Self-Sustainable Environmental Sensors for Flood Forecasting and Smart Metering Applications

用于洪水预报和智能计量应用的自我可持续环境传感器

• 批准号: 2536783
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2536783
• 关键词: Self; Sustainable; Environmental; Sensors; Flood

Key milestones* Development of Novel energy harvesting method that utilises the sensor as the energy generator. * Energy efficient computing that ultimately makes the system self-sustainable.* Development of a low-power, re-routable network of sensors, that communicate data to a server.* real-time computation of the datasets. * Investigation into machine learning based algorithms for flood forecasting and smart metering.This project looks at the development of versatile system that can be used for flood forecasting and smart metering application, but not limited to. The applications of the developed hardware and software would be modular to various sensors for IoT devices, such as environmental monitoring, industrial automation, etc.The fundamentals of the system will consist of a network of self-sustainable embedded sensors that uses novel energy harvesting to power the embedded system from the sensors themselves. Once the energy is harnessed the project will investigate how to best utilise the harnessed energy to prolong said system's survival time and maximise the systems quality of service. Additionally, research will focus on the development of real-time machine learning algorithms for flood forecasting and smart metering. But also, how can we execute computationally intensive algorithms in remote IoT devices when energy is scarce.

关键里程碑*利用传感器作为能量发生器的新型能量收集方法的开发。 *最终使系统自我维护的能源效率计算。*开发低功率，可重新布置的传感器网络，该网络将数据通信到服务器。*数据集的实时计算。 *调查基于机器学习的算法，以进行洪水预测和智能计量。该项目着眼于开发多功能系统，该系统可用于洪水预测和智能计量应用，但不限于。已开发的硬件和软件的应用将对物联网设备的各种传感器（例如环境监视，工业自动化等）模块化。系统的基础将包括一个可自我维护的嵌入式传感器网络，该网络使用新颖的能量收集来为传感器本身启动嵌入式系统。一旦利用能量，该项目将研究如何最好地利用利用的能量来延长该系统的生存时间并最大程度地提高系统的服务质量。此外，研究将集中于开发用于洪水预测和智能计量的实时机器学习算法。但是，当能量稀缺时，我们如何在远程物联网设备中执行计算密集型算法。