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More is more: The benefits of denser sensor deployment

More is more: The benefits of denser sensor deployment More Is More: The Benefits of Denser Sensor Deployment ¨ MATTHEW P. JOHNSON, DENIZ SARIOZ, AMOTZ BAR-NOY, and THEODORE BROWN, City University of New York DINESH VERMA and CHAI WAH WU, IBM T. J. Watson Research Center Positioning disk-shaped sensors to optimize certain coverage parameters is a fundamental problem in ad hoc sensor networks. The hexagon lattice arrangement is known to be optimally efficient in the plane, even though 20.9% of the area is unnecessarily covered twice, however, the arrangement is very rigid--any movement of a sensor from its designated grid position (due to, e.g., placement error or obstacle avoidance) leaves some region uncovered, as would the failure of any one sensor. In this article, we consider how to arrange sensors in order to guarantee multiple coverage, that is, k-coverage for some value k > 1. A naive approach is to superimpose multiple hexagon lattices, but for robustness reasons, we may wish to space sensors evenly apart. We present two arrangement methods for k-coverage: (1) optimizing a Riesz energy function in order to evenly distribute nodes, and (2) simply shrinking the hexagon lattice and making it denser. The first method often approximates the second, and so we focus http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Sensor Networks (TOSN) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
1550-4859
DOI
10.1145/2240092.2240096
Publisher site
See Article on Publisher Site

Abstract

More Is More: The Benefits of Denser Sensor Deployment ¨ MATTHEW P. JOHNSON, DENIZ SARIOZ, AMOTZ BAR-NOY, and THEODORE BROWN, City University of New York DINESH VERMA and CHAI WAH WU, IBM T. J. Watson Research Center Positioning disk-shaped sensors to optimize certain coverage parameters is a fundamental problem in ad hoc sensor networks. The hexagon lattice arrangement is known to be optimally efficient in the plane, even though 20.9% of the area is unnecessarily covered twice, however, the arrangement is very rigid--any movement of a sensor from its designated grid position (due to, e.g., placement error or obstacle avoidance) leaves some region uncovered, as would the failure of any one sensor. In this article, we consider how to arrange sensors in order to guarantee multiple coverage, that is, k-coverage for some value k > 1. A naive approach is to superimpose multiple hexagon lattices, but for robustness reasons, we may wish to space sensors evenly apart. We present two arrangement methods for k-coverage: (1) optimizing a Riesz energy function in order to evenly distribute nodes, and (2) simply shrinking the hexagon lattice and making it denser. The first method often approximates the second, and so we focus

Journal

ACM Transactions on Sensor Networks (TOSN)Association for Computing Machinery

Published: Jul 1, 2012

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