Software framework implementation for air quality monitoring system (available)

Starting Date: June 2020
Duration: Part time, ongoing
Time commitment: 6 hours per week
Prerequisites: Experience with Raspberry Pi and Python

The project requires the installation and use of commercially developed Python based software packages on a Raspberry Pi 3 board which access commercial sensors to measure air quality. The software has calibration, data acquisition and data storage functions.

The hardware platform is based around 4 air contaminants Alphasense sensors mounted on an Alphasense AFE (analogue front end) board that communicates with a Raspberry Pi 3 through a Southcoast Science DFE (digital front end) board via I2C protocol. Besides the air contaminants sensors, the platform will also include a GPS hat module, a camera and a Thundersboard Sense 2 board that should communicate with RPI via the BLE or UART port. All data will be stored locally, on a memory card and send wirelessly (WiFi and radio) to a storage server. All the libraries needed for implementation are provided on GitHub but the documentation is sparse. You will need experience of the raspberry Pi environment, and ability to interact with the software developers to get things up and running.

This project would suit somebody who is familiar with Raspberry Pi and Python projects and who is comfortable with calibrating and monitoring sensors via multiple communication protocols (I2C, BLE, UART). The expected work is to acquire data from all the sensor, save it in a predefined format and send it to a storage server via WIFI and radio.

The project will be supervised within Computer Science and Electronic Engineering. The lead superviser is Alin Tisan (  in Electronics supported by Adrian Johnstone in Computer Science. Please get in touch with Adrian for general discussions, and with Alin for detailed technical requirements.