On Directional Bias for Network Coverage

  • Graeme Smith
  • J. W. Sanders
  • Qin Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 472)

Abstract

Random walks have been proposed as a simple method of efficiently searching, or disseminating information throughout, communication and sensor networks. In nature, animals (such as ants) tend to follow correlated random walks, i.e., random walks that are biased towards their current heading. In this paper, we investigate whether or not complementing random walks with directional bias can decrease the expected discovery and coverage times in networks. To do so, we use a macro-level model of a directionally biased random walk based on Markov chains. By focussing on regular, connected networks, the model allows us to efficiently calculate expected coverage times for different network sizes and biases. Our analysis shows that directional bias can significantly reduce the coverage time, but only when the bias is below a certain value which is dependent on the network size.

Keywords

Random walks Markov chains Network coverage 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Graeme Smith
    • 1
  • J. W. Sanders
    • 2
    • 3
  • Qin Li
    • 1
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandAustralia
  2. 2.African Institute for Mathematical ScienceSouth Africa
  3. 3.Department of Mathematical SciencesStellenbosch UniversitySouth Africa

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