Black-box optimization of sensor placement with elevation maps and probabilistic sensing models

Akbarzadeh, Vahab; Gagné, Christian; Parizeau, Marc; Mostafavi, Mir Abolfazl

doi:10.1109/rose.2011.6058544

Cited by 19 publications

(25 citation statements)

References 15 publications

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“…In order to tackle these problems, we develop a novel probabilistic coverage function for sensor placement that takes into account the above mentioned issues, and then compare this approach with some classical optimization algorithms. This paper extends our previous work [2,3] by proposing directional and probabilistic sensor models along the pan and tilt sensing angles, and comparing the optimization with other methods, that is simulated annealing and L-BFGS.…”

Section: Introductionmentioning

confidence: 71%

Probabilistic Sensing Model for Sensor Placement Optimization Based on Line-of-Sight Coverage

Akbarzadeh

Gagné

Parizeau

et al. 2013

IEEE Trans. Instrum. Meas.

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111

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This paper proposes a probabilistic sensor model for the optimization of sensor placement. Traditional schemes rely on simple sensor behaviours and environmental factors. The consequences of these oversimplifications are unrealistic simulation of sensor performance and, thus, suboptimal sensor placement. In this paper, we develop a novel probabilistic sensing model for sensors with line-of-sight based coverage (e.g. cameras) to tackle the sensor placement problem for these sensors. The probabilistic sensing model consists of membership functions for sensing range and sensing angle, which takes into consideration sensing capacity probability as well as critical environmental factors such as terrain topography. We then implement several optimization schemes for sensor placement optimization, including simulated annealing, L-BFGS, and CMA-ES.

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Section: Introductionmentioning

confidence: 71%

Probabilistic Sensing Model for Sensor Placement Optimization Based on Line-of-Sight Coverage

Akbarzadeh

Gagné

Parizeau

et al. 2013

IEEE Trans. Instrum. Meas.

Self Cite

111

View full text Add to dashboard Cite

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“…The sensing range of s j is a reference distance r s from which we can pronounce on the coverage of p i by s j in function of their distance d(p i , s j ) [5], [6], [7], [8]. The influence of this factor on C(p i , s j ), modeled by the function [9], [10], [7], where C(p i , s j ) degrades with respect to d(p i , s j ), and it becomes null when the point p i is outside the sensing range of s j ; (iii) Hybrid impact [11], [12], [13], by considering that s j has two sensing ranges, the first is "with certitude", noted r 1 , and the second is "without certitude", noted r 2 , where r 2 > r 1 . Thus, C(p i , s j ) is constant with respect to d(p i , s j ), as long as p i is in the sensing range "with certitude" of s j ; it is null when p i is outside the sensing range "without certitude" of s j , and it degrades with [14], [6], [15], which means that…”

Section: A Impact Of the Sns Sensing Rangementioning

confidence: 99%

“…as long as p i is in the sensing angle of s j . Otherwise, C(p i , s j ) is null; (ii) Probabilistic impact [9], [10], which means that…”

Section: A Impact Of the Sns Sensing Rangementioning

confidence: 99%

See 1 more Smart Citation

On Coverage of 3D Terrains by Wireless Sensor Networks

Zafer

Senouci

Aissani

2019

Annals of Computer Science and Information Systems

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The coverage of a Region of Interest (RoI), that must be satisfied when deploying a Wireless Sensor Network (WSN), depends on several factors related not only to the sensor nodes (SNs) capabilities but also to the RoI topography. This latter has been omitted by most previous deployment approaches, which assume that the RoI is 2D. However, some recent WSNs deployment approaches dropped this unrealistic assumption. This paper surveys the different models adopted by the state-of-theart deployment approaches. The weaknesses that need to be addressed are identified and some proposals expected to enhance the practicality of these models are discussed.

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“…Using the Mahalanobis distance metric with CMA-ES in SCMA-ES algorithm , better niching behavior was observed by Shir et al [13] on multimodal landscapes, The algorithm depends on spatial classification based on ellipsoids rather than Euclidean hyperspheres. In real-life application problems like digital filter design [14] wireless sensor networks [15],design of plannar antenna arrays [16], image reconstruction [17],CMA-ES has found wide applicability.…”

Section: Introductionmentioning

confidence: 99%

Improved CMA-ES with Memory based Directed Individual Generation for Real Parameter Optimization

Kundu

Mukherjee

Debchoudhury

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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Covariance Matrix Adaptation and Evolution Strategy (CMA-ES) is an efficient method of optimization that iteratively generates new individuals around an ever-adaptive recombination point. Although it ensures speed and high rate of exploitation, CMA-ES suffers a major drawback as the scheme of generating new members scattered around an influential mean may often lead to members drawn to local minima. The result is that while precision of better solutions increases, the ability to reform is lost. In this paper we incorporate a directional feature to the generation wise perturbation of individuals in standard version of CMA-ES that utilizes potentially useful information from previous generation to retain the influence of old recombination point. Coupled with a modified population size we attempt to form an algorithm that amalgamates the effectiveness of CMA-ES along with the ability to explore. The performance is tested on IEEE CEC (Congress on Evolutionary Computation) 2013 Special Session on Real-Parameter Optimization in 10, 30 and 50 dimensions. The results obtained clearly indicates that the proposed algorithm addressed as CMA-ES with Memory based Directed Individual Generation (CMA-ES-DIG) is able to perform excessively well on majority of the test cases in a statistically meaningful way.

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Black-box optimization of sensor placement with elevation maps and probabilistic sensing models

Cited by 19 publications

References 15 publications

Probabilistic Sensing Model for Sensor Placement Optimization Based on Line-of-Sight Coverage

Probabilistic Sensing Model for Sensor Placement Optimization Based on Line-of-Sight Coverage

On Coverage of 3D Terrains by Wireless Sensor Networks

Improved CMA-ES with Memory based Directed Individual Generation for Real Parameter Optimization

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