Engineering Transformation for the Integration of Sensor Networks: A Feasibility Study - 'ENTRAIN'

传感器网络集成的工程转型:可行性研究 -“ENTRAIN”

基本信息

  • 批准号:
    NE/S016244/1
  • 负责人:
  • 金额:
    $ 32.06万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2019
  • 资助国家:
    英国
  • 起止时间:
    2019 至 无数据
  • 项目状态:
    已结题

项目摘要

There is a need to make use of new digital data analysis techniques to improve our understanding of the environment. Data from a new generation of environmental sensors, combined with analyses based on Artificial Intelligence, has the potential to help us understand from human influences and long-term change are affecting the environment around us. Artificial Intelligence approaches enable computers to identify trends and relationships across different streams of data, often picking out patterns that would be too difficult or time-consuming for humans to identify manually.To realise these benefits, data from diverse sensor networks must combined and analysed together. Currently many sensor networks are operated individually, and data are not readily combined due to differences in the way measurements are made (e.g. between weekly river samples and sub-second measurements of gases in the atmosphere). In addition, to combine these data in an automatic way without human intervention requires much finer and more consistent descriptions of the contents of data streams, so that machines can understand the content sufficiently. Links between sensors in space are also important, and machines will need an understanding of these links, not just in the sense of coordinates, but for example how sensors are linked along rivers. We can construct a digital representation of rivers in order to enable this.We will describe the various elements of a future environmental analysis system that will be required in order to achieve these benefits, and addressing some of these currently missing components. We will look at technologies, from databases to data transfer mechanisms, to understand how a system could be built.We will use data from 3 NERC sensor networks measuring environmental variables from the atmosphere to river water quality, and show how this data can be automatically integrated in such a way that machines would be able to analyse it automatically.A significant issue when monitoring with high-resolution sensors is how to handle problems in the data, which could include missing data, and erroneous values due to sensor failure. There is too much data for humans to manually view and check, and so automated approaches are needed. Currently these are often simple checks of individual data values against expected ranges, but again there are opportunities for artificial intelligence to improve this. AI approaches can look across multiple sensors, identify relationships, and find subtle changes in data signals, and this can be used to both identify data problems and to fix them through infilling. We will enhance the 3 NERC networks by testing and applying such approaches to data quality control.We will investigate some fundamental limitations of high-resolution monitoring, the transfer of large amounts of data from the field site to the data centre, the security of such systems, and whether more processing could be done on the instruments themselves to reduce data transfer volumes.We will meet with the public, with policy-makers, with industry and with researchers to discuss where there will be most to be gained from development of AI approaches to analysing environmental sensor data. We will develop ideas for future work to realise these gains, and will promote the benefits of an integrated system for environmental monitoring. These stakeholders are likely to include the Environment Agency, SEPA, Natural Resources Wales, Defra, Water companies, sensor network developers, and public organisations with an interest in the environment, including the National Trust, the Rivers Trusts, and local community groups.
需要利用新的数字数据分析技术来提高我们对环境的了解。来自新一代环境传感器的数据与基于人工智能的分析相结合,有可能帮助我们了解人类影响和长期变化正在影响我们周围的环境。人工智能方法使计算机能够识别不同数据流之间的趋势和关系,通常会挑选出人类手动识别过于困难或耗时的模式。为了实现这些好处,来自不同传感器网络的数据必须组合在一起并进行分析。目前,许多传感器网络都是单独运行的,由于测量方式的差异(例如每周的河流样本和大气中气体的亚秒测量之间),数据不容易合并。此外,要在没有人工干预的情况下以自动方式组合这些数据,需要对数据流的内容进行更精细、更一致的描述,以便机器能够充分理解内容。空间传感器之间的链接也很重要,机器需要了解这些链接,不仅仅是坐标意义上的链接,例如传感器如何沿着河流链接。为了实现这一点,我们可以构建河流的数字表示。我们将描述实现这些好处所需的未来环境分析系统的各种要素,并解决其中一些目前缺失的组件。我们将研究从数据库到数据传输机制的技术,以了解如何构建系统。我们将使用来自 3 个 NERC 传感器网络的数据来测量从大气到河流水质的环境变量,并展示如何以机器能够自动分析的方式自动集成这些数据。使用高分辨率传感器进行监测时的一个重要问题是如何处理数据中的问题,其中可能包括数据丢失和错误 由于传感器故障而导致的值。人类需要手动查看和检查的数据太多,因此需要自动化方法。目前,这些通常是根据预期范围对单个数据值进行简单检查,但人工智能有机会改进这一点。人工智能方法可以跨多个传感器,识别关系,并发现数据信号中的细微变化,这可以用来识别数据问题并通过填充来修复它们。我们将通过测试和应用此类数据质量控制方法来增强 3 个 NERC 网络。我们将研究高分辨率监测的一些基本限制、从现场到数据中心的大量数据传输、此类系统的安全性,以及是否可以在仪器本身上进行更多处理以减少数据传输量。我们将与公众、政策制定者、行业和研究人员会面,讨论从发展中获得最大收益的领域。 分析环境传感器数据的人工智能方法。我们将为未来的工作制定想法以实现这些成果,并将促进环境监测综合系统的好处。这些利益相关者可能包括环境局、SEPA、威尔士自然资源部、Defra、自来水公司、传感器网络开发商以及对环境感兴趣的公共组织,包括国家信托基金、河流信托基金和当地社区团体。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Sensor data and metadata standards review for UKCEH
UKCEH 的传感器数据和元数据标准审查
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Williams S
  • 通讯作者:
    Williams S
Estimating snow water equivalent using cosmic-ray neutron sensors from the COSMOS-UK network
使用 COSMOS-UK 网络的宇宙射线中子传感器估算雪水当量
  • DOI:
    10.1002/hyp.14048
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    3.2
  • 作者:
    Wallbank J
  • 通讯作者:
    Wallbank J
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Matthew Fry其他文献

