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

关键里程碑 * 开发利用传感器作为能量发生器的新型能量收集方法。* 节能计算，最终使系统自我可持续。*开发低功耗、可重新路由的传感器网络，将数据传输到服务器。*数据集的实时计算。* 研究基于机器学习的洪水预报和智能计量算法。该项目着眼于开发可用于洪水预报和智能计量应用的多功能系统，但不限于。所开发的硬件和软件的应用将模块化到物联网设备的各种传感器中，例如环境监测，工业自动化等。该系统的基本原理将包括一个可自我维持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的嵌入式系统供电。一旦能源被利用，该项目将研究如何最好地利用所利用的能源，以延长所述系统的生存时间，并最大限度地提高系统的服务质量。此外，研究将侧重于开发用于洪水预报和智能计量的实时机器学习算法。但是，当能源稀缺时，我们如何在远程物联网设备中执行计算密集型算法。

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.