Toward energy efficient big data gathering in densely distributed sensor networks

Daisuke Takaishi, Hiroki Nishiyama, Nei Kato, Ryu Miura

Research output: Contribution to journalArticlepeer-review

111 Citations (Scopus)

Abstract

Recently, the big data emerged as a hot topic because of the tremendous growth of the information and communication technology. One of the highly anticipated key contributors of the big data in the future networks is the distributed wireless sensor networks (WSNs). Although the data generated by an individual sensor may not appear to be significant, the overall data generated across numerous sensors in the densely distributed WSNs can produce a significant portion of the big data. Energy-efficient big data gathering in the densely distributed sensor networks is, therefore, a challenging research area. One of the most effective solutions to address this challenge is to utilize the sink node's mobility to facilitate the data gathering. While this technique can reduce energy consumption of the sensor nodes, the use of mobile sink presents additional challenges such as determining the sink node's trajectory and cluster formation prior to data collection. In this paper, we propose a new mobile sink routing and data gathering method through network clustering based on modified expectation-maximization technique. In addition, we derive an optimal number of clusters to minimize the energy consumption. The effectiveness of our proposal is verified through numerical results.

Original languageEnglish
Article number6800057
Pages (from-to)388-397
Number of pages10
JournalIEEE Transactions on Emerging Topics in Computing
Volume2
Issue number3
DOIs
Publication statusPublished - 2014 Sep 1

Keywords

  • Big data
  • and energy effciency
  • clustering
  • data gathering
  • optimization
  • wireless sensor networks (WSNs)

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications

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