Azadeh Pourkabirian
An accurate RSS/AoA-based localization method for internet of underwater things
Pourkabirian, Azadeh; Kooshki, Fereshteh; Anisi, Mohammad Hossein; Jindal, Anish
Authors
Fereshteh Kooshki
Mohammad Hossein Anisi
Dr Anish Jindal anish.jindal@durham.ac.uk
Associate Professor
Abstract
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.
Citation
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. https://doi.org/10.1016/j.adhoc.2023.103177
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 |
DOI | https://doi.org/10.1016/j.adhoc.2023.103177 |
Public URL | https://durham-repository.worktribe.com/output/1174604 |
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Copyright Statement
© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
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