered. We present weighted Low-Energy Localized Clustering (ω-LLC), which consumes less energy than LLC with weight functions.",

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AU - Kim, Joongheon

AU - Lee, Wonjun

AU - Kim, Eunkyo

AU - Lee, Choonhwa

PY - 2005

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AB - This paper addresses a weighted dynamic localized clustering unique to a hierarchical sensor network structure, while reducing the energy consumption of cluster heads and as a result prolonging the network lifetime. Low-Energy Localized Clustering, our previous work, dynamically regulates the radii of clusters to minimize energy consumption of cluster heads while the network field is being covered. We present weighted Low-Energy Localized Clustering (ω-LLC), which consumes less energy than LLC with weight functions.

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AN - SCOPUS:26944502713

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ER -

ω-LLC: Weighted Low-Energy Localized Clustering for embedded networked sensors

Abstract

ASJC Scopus subject areas

UN SDGs

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