基于密集传感器网络和 NB-IoT 连接的基于事件的水文过程监测云系统

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ernesto Sanz , Jorge Trincado , Jorge Martínez , Jorge Payno , Omer Morante , Andrés F. Almeida-Ñaulay , Antonio Berlanga , José M. Molina , Sergio Zubelzu , Miguel A. Patricio
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引用次数: 0

摘要

水文学家认为,要加深对水文过程的理解,就需要高质量的实验数据。虽然已有精确的水文过程测量设备,但由于水文系统的特殊性,现场部署和运行有效的监测网络面临许多相关问题。在本手稿中,我们介绍了一个自主开发的基于事件的水文过程监测系统,该系统由一个密集的网络组成,土壤水分和水位测量仪通过 NB-IoT 技术连接到一个云系统中,以实现近乎实时的信息收集。我们设计、制造并校准了传感器,并将其集成到云系统中。我们在两个监测网络中部署了这些传感器,并从几次实验运行(电池生命周期)中收集了数据。结果表明,传感器和网络适用于正确监控流程,解决了最初主要由连接问题和电池持续时间引起的相关问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud-based system for monitoring event-based hydrological processes based on dense sensor network and NB-IoT connectivity

Hydrologists claim high-quality experimental data are required to improve the understanding of hydrological processes. Though accurate devices for measuring hydrological processes are available, the on-site deployment and operation of effective monitoring networks face many relevant issues caused by the peculiar characteristics of hydrological systems. In this manuscript, we present a self-developed system for monitoring events-based hydrological processes comprising a dense network with both soil moisture and water level gauges connected by NB-IoT technology integrated into a cloud system for near real-time gathering of information. We designed, built and calibrated the sensors and integrated them into a cloud system. We deployed them in two monitoring networks and gathered the data from several experimental runs (battery lifecycles). Results showed the suitability of the sensors and the network to properly monitor the processes solving the initial relevant issues mainly derived from connectivity issues and battery duration.

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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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