FUSE: Floodplain Underground SEnsors- A high-density, wireless, underground Sensor Network to quantify floodplain hydro-ecological interactions

FUSE：洪泛区地下传感器 - 高密度、无线、地下传感器网络，用于量化洪泛区水文生态相互作用

• 批准号: NE/I00694X/1
• 负责人: Julie A McCann
• 金额: $ 36.56万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FI00694X%2F1
• 关键词: FUSE; Floodplain; Underground; SEnsors; density

Improved understanding of the functioning of hydrological systems and dependent ecology is essential for optimal environmental management. Floodplains in particular are important due to the ecosystem services they provide. The species composition of floodplain vegetation and their ecosystem functions (e.g. leaf CO2 uptake and transpiration) are very sensitive to the soil hydrological regime, which is highly variable both spatially and temporally. The hydrological regime also affects the temperature and nutrient regime of the root environment, leading to indirect impacts on vegetation. However, the mechanisms controlling these interdependencies are not well established. The proposed project, FUSE, aims to advance this knowledge at a variety of scales. A better understanding of these vulnerable ecosystems will allow improved environmental management, under current and future conditions. A field study is proposed in the Oxford Floodplain (OFP). This study will build upon an existing hydrological monitoring network currently in place in the Oxford Meadows Special Area of Conservation (SAC). The aims will be achieved by a sophisticated combination of environmental data and computer models. This involves state-of-the-art tools: a Wireless Underground Sensor Network (WUSN) and related monitoring of environmental variables, as well as high-resolution Earth Observation (EO, i.e. satellite) data. WSNs are a relatively recent application of technology; uptake of this technology by environmental scientists enables continuous monitoring that is both scalable and less intrusive on its surroundings. It is desirable for land-based sensor networks to have few or no above-ground components, for aesthetic and security reasons, as well as to avoid interference with land management practices. Recently, this has led to the introduction of WUSNs where all or at least the majority of the sensing and transmitting components are underground. WUSNs are rare, especially in the UK, and have not been tested long-term in a challenging environment such as the OFP. Reliability and the potential distance of data transmission depend on a number of factors, including the soil type, sensor installation depth, soil moisture content and technological factors. These will be researched extensively in the FUSE project, initially using existing data on the OFP hydrological regime, soils and vegetation height/density. The precise design of the WUSN will be determined with the aid of a geostatistical procedure. FUSE will allow researchers to reliably measure underground spatial variability at hitherto unachievable resolutions of less than a metre. The project will use a mesh of simple wireless sensor nodes previously developed at Imperial College ('Beasties'). These nodes will gather environmental data, and route these to a base-station that transmits to a remote database via GPRS. The low-cost, low-power Beasties have been used extensively in similar, but less challenging environments. The enhanced sensor technology will be entirely transferable. Theoretical tools in FUSE comprise of a simulation model (SCOPE_SUB), that can be used to describe and predict the interaction between the soil (soil moisture content, soil temperature and nutrient status), the vegetation (root water/nutrient uptake, CO2 uptake and transpiration), and the hydrometeorological regime. Furthermore we will use geospatial models to spatially interpolate between measured, modelled and EO data, thereby increasing data-density. EO data will serve to guide the continuous (in time) simulation model predictions. In that way high resolution maps of key soil and vegetation variables can be constructed. Computer Science tools, e.g. a so-called Integrated Development Environment to help environmental scientist to set up and test the WUSN, and a Web portal for quality control, sensor calibration, time series- and geospatial-analysis, parameter estimation and real-time model output, will be developed.

更好地了解水文系统和依赖生态系统的运作对于优化环境管理至关重要。洪泛区因其提供的生态系统服务而尤为重要。洪泛区植被的物种组成及其生态系统功能（如叶片CO2吸收和蒸腾）对土壤水文状况非常敏感，这在空间和时间上都是高度可变的。水文状况也影响根系环境的温度和养分状况，从而对植被产生间接影响。然而，控制这些相互依存关系的机制并没有很好地建立。拟议的项目FUSE旨在以各种规模推进这一知识。更好地了解这些脆弱的生态系统将有助于在当前和未来的条件下改善环境管理。在牛津洪泛区（OFP）进行了实地研究。这项研究将建立在现有的水文监测网络，目前在牛津草地特别保护区（SAC）。这些目标将通过环境数据和计算机模型的复杂结合来实现。这涉及最先进的工具：无线地下传感器网络和相关的环境变量监测，以及高分辨率地球观测（EO，即卫星）数据。无线传感器网络是一种相对较新的技术应用;环境科学家采用这种技术可以实现连续监测，这种监测既具有可扩展性，又对周围环境的干扰较小。出于美观和安全原因，以及为了避免干扰土地管理做法，陆基传感器网络最好有很少或没有地上组件。最近，这导致了WUSS的引入，其中所有或至少大部分的传感和传输组件都在地下。WUSN是罕见的，特别是在英国，并没有在一个具有挑战性的环境，如OFP长期测试。可靠性和数据传输的潜在距离取决于许多因素，包括土壤类型、传感器安装深度、土壤含水量和技术因素。FUSE项目将对这些问题进行广泛研究，首先使用关于OFP水文状况、土壤和植被高度/密度的现有数据。WUSN的精确设计将借助地质统计程序确定。FUSE将使研究人员能够以迄今无法实现的小于一米的分辨率可靠地测量地下空间变化。该项目将使用以前在帝国理工学院开发的简单无线传感器节点网格（“Beasties”）。这些节点将收集环境数据，并将这些数据路由到基站，基站通过GPRS传输到远程数据库。低成本、低功耗的Beasties已被广泛用于类似但挑战性较小的环境中。增强的传感器技术将完全可转让。FUSE中的理论工具包括一个模拟模型（SCOPE_ESTA），可用于描述和预测土壤（土壤水分含量、土壤温度和养分状况）、植被（根系水分/养分吸收、CO2吸收和蒸腾）和水文气象状况之间的相互作用。此外，我们将使用地理空间模型在测量，建模和EO数据之间进行空间插值，从而提高数据密度。EO数据将用于指导连续（及时）模拟模型预测。这样就可以绘制高分辨率的关键土壤和植被变量图。将开发计算机科学工具，例如帮助环境科学家建立和测试WUSN的所谓综合开发环境，以及用于质量控制、传感器校准、时间序列和地理空间分析、参数估计和实时模型输出的门户网站。

项目成果

Cognisense

• DOI: 10.1145/3485730.3492879
• 发表时间: 2021-11
• 期刊: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems
• 作者: M. Heggo;Laksh Bhatia;J. Mccann

RFTacho: Non-intrusive RF monitoring of rotating machines

• DOI: 10.1109/ipsn54338.2022.00039
• 发表时间: 2022-02
• 期刊: 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
• 作者: M. Heggo;Laksh Bhatia;J. McCann

Self-Synchronization in Duty-Cycled Internet of Things (IoT) Applications

• DOI: 10.1109/jiot.2017.2757138
• 发表时间: 2017-07
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Poonam Yadav;J. Mccann;T. Pereira