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Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracyopen access

Authors
Sathish, KaveripakamChinthaginjala, RavikumarKim, WooseongRajesh, AnbazhaganCorchado, Juan M.Abbas, Mohamed
Issue Date
Aug-2023
Publisher
MDPI
Keywords
localization; UWSN; mean estimation error; RSSI; TOA; TDOA
Citation
SENSORS, v.23, no.15
Journal Title
SENSORS
Volume
23
Number
15
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88881
DOI
10.3390/s23156973
ISSN
1424-8220
Abstract
Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object's position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.
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College of IT Convergence (컴퓨터공학부(컴퓨터공학전공))
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