Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation

Seung Jun Baek, Gustavo De Veciana, Xun Su

Research output: Contribution to journalArticle

151 Citations (Scopus)

Abstract

In this paper, we study how to reduce energy consumption in large-scale sensor networks, which systematically sample a spatio-temporal field. We begin by formulating a distributed compression problem subject to aggregation (energy) costs to a single sink. We show that the optimal solution is greedy and based on ordering sensors according to their aggregation costs - typically related to proximity - and, perhaps surprisingly, it is independent of the distribution of data sources. Next, we consider a simplified hierarchical model for a sensor network including multiple sinks, compressors/aggregation nodes, and sensors. Using a reasonable metric for energy cost, we show that the optimal organization of devices is associated with a Johnson-Mehl tessellation induced by their locations. Drawing on techniques from stochastic geometry, we analyze the energy savings that optimal hierarchies provide relative to previously proposed organizations based on proximity, i.e., associated Voronoi tessellations. Our analysis and simulations show that an optimal organization of aggregation/compression can yield 8%-28% energy savings depending on the compression ratio.

Original languageEnglish
Pages (from-to)1130-1140
Number of pages11
JournalIEEE Journal on Selected Areas in Communications
Volume22
Issue number6
DOIs
Publication statusPublished - 2004 Aug

Keywords

  • Data aggregation
  • Distributed data compression
  • Sensor networks
  • Stochastic geometry

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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