See Bruno’s talk here from 3:30 mins through to 21 minutes (Bruno’s was the first of three talks).
Use password AQ%4Y6iN to view the recording.
The puzzle: up until now, the task of measuring air pollution began with a choice between expensive, bulky and delicate scientific instruments and low-cost devices that offered inadequate levels of accuracy and repeatability.
By using Amazon Web Services big data and machine learning technologies, South Coast Science developed machine learning models that corrected the errors inherent in low-cost sensor components. By deploying these models to its monitoring devices, it is able to deliver products with a precision equivalent to reference stations, together with the cost, size and price of mainstream IoT equipment.
The talk steps through the tasks, and how they were solved.
Find out more about London IoT Meetup here and find out more about South Coast Science air quality monitors here.
