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Sensors 2014, 14(8), 15525-15552; doi:10.3390/s140815525

Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage

Laboratoire de vision et systèmes numériques, Département de génie électrique et de génie informatique, Université Laval, Québec, QC G1V 0A6, Canada
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Received: 20 June 2014 / Revised: 8 August 2014 / Accepted: 14 August 2014 / Published: 21 August 2014
(This article belongs to the Special Issue State-of-the-Art Sensors in Canada 2014)
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Abstract

We are proposing an adaptation of the gradient descent method to optimize the position and orientation of sensors for the sensor placement problem. The novelty of the proposed method lies in the combination of gradient descent optimization with a realistic model, which considers both the topography of the environment and a set of sensors with directional probabilistic sensing. The performance of this approach is compared with two other black box optimization methods over area coverage and processing time. Results show that our proposed method produces competitive results on smaller maps and superior results on larger maps, while requiring much less computation than the other optimization methods to which it has been compared. View Full-Text
Keywords: sensor placement; gradient descent optimization; line-of-sight coverage sensor placement; gradient descent optimization; line-of-sight coverage
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Akbarzadeh, V.; Lévesque, J.-C.; Gagné, C.; Parizeau, M. Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage. Sensors 2014, 14, 15525-15552.

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