Titre :

Review on Unmanned Aerial Vehicle Assisted Sensor Node Localization in Wireless Networks: Soft Computing Approaches

Auteur(s) :

Annepu, Visalakshi [Auteur]
Vellore Institute of Technology [VIT]
Sona, Deepika Rani [Auteur]
Vellore Institute of Technology [VIT]
Ravikumar, Chinthaginjala [Auteur]
Vellore Institute of Technology [VIT]
Bagadi, Kalapraveen [Auteur]
Vellore Institute of Technology [VIT]
Alibakhshikenari, Mohammad [Auteur]
Universidad Carlos III de Madrid [Madrid] [UC3M]
Althuwayb, Ayman [Auteur]
Alali, Bader [Auteur]
Virdee, Bal [Auteur]
London Metropolitan University
Pau, Giovanni [Auteur]
Università degli Studi di Enna " KORE " = Kore University of Enna
Dayoub, Iyad [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
See, Chan Hwang [Auteur]
Edinburgh Napier University
Falcone, Francisco [Auteur]
Universidad Pública de Navarra [Espagne] = Public University of Navarra [UPNA]

Titre de la revue :

IEEE ACCESS

Pagination :

132875-132894

Éditeur :

IEEE

Date de publication :

2022

ISSN :

2169-3536

Mot(s)-clé(s) en anglais :

Extreme learning machine
localization
unmanned aerial vehicles
wireless sensor networks

Discipline(s) HAL :

Informatique [cs]/Réseaux et télécommunications [cs.NI]
Informatique [cs]/Apprentissage [cs.LG]

Résumé en anglais : [en]

Node positioning or localization is a critical requisite for numerous position-based applications of wireless sensor network (WSN). Localization using the unmanned aerial vehicle (UAV) is preferred over localization using ...
Lire la suite >Node positioning or localization is a critical requisite for numerous position-based applications of wireless sensor network (WSN). Localization using the unmanned aerial vehicle (UAV) is preferred over localization using fixed terrestrial anchor node (FTAN) because of low implementation complexity and high accuracy. The conventional multilateration technique estimates the position of the unknown node (UN) based on the distance from the anchor node (AN) to UN that is obtained from the received signal strength (RSS) measurement. However, distortions in the propagation medium may yield incorrect distance measurement and as a result, the accuracy of RSS-multilateration is limited. Though the optimization based localization schemes are considered to be a better alternative, the performance of these schemes is not satisfactory if the distortions are non-linear. In such situations, the neural network (NN) architecture such as extreme learning machine (ELM) can be a better choice as it is a highly non-linear classifier. The ELM is even superior over its counterpart NN classifiers like multilayer perceptron (MLP) and radial basis function (RBF) due to its fast and strong learning ability. Thus, this paper provides a comparative review of various soft computing based localization techniques using both FTAN and aerial ANs for better acceptability.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Projet Européen :

CONcentrating EXcellence in UC3M - Postdoc Programme

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL