Ihsane Gryech , Jimmy Fernandez Landivar , and  Sofie Pollin.

Department of Electrical Engineering (ESAT) - WaveCoRE, KU Leuven, Belgium

Overview

Addressing the pressing environmental challenges of today is crucial, and the call for action will only grow stronger in the future. This is where the dynamic combination of Artificial Intelligence (AI) and the Internet of Things (IoT) comes into play. By merging these advanced technologies, we can transform the way we observe and understand our planet. 🌍

 In this tutorial, we’ll explore the integration of Machine Learning (ML) with IoT to develop smart, efficient systems that provide real-time insights into our environment.  But why stop there? Let's add a human twist! Imagine if we not only tracked pollution through sensors but also compared that data with how people feel and talk about pollution?  

It's time to bridge the gap between objective data and real human stories 🌱.

What You'll Learn:

• How to assemble simple IoT sensors to collect environmental data, with a strong focus on challenges such as energy efficiency, sustainability, and e-waste. 

• How to apply Machine Learning (ML) models to process and analyze environmental sensor data. Using simple ML methods, the participants will learn how to generate actionable insights, spatio-temporal forecasts, and useful visualizations for real-world challenges like air quality monitoring and climate prediction.

• Techniques to compare objective data with subjective human feedback and narratives.

   🌐  This tutorial is customized for a wide audience of master’s, PhD students or even bachelors interested in computer science, environmental science, or electrical engineering, where no background is required in Artificial Intelligence or the Internet of Things. By the end of the tutorial, participants will be equipped to design sustainable IoT solutions enhanced with ML capabilities to tackle environmental challenges effectively.

Ready to dive in? Sign up now!