TY - JOUR
T1 - Efficient AUV-Aided Localization for Large-Scale Underwater Acoustic Sensor Networks
AU - Wang, Yiran
AU - Song, Shanshan
AU - Liu, Jun
AU - Guo, Xiaoxin
AU - Cui, Junhong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2024
Y1 - 2024
N2 - Localization is a vital service in underwater acoustic sensor networks (UASNs). Autonomous underwater vehicles (AUVs), with their mobility and collaborations can provide accurate, extensive, and efficient localization service for large-scale UASNs. During localization, AUVs travel along the predefined paths and broadcast reference messages to aid sensor nodes in estimating locations. However, AUVs-aided localization faces the following two challenges: 1) complex localization path planning for multiple AUVs, which requires consideration of localization accuracy and optimization of travel path simultaneously and 2) harsh underwater localization conditions, such as unsynchronized clocks and stratification effects seriously affect the localization accuracy. To this end, an efficient AUVs-aided localization scheme (EAL) is proposed for large-scale UASNs, which jointly addresses the path planning and localization in an unified framework. Specifically, we propose a graph-based localization path planning mechanism, which considers the impact of path on localization and determines effective travel paths for AUVs. Furthermore, we design an iteration-based asynchronous localization mechanism, which could compensate the stratification effect and achieve accurate localization for the sensor nodes. Extensive simulation results show that the EAL can achieve efficient and high accuracy localization for the sensor nodes with the aid of multiple AUVs.
AB - Localization is a vital service in underwater acoustic sensor networks (UASNs). Autonomous underwater vehicles (AUVs), with their mobility and collaborations can provide accurate, extensive, and efficient localization service for large-scale UASNs. During localization, AUVs travel along the predefined paths and broadcast reference messages to aid sensor nodes in estimating locations. However, AUVs-aided localization faces the following two challenges: 1) complex localization path planning for multiple AUVs, which requires consideration of localization accuracy and optimization of travel path simultaneously and 2) harsh underwater localization conditions, such as unsynchronized clocks and stratification effects seriously affect the localization accuracy. To this end, an efficient AUVs-aided localization scheme (EAL) is proposed for large-scale UASNs, which jointly addresses the path planning and localization in an unified framework. Specifically, we propose a graph-based localization path planning mechanism, which considers the impact of path on localization and determines effective travel paths for AUVs. Furthermore, we design an iteration-based asynchronous localization mechanism, which could compensate the stratification effect and achieve accurate localization for the sensor nodes. Extensive simulation results show that the EAL can achieve efficient and high accuracy localization for the sensor nodes with the aid of multiple AUVs.
KW - Autonomous underwater vehicles (AUVs)
KW - path planning
KW - underwater acoustic sensor networks (UASNs)
KW - underwater localization
UR - https://www.scopus.com/pages/publications/85197553278
U2 - 10.1109/JIOT.2024.3420448
DO - 10.1109/JIOT.2024.3420448
M3 - 文章
AN - SCOPUS:85197553278
SN - 2327-4662
VL - 11
SP - 31776
EP - 31790
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 19
ER -