Optimal Reference Nodes Deployment for Positioning Seafloor Anchor Nodes in the Internet of Underwater Things

Seafloor anchor nodes are a crucial component of internet of underwater things (IoUT), which is designed to provide surface and underwater users with positioning, navigation, timing and communication (PNTC) services. Traditional anchor node positioning typically uses cross or circular shaped deployment. However, accurate positioning of underwater anchor nodes becomes a challenge task due to the non-uniform distribution of underwater sound speed, which is seldom considered in the literature. Due to the complexity of distance measurement caused by curved sound lines, the optimization of reference node deployment based on curved sound lines is worthy of further research. This paper focuses on the optimal reference node deployment strategies for time-of-arrival (TOA) localization of anchor nodes in IoUT networks in the three-dimensional (3D) underwater space. We adopt the criterion that minimizing the trace of the inverse Fisher information matrix (FIM) to facilitate the optimal reference nodes deployment with Gaussian measurement noise, the magnitude of which is positive related to the real signal propagation path. It is proved that the optimal reference node deployment position exhibits central symmetry with respect to the target, which greatly simplifies the analysis of the optimal node deployment position. Then, a semi-closed form of the optimal pitch angle is derived to determine the optimal geometries. To demonstrate the findings in this paper, we conducted both simulations and real-world experiments on underwater anchor node positioning. Both the simulation and experiment results demonstrate that the proposed deployment scheme significantly improve the positioning accuracy.

Publication Details

Journal
UCL Discovery (University College London)
Published
2024-12-31
Primary Topic
Thermal Analysis in Power Transmission
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article

Optimal Reference Nodes Deployment for Positioning Seafloor Anchor Nodes in the Internet of Underwater Things

UCL Discovery (University College London)
Thermal Analysis in Power Transmission
article

Optimal Reference Nodes Deployment for Positioning Seafloor Anchor Nodes in the Internet of Underwater Things

article en

Abstract

Seafloor anchor nodes are a crucial component of internet of underwater things (IoUT), which is designed to provide surface and underwater users with positioning, navigation, timing and communication (PNTC) services. Traditional anchor node positioning typically uses cross or circular shaped deployment. However, accurate positioning of underwater anchor nodes becomes a challenge task due to the non-uniform distribution of underwater sound speed, which is seldom considered in the literature. Due to the complexity of distance measurement caused by curved sound lines, the optimization of reference node deployment based on curved sound lines is worthy of further research. This paper focuses on the optimal reference node deployment strategies for time-of-arrival (TOA) localization of anchor nodes in IoUT networks in the three-dimensional (3D) underwater space. We adopt the criterion that minimizing the trace of the inverse Fisher information matrix (FIM) to facilitate the optimal reference nodes deployment with Gaussian measurement noise, the magnitude of which is positive related to the real signal propagation path. It is proved that the optimal reference node deployment position exhibits central symmetry with respect to the target, which greatly simplifies the analysis of the optimal node deployment position. Then, a semi-closed form of the optimal pitch angle is derived to determine the optimal geometries. To demonstrate the findings in this paper, we conducted both simulations and real-world experiments on underwater anchor node positioning. Both the simulation and experiment results demonstrate that the proposed deployment scheme significantly improve the positioning accuracy.

UCL Discovery (University College London)
Life below water
Openalex Percentile: Top 53%
Thermal Analysis in Power Transmission
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