Objective:
This project proposes, designs, evaluates, and tests the Fi-Mi mobile air quality monitoring and forecasting system. Fi-Mi is a monitoring system based on compact devices placed on buses, and uses artificial intelligence to predict future air quality, as well as air quality in areas unobservable by measuring equipment.
To optimize the operation of Fi-Mi, the project proposes an architecture consisting of three layers: sensor layer, information layer, and application layer. The sensor layer includes self-made monitoring devices containing air quality sensor modules. By installing monitoring devices on mobile buses, Fi-Mi is capable of monitoring a large area of space with only a small number of devices. The information layer is responsible for transmitting sensor information from measuring devices to the central server system. The application layer includes a server system responsible for storing information and processing data to predict air quality. The project focuses on two main research directions: Improving the accuracy of monitoring and forecasting air quality indicators, and improving the efficiency of information transmission.
Impact:
This project has major impact in many aspects, including theory, practice and education. The project will promote related theoretical research and achieve many influential results in many fields such as optimization, deep learning, high-precision positioning, data correction, and effective information communication. In practical terms, the project will build a mobile air quality monitoring system throughout Hanoi city. The project will also provide a library of air quality data processing APIs to help third parties analyze and predict their air quality. The project builds and provides an open database of air quality indexes of Hanoi city collected from the system. The open database will help researchers access complete information about air quality in Hanoi, promoting related research. In terms of education, the project will contribute to training a number of postdocs and masters.
