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Design and Realization of Marine Internet of Things Environmental Parameter Acquisition System Based on NB-IoT Technology

Published: 06 June 2021 Publication History

Abstract

To monitor the shallow sea environment in time and accurately, and promote the construction of the marine Internet of Things, a parameter acquisition system for the shallow sea environment was designed in conjunction with the Narrow Band Internet of Things (NB-IoT). We use STM32 in this system to drive a variety of sensors to obtain the environ-mental parameters of the measured area and store them. And the NB-IoT module is used to upload the data to the remote control center, which is convenient for timely analysis and processing of the data and completing the data visualization on the user equipment. Also, to improve data accuracy, the data collection terminal is mounted on a mobile buoy to realize large-area sea area collection. Finally, the system has stable operation, perfect functions, low power consumption and high reliability via testing and verification, meet the shallow sea monitoring requirements. The method in this paper could well realize the environmental monitoring of a large area of the sea and improve the reliability of the data. At the same time, we can obtain the environmental parameters of the measured area in a timely and intuitive manner, and reduce the cost of manual data recovery and data processing. This method could provide effective data and scientific decision-making basis for marine protection, governance and development.

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  • (2023)Deep learning: survey of environmental and camera impacts on internet of things imagesArtificial Intelligence Review10.1007/s10462-023-10405-756:9(9605-9638)Online publication date: 6-Feb-2023

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cover image ACM Other conferences
ICCBN '21: Proceedings of the 2021 9th International Conference on Communications and Broadband Networking
February 2021
342 pages
ISBN:9781450389174
DOI:10.1145/3456415
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 06 June 2021

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Author Tags

  1. Environment parameter
  2. NB-IoT
  3. Sensors
  4. Visualization

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  • (2023)Deep learning: survey of environmental and camera impacts on internet of things imagesArtificial Intelligence Review10.1007/s10462-023-10405-756:9(9605-9638)Online publication date: 6-Feb-2023

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