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Sentry Selection in Wireless Networks

Part of: Equilibrium statistical mechanics Graph theory Geometric probability and stochastic geometry

Published online by Cambridge University Press:  01 July 2016

Paul Balister ,
Béla Bollobás ,
Amites Sarkar  and
Mark Walters
Paul Balister*
Affiliation:
University of Memphis
Béla Bollobás*
Affiliation:
Trinity College Cambridge and University of Memphis
Amites Sarkar*
Affiliation:
Western Washington University
Mark Walters*
Affiliation:
Queen Mary, University of London
*
∗ Postal address: Department of Mathematical Sciences, University of Memphis, Dunn Hall, 3725 Norriswood, Memphis, TN 38152, USA. Email address: pbalistr@memphis.edu
∗∗ Postal address: Trinity College, Cambridge, CB2 1TQ, UK. Email address: b.bollobas@dpmms.cam.ac.uk
∗∗∗ Postal address: Department of Mathematics, Western Washington University, Bellingham, WA 98225, USA. Email address: amites.sarkar@wwu.edu
∗∗∗∗ Postal address: School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London, E1 4NS, UK. Email address: m.walters@qmul.ac.uk
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Abstract

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Let be a Poisson process of intensity one in the infinite plane ℝ2. We surround each point x of by the open disc of radius r centred at x. Now let S n be a fixed disc of area n, and let C r (S n ) be the set of discs which intersect S n . Write E r k for the event that C r (S n ) is a k-cover of S n , and F r k for the event that C r (S n ) may be partitioned into k disjoint single covers of S n . We prove that P(E r k ∖ F r k ) ≤ c k / logn, and that this result is best possible. We also give improved estimates for P(E r k ). Finally, we study the obstructions to k-partitionability in more detail. As part of this study, we prove a classification theorem for (deterministic) covers of ℝ2 with half-planes that cannot be partitioned into two single covers.

Keywords

Poisson processcoveragepartition

MSC classification

Primary: 05C80: Random graphs
Secondary: 60D05: Geometric probability and stochastic geometry 82B43: Percolation

Information

Type
Stochastic Geometry and Statistical Applications
Information
Advances in Applied Probability , Volume 42 , Issue 1 , March 2010 , pp. 1 - 25
Copyright
Copyright © Applied Probability Trust 2010 

Footnotes

Supported by NSF grant CCF-0728928.

Supported by ARO grant W911NF-06-1-0076, and NSF grants CNS-0721983, CCF-0728928, and DMS-0906634.

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