Next Article in Journal
Application of Service Oriented Architecture for Sensors and Actuators in District Heating Substations
Next Article in Special Issue
A Low-Rank Matrix Recovery Approach for Energy Efficient EEG Acquisition for a Wireless Body Area Network
Previous Article in Journal
Lab-on-Chip Cytometry Based on Magnetoresistive Sensors for Bacteria Detection in Milk
Previous Article in Special Issue
Stability Analysis for a Multi-Camera Photogrammetric System
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(8), 15525-15552;

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
Author to whom correspondence should be addressed.
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)
Full-Text   |   PDF [3886 KB, uploaded 21 August 2014]


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
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top