Stochastic binary sensor networks for noisy environments

  • Authors:

  • T. Nguyen;Dong Nguyen;Huaping Liu;Duc A. Tran

  • Affiliations:

  • School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.;School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.;School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.;Department of Computer Science, University of Dayton, Dayton, OH, USA

  • Venue:
  • International Journal of Sensor Networks
  • Year:
  • 2007

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Abstract

This paper proposes a stochastic framework for detecting anomalies or gathering events of interest in a noisy environment using a network consisting of binary sensors. A binary sensor is an extremely coarse sensor, capable of measuring data to only 1-bit accuracy. Our proposed stochastic framework employs a large number of inexpensive binary sensors operating in a noisy environment, yet collaboratively they are able to obtain accurate measurements. The main contributions of this paper are: (a) The theoretical accuracy analysis of the proposed stochastic binary sensor network in noisy environments, (b) an adaptive data collection framework based on the current measurements to reduce the energy consumption and (c) a novel coding scheme for energy-efficient routing. To quantify the performance of our proposed stochastic approach, we present the simulation results of two stochastic binary sensor networks for anomaly detection using our proposed coding scheme and adaptive data gathering framework. We demonstrate that our proposed framework can potentially reduce the energy consumption over the traditional approach by an order of magnitude.