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

Annepu, Visalakshi; Sona, Deepika Rani; Ravikumar, C.V.; Bagadi, Kalapraveen; Alibakhshikenari, Mohammad; Althuwayb, Ayman A.; Alali, Bader; Virdee, Bal S.; Pau, Giovanni; Dayoub, Iyad; See, Chan Hwang; Falcone, Francisco

Authors

Visalakshi Annepu

Deepika Rani Sona

C.V. Ravikumar

Kalapraveen Bagadi

Mohammad Alibakhshikenari

Ayman A. Althuwayb

Bader Alali

Bal S. Virdee

Giovanni Pau

Iyad Dayoub

Francisco Falcone



Abstract

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-linearclassifier. 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.

Journal Article Type Article
Acceptance Date Dec 14, 2022
Online Publication Date Dec 19, 2022
Publication Date 2022
Deposit Date Dec 14, 2022
Publicly Available Date Dec 15, 2022
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 10
Pages 132875-132894
DOI https://doi.org/10.1109/access.2022.3230661
Keywords Extreme learning machine, localization, unmanned aerial vehicles, wireless sensor networks
Public URL http://researchrepository.napier.ac.uk/Output/2983289

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