A Novel Sensor Deployment Method Based on Image Processing and Wavelet Transform to Optimize the Surface Coverage in WSNs

Yang, Hang; Li, Xunbo; Wang, Zhenlin; Yu, Wenjie; Huang, Bo

doi:10.1049/cje.2016.05.015

Cited by 18 publications

(15 citation statements)

References 19 publications

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“…The comparison algorithms in the experiment are GWO, DEA (differential evolution algorithm) and ABC-DSS (artificial bee colony algorithm with dynamic search strategy), and their information is shown in Table 12. The parameters of the comparison algorithms are consistent with those in the literature [28], [29], [31]. It should be noted that in DEA, mutation factor F = 0.9 and cross-factor CR = 0.1.…”

Section: B Experiments 2: 3d Sensor Simulation Experiments and Analysissupporting

confidence: 78%

“…However, the situation of blind spots is not considered in this article, and the deployment method is slow to deploy. Yang et al [28] proposed an ABC-DSS (artificial bee colony algorithm with dynamic search strategy) to deploy 3D surfaces with limited sensor nodes. This algorithm matches the sensor deployment problems on 3D surface well.…”

Section: Related Workmentioning

confidence: 99%

“…At the same time, a method is proposed to calculate the coverage area by combining the grid and the surface integral. 5) In order to verify the effectiveness of EGWO deploying sensor networks on 3D surfaces, our work will also be compared with two existing 3D deployment methods, i.e., ABC-DSS (artificial bee colony algorithm with dynamic search strategy) in [28] and DEA (differential evolution algorithm) in [29].…”

Section: Introductionmentioning

confidence: 99%

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Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

Wang

Xie

2020

IEEE Access

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Aiming at the difficulty of deploying wireless sensor networks (WSNs) on three-dimensional (3D) surfaces, based on the grey wolf optimizer (GWO), an enhanced version of the grey wolf optimizer is proposed for deploying WSNs on 3D surfaces, namely the enhanced grey wolf optimizer (EGWO), which is characterized by enhanced exploitation and exploration ability of the algorithm. The novelty of EGWO is that the grey wolf population is divided into two parts, one part is responsible for the outer-layer encircle and the other is responsible for the inner-layer encircle, and the introduction of Tent mapping. The purpose of this is to enhance the exploitation and exploration ability of the algorithm respectively, so as to improve the convergence and optimization precision of the algorithm. In addition, in terms of WSN deployment in 3D surfaces, this paper improves the means of determining the perceived blind zone. Meanwhile, a novel method to calculate the WSNs coverage area of simple and complex 3D surfaces is presented by combining the grid and integral of the 3D surfaces. The EGWO is favorably compared with the GWO and three existing variants of the grey wolf optimizer when testing on 12 well-known benchmark functions. The simulation experiment results show that compared with the existing algorithms, EGWO can provide a very competitive search result in terms of optimization precision and convergence performance. Finally, this paper applies EGWO to the 3D surface deployment of WSN. Simulations show that compared with the other three deployment algorithms, EGWO can improve the network coverage of WSN, which can save network deployment costs. In addition, the probability of network connectivity deployed by EGWO is higher, that is, EGWO can provide a better deployment solution. INDEX TERMS Wireless sensor network, three-dimensional surface deployment, perceived blind zone, grid and integration, optimization, grey wolf optimizer, inner-layer encircle.

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Section: B Experiments 2: 3d Sensor Simulation Experiments and Analysissupporting

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

Wang

Xie

2020

IEEE Access

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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%

“…[26], [20], [18], [19], [4], [ be discontinuous, such as the matrix model or continuous, such as the mathematical and the TIN (Triangulated Irregular Network) models. In the matrix model, Cov(A, N ) represents the rate of points of E covered by the N SNs [11], [12], [15], [18], considering that these points have the same level [11], [12], [13], [15], [18] or different levels [10] of importance. Thus, Cov(A, N ) is given by Eq.…”

Section: Roi Coveragementioning

confidence: 99%

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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Terrain Partitioning Based Approach for Realistic Deployment of Wireless Sensor Networks

Zafer

Senouci

Aissani

2018

Computational Intelligence and Its Applications

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This paper addresses the NP-hard problem of deploying wireless sensor networks on 3D terrains. On the contrary to previous works that place the sensors without any analysis of the terrain, we propose a two-phase solution based on terrain partitioning. The main idea is to estimate the number of sensors to be used and simplify the sensors deployment by partitioning the terrain according to topographic criteria. Simulation results based on real-world terrains confirm the efficiency of our solution in terms of coverage quality.

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A Novel Sensor Deployment Method Based on Image Processing and Wavelet Transform to Optimize the Surface Coverage in WSNs

Cited by 18 publications

References 19 publications

Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

On Coverage of 3D Terrains by Wireless Sensor Networks

Terrain Partitioning Based Approach for Realistic Deployment of Wireless Sensor Networks

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