A GIS Based Wireless Sensor Network Coverage Estimation and Optimization: A Voronoi Approach

Argany, Meysam; Mostafavi, Mir Abolfazl; Karimipour, Farid; Gagné, Christian

doi:10.1007/978-3-642-25249-5_6

Cited by 40 publications

(37 citation statements)

References 34 publications

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“…The algorithms that use Voronoi diagrams to estimate coverage areas do not take into consideration the features of the propagation environment, and they oversimplify the coverage problem [16]. For this reason, among others, Voronoi diagrams are not being used in this work.…”

Section: Geometrymentioning

confidence: 99%

Spatial Sampling Requirements for Received Signal Level Measurements in Cellular Networks of Suburban Area

Basere¹,

Ivica²

2017

Adv. sci. technol. eng. syst. j.

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Section: Geometrymentioning

confidence: 99%

Spatial Sampling Requirements for Received Signal Level Measurements in Cellular Networks of Suburban Area

Basere¹,

Ivica²

2017

Adv. sci. technol. eng. syst. j.

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“…One example is the art-gallery problem that aims to define the required number of cameras where each point in the gallery is covered by at least one observer [22]. In the art-gallery problem, the coverage is defined by the direct visibility between the observer and the target [23]. Another example related to coverage issues is extending the lifetime of sensors through reducing energy consumption ( [24][25][26]).…”

Section: Coverage Calculation Based On the Line Of Sightmentioning

confidence: 99%

HybVOR: A Voronoi-Based 3D GIS Approach for Camera Surveillance Network Placement

Yaagoubi

Yarmani

Kamel

et al. 2015

IJGI

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Abstract:As a consequence of increasing safety concerns, camera surveillance has been widely adopted as a way to monitor public spaces. One of the major challenges of camera surveillance is to design an optimal method for camera network placement in order to ensure the greater possible coverage. In addition, this method must consider the landscape of the monitored environment to take into account the existing objects that may influence the deployment of such a network. In this paper, a new Voronoi-based 3D GIS oriented approach named "HybVOR" is proposed for surveillance camera network placement. The "HybVOR" approach aims to achieve a coverage near 100% through three main phases. First, a Voronoi Diagram from buildings' footprints is generated and cameras are placed on the Voronoi Edges. Second, the level of coverage is assessed by calculating a viewshed based on a raster Digital Surface Model of the region of interest. Finally, the visibility of the main buildings' entrances is evaluated based on a 3D vector model that contains these features. The effectiveness of the "HybVOR" approach is demonstrated through a case study that corresponds to an area of interest in Jeddah Seaport in the Kingdom of Saudi Arabia.

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“…Several methods have been proposed in the literature to estimate the sensors coverage (Ghosh, 2004;Wang and Cao, 2011). Extending the spatial coverage to 3D environments (such as digital terrain models) has been considered in recent studies (Akbarzadeh et al, 2010;Argany et al, 2011), which use the concepts of visibility, line of sights and viewshed analysis (Figure 1.c). …”

Section: Sensor Spatial Coverage In 2d and 3dmentioning

confidence: 99%

“…Sensors have their module to detect the events occur in the interested environments. It is assumed that each sensor have a sensing range that is limited by environmental conditions (Argany et al, 2011). One of the most important conditions is the existence of physical features in environment such as terrain topography and natural/man-made obstacles, which affect the network coverage and result in holes in the sensing area.…”

Section: Introductionmentioning

confidence: 99%

3d Extention of the Vor Algorithm to Determine and Optimize the Coverage of Geosensor Networks

Doodman

Afghantoloee

Mostafavi

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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KEY WORDS: Geosensor networks deployment, Network coverage problem, Voronoi based optimization algorithm ABSTRACT:Recent advances in electrical, mechanical and communication systems have led to development of efficient low-cost and multifunction geosensor networks. The efficiency of a geosensor network is significantly based on network coverage, which is the result of network deployment. Several optimization methods have been proposed to enhance the deployment efficiency and hence increase the coverage, but most of them considered the problem in the 2D environment models, which is usually far from the real situation. This paper extends a Voronoi-based deployment algorithm to 3D environment, which takes the 3D features into account. The proposed approach is applied on two case studies whose results are evaluated and discussed.

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A GIS Based Wireless Sensor Network Coverage Estimation and Optimization: A Voronoi Approach

Cited by 40 publications

References 34 publications

Spatial Sampling Requirements for Received Signal Level Measurements in Cellular Networks of Suburban Area

Spatial Sampling Requirements for Received Signal Level Measurements in Cellular Networks of Suburban Area

HybVOR: A Voronoi-Based 3D GIS Approach for Camera Surveillance Network Placement

3d Extention of the Vor Algorithm to Determine and Optimize the Coverage of Geosensor Networks

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