Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/17275
Title: Environment aware greedy deployment strategy for underwater acoustic sensor networks using bathymetric mapping and transmission loss modeling
Authors: Tyagi, Shekhar
Shah, Akshat
Srivastava, Abhishek
Keywords: Absorption loss;Bathymetric maps;Sensor deployment;Transmission loss;Underwater acoustic sensor networks
Issue Date: 2025
Publisher: PeerJ Inc.
Citation: Tyagi, S., Shah, A., & Srivastava, A. (2025). Environment aware greedy deployment strategy for underwater acoustic sensor networks using bathymetric mapping and transmission loss modeling. PeerJ Computer Science, 11. https://doi.org/10.7717/peerj-cs.3321
Abstract: Underwater acoustic sensor networks (UASNs) are rapidly evolving and serve a wide array of applications, including marine biology, underwater surveillance, oceanographic data collection, and disaster prevention. Despite notable technological advancements, UASNs continue to face critical challenges, including high propagation delays, limited bandwidth, and significant signal attenuation. To address these issues, this article proposes a novel greedy approach for optimal sensor deployment that aims to maximize coverage, minimize the number of sensors, and effectively account for transmission loss in underwater environments. The methodology begins with the extraction of a region of interest (RoI) using satellite imagery obtained via Google Earth. Following that is the construction of a bathymetry map that captures key topographical features. Recognizing the dynamic and complex nature of underwater environments, multiple simulation scenarios were developed to estimate transmission losses influenced by factors such as salinity, dissolved minerals (e.g., magnesium and other salts), turbidity, pH, and temperature. These simulations help to evaluate how such environmental constraints affect acoustic propagation ranges and coverage estimations. The proposed approach was validated on a real-world RoI and benchmarked against existing methods, demonstrating up to a 10% improvement in coverage while requiring fewer sensors compared to traditional deployment techniques. Additionally, a prototypical real-world implementation was conducted to determine the optimal number of sensors required and the achieved coverage, thereby confirming the practicality and effectiveness of the method. © 2025 Elsevier B.V., All rights reserved.
URI: https://dx.doi.org/10.7717/peerj-cs.3321
https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17275
ISSN: 2376-5992
Type of Material: Journal Article
Appears in Collections:Department of Computer Science and Engineering

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetric Badge: