An empirical study on underwater acoustic sensor networks based on localization and routing approaches. (January 2023)

• Record Type: Journal Article
• Authors: Gola, Kamal Kumar; Dhingra, Manish; Gupta, Bhumika; Rathore, Rahul
• Is Part Of: Advances in engineering software. Volume 175(2023)
• Publication Date: 2023-01
• Subjects: Underwater wireless sensor network -- Global positioning system -- Power management -- Localization algorithm -- Machine learning; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103319
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: This review article focuses on the Underwater Wireless Sensor Network (UWSNs) based on localization and routing. The results discussed in this work are solely based on prior published research-based information obtained. Here many of the machine learning techniques are highly focused in UWSNs. This work provides a better understanding of Underwater Wireless Sensor Network; therefore, an effective technique can be compared and achieved in terms of better performance. The research articles are searched and utilized from 2014 to 2021 from different resources. In the localization have several techniques and algorithm with numerous features namely accuracy, capability of resource, and deployment restriction. Abstract: This review article focuses on the Underwater Wireless Sensor Network (U0s) based on localization and routing. Moreover, this review mainly incorporates several research articles based on the date of publication, domains, journals, authors, and methodologies utilized for studies. The results discussed in this work are solely based on prior published research-based information obtained. The research articles are searched and utilized from 2014 to 2021 from different resources. In the localization have several techniques and algorithm with numerous features namely accuracy, capability of resource, and deployment restriction. then, the routing based on the various approaches and algorithm with several parameters like energy consumption, packet delivery ratio, network lifetime, and so on., are reviewed. Here many of the machine learning techniques are highly focused in UWSNs. This work provides a better understanding of Underwater Wireless Sensor Network; therefore, an effective technique can be compared and achieved in terms of better performance.