Optimizing Wireless Sensor Network Installations by Visibility Analysis on 3D Point Clouds

Gracchi Gracchi,Noël Noël,Madiai Madiai,Jaboyedoff Jaboyedoff,Gigli Gigli,Casagli Casagli

doi:10.3390/ijgi8100460

Gracchi Gracchi, Noël Noël + Show 4 more

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https://doi.org/10.3390/ijgi8100460

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Abstract

In this paper, a MATLAB tool for the automatic detection of the best locations to install a wireless sensor network (WSN) is presented. The implemented code works directly on high-resolution 3D point clouds and aims to help in positioning sensors that are part of a network requiring inter-visibility, namely, a clear line of sight (LOS). Indeed, with the development of LiDAR and Structure from Motion technologies, there is an opportunity to directly use 3D point cloud data to perform visibility analyses. By doing so, many disadvantages of traditional modelling and analysis methods can be bypassed. The algorithm points out the optimal deployment of devices following mainly two criteria: inter-visibility (using a modified version of the Hidden Point Removal operator) and inter-distance. Furthermore, an option to prioritize significant areas is provided. The proposed method was first validated on an artificial 3D model, and then on a landslide 3D point cloud acquired from terrestrial laser scanning for the real positioning of an ultrawide-band WSN already installed in 2016. The comparison between collected data and data acquired by the WSN installed following traditional patterns has demonstrated its ability for the optimal deployment of a WSN requiring inter-visibility.

Highlights

A wide variety of techniques aimed at monitoring the surface displacements of unstable slopes exist today
This paper proposes an algorithm called Wireless Sensor network Installation Optimizer (WiSIO) to geometrically find the best locations to install a wireless sensor network (WSN) requiring visibility, i.e., the ith Point (Pi)
The model was designed on AutoCAD 3D (Autodesk, v. 20.0) and transformed into a 3D point cloud with CloudCompare (v. 2.10)

Summary

Introduction

A wide variety of techniques aimed at monitoring the surface displacements of unstable slopes exist today. Devices offering extremely high accuracy and spatial coverage are typically associated with significant costs and a number of logistical constraints For this reason, it is important to refine practices and analysis methodologies related to monitoring techniques characterized by lower costs and higher flexibility, whose employment may be much more sustainable and efficient in certain scenario types. It is important to refine practices and analysis methodologies related to monitoring techniques characterized by lower costs and higher flexibility, whose employment may be much more sustainable and efficient in certain scenario types Some examples of this kind were provided by Dou et al [3], Hemalatha et al [4], Kromer et al [5], and Prabha et al [6]

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