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

Original languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsL. Wang, Y. Jin
Pages1162-1165
Number of pages4
Volume3614
EditionPART II
Publication statusPublished - 2005
EventSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China
Duration: 2005 Aug 272005 Aug 29

Other

OtherSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005
CountryChina
CityChangsha
Period05/8/2705/8/29

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ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Kim, J., Lee, W., Kim, E., & Lee, C. (2005). ω-LLC: Weighted Low-Energy Localized Clustering for embedded networked sensors. In L. Wang, & Y. Jin (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (PART II ed., Vol. 3614, pp. 1162-1165)