On the Coverage Process of a Moving Target in a Dynamic Nonstationary Sensor Field

Pallavi Manohar; D. Manjunath

4th Workshop on Spatial Stochastic Models for Wireless Networks

Research Article

On the Coverage Process of a Moving Target in a Dynamic Nonstationary Sensor Field

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Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/ICST.WIOPT2008.3139,
    author={Pallavi Manohar and D. Manjunath},
    title={On the Coverage Process of a Moving Target in a Dynamic Nonstationary Sensor Field},
    proceedings={4th Workshop on Spatial Stochastic Models for Wireless Networks},
    publisher={IEEE},
    proceedings_a={SPASWIN},
    year={2008},
    month={8},
    keywords={Computational geometry Fading Mobile communication Performance analysis Scheduling algorithm Sensor phenomena and characterization Statistical distributions Stochastic processes Target tracking Wireless sensor networks},
    doi={10.4108/ICST.WIOPT2008.3139}
}

Pallavi Manohar
D. Manjunath
Year: 2008
On the Coverage Process of a Moving Target in a Dynamic Nonstationary Sensor Field
SPASWIN
IEEE
DOI: 10.4108/ICST.WIOPT2008.3139

Pallavi Manohar¹^,*, D. Manjunath¹^,*

1: Department of Electrical Engg., IIT Bombay, Mumbai, INDIA.

*Contact email: pallavim@ee.iitb.ac.in, dmanju@ee.iitb.ac.in

Abstract

We analyze the statistical properties of the k- coverage of a point-target moving in a straight line in a dynamic, nonstationary sensor field. The availability of each node is modeled by an independent, f0; 1g-valued continuous time Markov chain. Sensor locations form a nonhomogeneous spatial Poisson process. The sensing areas of the sensors are circles of i.i.d. radii. We first describe the induced nonstationary Markov- Boolean model and obtain k-coverage of the target at an arbitrary time instant. We then obtain k-coverage statistics for the time interval [0; T]: A pointwise stationary approximation that yields a limit theorem is also discussed. Numerical results illustrate the analysis.

Keywords: Computational geometry Fading Mobile communication Performance analysis Scheduling algorithm Sensor phenomena and characterization Statistical distributions Stochastic processes Target tracking Wireless sensor networks

Published: 2008-08-01
Publisher: IEEE
Modified: 2010-05-16

: http://dx.doi.org/10.4108/ICST.WIOPT2008.3139