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

Joongheon Kim, Wonjun Lee, Eunkyo Kim, Choonhwa Lee

Research output: Contribution to journalConference article

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
Pages (from-to)1162-1165
Number of pages4
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume3614
Issue numberPART II
DOIs
Publication statusPublished - 2005
EventSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China
Duration: 2005 Aug 272005 Aug 29

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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