Intelligent Multi-AUG Ocean Data Collection Scheme in Maritime Wireless Communication Network

The new generation of wireless networks is strengthening ocean development capabilities. The deep-sea detection ability of the offshore platform is being improved by Ocean Internet of Everything (OIoE) system formed by various equipment. This paper studies the application of a new generation of Artificial Intelligence (AI) wireless communication technology in marine monitoring platforms. The purpose is to solve the problem of high data redundancy and high energy consumption in multi-Automatic Underwater Glider (AUG) monitoring. In response to these problems, this paper proposes a matrix completion-based Sampling Points Selection joint Multi-AUG Path Planning (SPS-MAPP) scheme, which includes AUGs path planning and collection information matrix completion. In parts of AUGs path planning, the SPS-MAPP designed a multi-AUG trajectory optimization method using intermittent satellite communication. It proposes a reinforcement learning reward rule based on the idea of artificial potential field, which can effectively increase the coverage of AUGs trajectory. In parts of information processing, the SPS-MAPP proposes a 3-D matrix completion method, which searches for key nodes and edge nodes by designing regular sample points and operators to recover the entire space information. The experimental results show that the proposed SPS-MAPP scheme can effectively improve the subsea monitoring capability of multi-AUG.

Automated summary

This paper studies the application of a new generation of AI wireless communication technology in marine monitoring platforms. It proposes a matrix completion-based Sampling Points Selection joint Multi-AUG Path Planning (SPS-MAPP) scheme to solve the problem of high data redundancy and high energy consumption in multi-AUG monitoring.