River reach-level machine learning estimation of nutrient concentrations in Great Britain
英国河流河段的养分浓度机器学习估计
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    2.9
  • 作者:
    C. M. Tso;Eugene Magee;David Huxley;Michael Eastman;Matthew Fry
  • 通讯作者:
    Matthew Fry
Breeding distrust: The biopolitics of chronic wasting disease in white-tailed deer
繁殖不信任:白尾鹿慢性消耗性疾病的生物政治学
The Geo-imaginaries of potential in Mexico's Burgos Basin
墨西哥布尔戈斯盆地潜力的地理想象
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Matthew Fry;Trey Murphy
  • 通讯作者:
    Trey Murphy
Powering the Future: The Latest Breakthrough in Wireless Charging for Electric Vehicles
为未来提供动力:电动汽车无线充电的最新突破
Contracts and access to Mexico’s natural gas resources: The text is not legible on the ground
合同和墨西哥天然气资源的获取:文本在地面上无法辨认
  • DOI:
    10.1016/j.geoforum.2022.06.004
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Trey Daniel;Matthew Fry;Andrew Hilburn;Armando García
  • 通讯作者:
    Armando García

Matthew Fry的其他文献

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{{ truncateString('Matthew Fry', 18)}}的其他基金

Engineering Transformation for the Integration of Sensor Networks: A Feasibility Study - 'ENTRAIN'
传感器网络集成的工程转型:可行性研究 -“ENTRAIN”
  • 批准号:
    NE/S016244/2
  • 财政年份:
    2019
  • 资助金额:
    $ 32.06万
  • 项目类别:
    Research Grant
Collaborative Research: A Spatial Analysis of the Determinants of Setback Distance Variation Between Shale Gas Wells and Residences
合作研究:页岩气井与住宅区退距变化决定因素的空间分析
  • 批准号:
    1262521
  • 财政年份:
    2013
  • 资助金额:
    $ 32.06万
  • 项目类别:
    Standard Grant

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Engineering Transformation for the Integration of Sensor Networks: A Feasibility Study - 'ENTRAIN'
传感器网络集成的工程转型:可行性研究 -“ENTRAIN”
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  • 财政年份:
    2019
  • 资助金额:
    $ 32.06万
  • 项目类别:
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