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Predictive Modeling-Based Data Collection in Wireless Sensor Networks

  • Lidan Wang
  • Amol Deshpande
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4913)

Abstract

We address the problem of designing practical, energy-efficient protocols for data collection in wireless sensor networks using predictive modeling. Prior work has suggested several approaches to capture and exploit the rich spatio-temporal correlations prevalent in WSNs during data collection. Although shown to be effective in reducing the data collection cost, those approaches use simplistic corelation models and further, ignore many idiosyncrasies of WSNs, in particular the broadcast nature of communication. Our proposed approach is based on approximating the joint probability distribution over the sensors using undirected graphical models, ideally suited to exploit both the spatial correlations and the broadcast nature of communication. We present algorithms for optimally using such a model for data collection under different communication models, and for identifying an appropriate model to use for a given sensor network. Experiments over synthetic and real-world datasets show that our approach significantly reduces the data collection cost.

Keywords

Sensor Network Sensor Node Wireless Sensor Network Communication Cost Junction Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Lidan Wang
    • 1
  • Amol Deshpande
    • 1
  1. 1.Computer Science DepartmentUniversity of MarylandUSA

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