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An accurate RSS/AoA-based localization method for internet of underwater things

Pourkabirian, Azadeh; Kooshki, Fereshteh; Anisi, Mohammad Hossein; Jindal, Anish

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Azadeh Pourkabirian

Fereshteh Kooshki

Mohammad Hossein Anisi


Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors.


Pourkabirian, A., Kooshki, F., Anisi, M. H., & Jindal, A. (2023). An accurate RSS/AoA-based localization method for internet of underwater things. Ad Hoc Networks, 145, Article 103177.

Journal Article Type Article
Acceptance Date Apr 6, 2023
Online Publication Date Apr 13, 2023
Publication Date Jun 1, 2023
Deposit Date May 13, 2023
Publicly Available Date May 15, 2023
Journal Ad Hoc Networks
Print ISSN 1570-8705
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 145
Article Number 103177


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