An energy-aware spatio-temporal correlation mechanism to perform efficient data collection in wireless sensor networks

  • Authors:

  • Leandro A. Villas;Azzedine Boukerche;Daniel L. Guidoni;Horacio A. B. F. De Oliveira;Regina Borges De Araujo;Antonio A. F. Loureiro

  • Affiliations:

  • Paradise Research Laboratory - SITE, University of Ottawa, Ottawa, Canada and UWL - Research Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil and WINDIS - Research Laboratory ...;Paradise Research Laboratory - SITE, University of Ottawa, Ottawa, Canada;Department of Computer Science, Federal University of São João del-Rei, São João del-Rei, Brazil;LAMP - Research Laboratory, Federal University of Amazonas, Manaus, Brazil;WINDIS - Research Laboratory, Federal University of São Carlos, São Carlos, Brazil;UWL - Research Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil

  • Venue:
  • Computer Communications
  • Year:
  • 2013

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Abstract

Large scale dense wireless sensor networks (WSNs) will be increasingly deployed in different classes of applications for accurate monitoring. Due to their high density of nodes, it is very likely that information that is both spatially and temporally correlated can be detected by several nodes what can be exploited to save energy, a key aspect on these networks. Furthermore, it is important to take advantage of these correlations to decrease communication and data exchange. However, current proposals usually result in high delays and outdated data arriving at the sink node. In this work, we go further and propose a new algorithm, called Efficient Data Collection Aware of Spatio-Temporal Correlation (EAST), which uses shortest routes for forwarding the gathered data toward the sink node and fully exploit both spatial and temporal correlations to perform near real-time data collection in WSNs. Simulation results clearly indicate that our proposal can sense an event with a high accuracy of more than 99.7% while still saving the residual energy of the nodes in more than 14 times when compared to the accurate data collection strategy reported in the literature.