An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT
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Published:2023-11-30
Issue:12
Volume:11
Page:2279
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ISSN:2077-1312
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Container-title:Journal of Marine Science and Engineering
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language:en
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Short-container-title:JMSE
Author:
Shi Wentao12, Tang Yongqi3, Jin Mingqi2, Jing Lianyou1ORCID
Affiliation:
1. Ocean Institute, Northwestern Polytechnical University, Taicang 215400, China 2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China 3. School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
Abstract
The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use of the autonomous underwater vehicles (AUV) as mobile collectors to build reliable collection systems, while the value of information (VoI) is used as the primary measure of information quality. This paper first builds a realistic model to characterize the behavior of sensor nodes and the AUV together with challenging environments. Then, improved deep reinforcement learning (DRL) is used to dynamically plan the AUV’s navigation route by jointly considering the location of nodes, the data value of nodes, and the status of the AUV to maximize the data collection efficiency of the AUV. The results of the simulation show the dynamic data collection scheme is superior to the traditional path planning scheme, which only considers the node location, and greatly improves the efficiency of AUV data collection.
Funder
National Natural Science Foundation of China Fundamental Research Funds for the Central Universities
Subject
Ocean Engineering,Water Science and Technology,Civil and Structural Engineering
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