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/S016236/1
• 负责人: Georgios Leontidis
• 金额: $ 11.73万
• 依托单位: University of Lincoln
• 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
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS016236%2F1
• 关键词: Engineering; Transformation; Integration; Sensor; Networks

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.

有必要利用新的数字数据分析技术来提高我们对环境的认识。来自新一代环境传感器的数据，结合基于人工智能的分析，有可能帮助我们了解人类的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结合在一起进行分析。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级测量之间的差异），数据不容易合并。此外，要在没有人为干预的情况下以自动方式联合收割机组合这些数据，需要对数据流的内容进行更精细和更一致的描述，以便机器可以充分理解内容。空间中传感器之间的链接也很重要，机器需要了解这些链接，不仅是坐标意义上的链接，还需要了解传感器如何沿着沿着链接。我们可以构建一个河流的数字表示，以实现这一点。我们将描述未来的环境分析系统，将需要为了实现这些好处的各种元素，并解决这些目前缺少的组件。我们将着眼于技术，从数据库到数据传输机制，以了解如何建立一个系统。我们将使用来自3个NERC传感器网络的数据，测量从大气到河流水质的环境变量，并展示了如何将这些数据自动集成，使机器能够自动分析这些数据。分辨率传感器是如何处理数据中的问题，其中可能包括丢失的数据和由于传感器故障而导致的错误值。对于人类来说，有太多的数据需要手动查看和检查，因此需要自动化方法。目前，这些通常是针对预期范围对单个数据值进行简单检查，但人工智能也有机会改进这一点。人工智能方法可以查看多个传感器，识别关系，并发现数据信号中的细微变化，这可以用于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这些方法来加强NERC网络的数据质量控制。我们将调查高分辨率监测的一些基本限制，将大量数据从现场传输到数据中心，这些系统的安全性，以及是否可以在仪器本身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，与工业界和研究人员讨论，从分析环境传感器数据的人工智能方法的发展中获得的好处最多。我们将为未来的工作提出想法，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署、SEPA、威尔士自然资源部、Defra、水务公司、传感器网络开发商以及对环境感兴趣的公共组织，包括国家信托基金会、河流信托基金会和当地社区团体。

项目成果

Imputation of missing sub-hourly precipitation data in a large sensor network: a machine learning approach

• DOI: 10.1016/j.jhydrol.2020.125126
• 发表时间: 2020-03
• 期刊: ArXiv
• 作者: Benedict D. Chivers;J. Wallbank;S. Cole;O. Šebek;S. Stanley;M. Fry;G. Leontidis

A Deep Learning Approach to Fill in Missing Values in Environmental Datasets

填补环境数据集中缺失值的深度学习方法

• 发表时间: 2019
• 作者: Pisarczyk, P

Sub-Hourly Imputation in Large Environmental Sensory Network: A Machine Learning Approach

大型环境感知网络中的每小时插补：一种机器学习方法

• 发表时间: 2020
• 作者: Holmes W.