W-LLC

Weighted low-energy localized clustering for embedded networked sensors

Joongheon Kim, Wonjun Lee, Eunkyo Kim, Choonhwa Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 (w-LLC), which consumes less energy than LLC with weight functions.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages1162-1165
Number of pages4
Volume3614 LNAI
Publication statusPublished - 2006 Oct 2
Event2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsa, China
Duration: 2005 Aug 272005 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3614 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005
CountryChina
CityChangsa
Period05/8/2705/8/29

Fingerprint

Cluster Analysis
Energy utilization
Clustering
Sensor
Energy Consumption
Sensors
Energy
Head
Sensor networks
Hierarchical Networks
Network Lifetime
Network Structure
Weight Function
Sensor Networks
Radius
Minimise
Weights and Measures

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Kim, J., Lee, W., Kim, E., & Lee, C. (2006). W-LLC: Weighted low-energy localized clustering for embedded networked sensors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3614 LNAI, pp. 1162-1165). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3614 LNAI).

W-LLC : Weighted low-energy localized clustering for embedded networked sensors. / Kim, Joongheon; Lee, Wonjun; Kim, Eunkyo; Lee, Choonhwa.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3614 LNAI 2006. p. 1162-1165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3614 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, J, Lee, W, Kim, E & Lee, C 2006, W-LLC: Weighted low-energy localized clustering for embedded networked sensors. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3614 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3614 LNAI, pp. 1162-1165, 2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005, Changsa, China, 05/8/27.
Kim J, Lee W, Kim E, Lee C. W-LLC: Weighted low-energy localized clustering for embedded networked sensors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3614 LNAI. 2006. p. 1162-1165. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Kim, Joongheon ; Lee, Wonjun ; Kim, Eunkyo ; Lee, Choonhwa. / W-LLC : Weighted low-energy localized clustering for embedded networked sensors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3614 LNAI 2006. pp. 1162-1165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{02d827a186004cd3902437279c1f3f24,
title = "W-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 (w-LLC), which consumes less energy than LLC with weight functions.",
author = "Joongheon Kim and Wonjun Lee and Eunkyo Kim and Choonhwa Lee",
year = "2006",
month = "10",
day = "2",
language = "English",
isbn = "9783540283317",
volume = "3614 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "1162--1165",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - W-LLC

T2 - Weighted low-energy localized clustering for embedded networked sensors

AU - Kim, Joongheon

AU - Lee, Wonjun

AU - Kim, Eunkyo

AU - Lee, Choonhwa

PY - 2006/10/2

Y1 - 2006/10/2

N2 - 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 (w-LLC), which consumes less energy than LLC with weight functions.

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 (w-LLC), which consumes less energy than LLC with weight functions.

UR - http://www.scopus.com/inward/record.url?scp=33749022765&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33749022765&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9783540283317

VL - 3614 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1162

EP - 1165

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -