Air Quality Prediction in Office Rooms (1-2S/M)

Status: Available

• Type: Semester Thesis
• Professor: Prof. Dr. L. Benini
• Supervisors:
  • Philip Wiese (IIS): wiesep@iis.ee.ethz.ch
  • Victor Kartsch Morinigo (IIS): victor.kartsch@iis.ee.ethz.ch

Introduction

Multi-modal sensing enables on-device feature extraction, reducing communication overhead, perform predictions, increases privacy and enable on-device learning.

Smart sensors, which are sensors equipped with advanced computation capabilities, present a unique opportunity for innovation in multiple critical domains such as environmental sensing to reduce global greenhouse gas emissions or infrastructure monitoring. Our infrastructure is responsible for almost 80% of all global greenhouse gas emissions. Therefore, it is very important to better understand the individual contributors and to be able to build precise models to plan future infrastructure.

A near-sensor processing of the gathered data that directly extracts meaning from the signal can lead to huge improvements in terms of energy efficiency and increased privacy. The reduced energy consumption comes from the fact that more specialized hardware such as the PULP platform including the GAP9 can be used, and, additionally, that the information density of the transmitted data is higher, and thus less data must be transmitted via the often very power-intensive wireless link.

Our long-term goal is to develop a low-power sensing node with very low power consumption. These nodes should incorporate multiple sensors to enable multi-modal sensing. In other words, combine the data of the different sensors to extract additional data. The idea is to use the very efficient GAP9 multicore processor and deploy multi-modal neural networks to perform feature extraction, prediction, anomaly detection, and even on-device learning to adaptively fine-tune the model based on the measured data.

Air quality monitoring in the workplace ensures employees' healthy and productive environment. In office settings, pollutants such as volatile organic compounds (VOCs), dust, mold, and carbon dioxide can significantly impact staff well-being, causing symptoms like headaches, fatigue, and respiratory issues. Regular monitoring helps identify and mitigate these contaminants, improving employee health, reducing absenteeism, and enhancing overall productivity. Automatic systems for sensing, processing and classifying alarms conditions are hence of interest.

Project

• Commissioning of the UTSensorNode: Test base functionalities of the UTSensorNode PCB.
• Base Framework for the UTSensorNode: Development of a supervisor system to manage the board resources and integrate or develop drivers for different sensors, including the camera.
• Data Acquisition: Development of a platform for the acquisition creation of a dataset.
• Application Specific Device Optimization: Algorithm exploration for an accurate measurement of air quality, implementation on GAP9 and potential use of on-device learning to fine-tune the model to new environments.

Prerequisites

• Good understanding of deep learning concepts.
• PCB Design and embedded C programming.
• Familiarity with Python programming.
• Familiarity with the PyTorch deep learning framework.