<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Underwater Wireless Sensor Networks (UWSNs) are established by Autonomous Underwater Vehicles (AUVs) or static Sensor Nodes (SN) that collect and transmit information over the underwater environment. Localization plays a vital role in the effective deployment, navigation and coordination of these nodes for many applications, namely underwater surveillance, underwater exploration, oceanographic data collection and environmental monitoring. Due to the unique characteristics of underwater transmission and acquisition, this is a fundamental challenge in underwater networks. However, localization in UWSNs is problematic due to the unique features of underwater transmission and the harsh underwater environment. To address these challenges, this paper presents an Improved Grey Wolf Optimization Based Node Localization Approach in UWSN (IGWONL-UWSN) technique. The presented IGWONL-UWSN technique is inspired by the hunting behavior of grey wolves with the Dimension Learning-based Hunting (DLH) search process. The proposed IGWONL-UWSN technique uses the Improved Grey Wolf Optimization Based (IGWO) algorithm to calculate the optimal location of the nodes in the UWSN. Moreover, the IGWONL-UWSN technique incorporates the DLH search process to improve the convergence and accuracy. The simulation results of the IGWONL-UWSN technique are validated using a set of performance measures. The simulation results show the improvements of the IGWONL-UWSN method over other approaches with respect to various metrics.</p>
</abstract>

Underwater Wireless Sensor Networks (UWSNs) are established by Autonomous Underwater Vehicles (AUVs) or static Sensor Nodes (SN) that collect and transmit information over the underwater environment. Localization plays a vital role in the effective deployment, navigation and coordination of these nodes for many applications, namely underwater surveillance, underwater exploration, oceanographic data collection and environmental monitoring. Due to the unique characteristics of underwater transmission and acquisition, this is a fundamental challenge in underwater networks. However, localization in UWSNs is problematic due to the unique features of underwater transmission and the harsh underwater environment. To address these challenges, this paper presents an Improved Grey Wolf Optimization Based Node Localization Approach in UWSN (IGWONL-UWSN) technique. The presented IGWONL-UWSN technique is inspired by the hunting behavior of grey wolves with the Dimension Learning-based Hunting (DLH) search process. The proposed IGWONL-UWSN technique uses the Improved Grey Wolf Optimization Based (IGWO) algorithm to calculate the optimal location of the nodes in the UWSN. Moreover, the IGWONL-UWSN technique incorporates the DLH search process to improve the convergence and accuracy. The simulation results of the IGWONL-UWSN technique are validated using a set of performance measures. The simulation results show the improvements of the IGWONL-UWSN method over other approaches with respect to various metrics.

Improved Grey Wolf Optimization Based Node Localization Approach in Underwater Wireless Sensor Networks

Department of ECE, University College of Engineering (BIT Campus)

Department of ECE, SSM Institute of Engineering & Technology

Department of ECE, Kings College of Engineering

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Underwater Wireless Sensor Networks (UWSNs) are established by Autonomous Underwater Vehicles (AUVs) or static Sensor Nodes (SN) that collect and transmit...

Improved Grey Wolf Optimization Based Node Localization Approach in Underwater Wireless Sensor Networks

Data publikacji: 06 lip 2024

Zakres stron: 95 - 99

Otrzymano: 17 wrz 2023

Przyjęty: 17 kwi 2024

DOI: https://doi.org/10.2478/msr-2024-0013

Słowa kluczowe
Underwater Wireless Sensor Networks (UWSN), localization, sensor nodes, Grey Wolf Optimizer (GWO), localization accuracy

© 2024 WR Salem Jeyaseelan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Improved Grey Wolf Optimization Based Node Localization Approach in Underwater Wireless Sensor Networks

Data publikacji: 06 lip 2024

Zakres stron: 95 - 99

Otrzymano: 17 wrz 2023

Przyjęty: 17 kwi 2024

DOI: https://doi.org/10.2478/msr-2024-0013

Słowa kluczoweUnderwater Wireless Sensor Networks (UWSN), localization, sensor nodes, Grey Wolf Optimizer (GWO), localization accuracy

© 2024 WR Salem Jeyaseelan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Underwater Wireless Sensor Networks (UWSN), localization, sensor nodes, Grey Wolf Optimizer (GWO), localization accuracy