The COVID-19 related lockdown measures offer a unique opportunity to understand how changes in economic activity and traffic affect ambient air quality, and how much pollution reduction can the society offer through digitalization and mobility-limiting policies. In this work, we estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring networks, training long-term prediction models and comparing their predictions to measured values over the lockdown month. We show that our models achieve state-of-the-art performance and evaluate up to -29.4%, -28.1%, and -52.8%, change in NO2 in Eastern Switzerland, Beijing and Wuhan respectively. Our reduction estimates take local weather into account. What can we learn from pollution emissions during lockdown? The lockdown period was too short to train meaningful models from scratch. We therefore use transfer learning to update only mobility-dependent variables. We show that the obtained models are suitable for the analysis of the post-lockdown periods and capable of estimating the future air pollution reduction potential.

Low-cost sensors are extensively used in numerous Internet of Things (IoT) applications to measure relevant physical processes. Today, processing context data is increasingly done by proprietary algorithms tuned to a specific use-case, e.g., a sensor measuring activity intensity of a cow. Readings from these sensors may be subject to data distribution shifts, which challenge robustness of models using these sensor readings. In this paper, we propose a new sensor data processing framework, which leverages a co-dependency between data quality and model robustness to detect performance issues of data-driven predictive models in the field. We show how distribution shifts in the input data impact the quality of the model, which relies on application-specific sensors, and present indicators capable of detecting such shifts in the wild. The proposed framework used in the context of precision cattle farming allows improving the quality of cow lameness predictive models on the field data by up to 62%.

Machine learning has a lot of potential when applied to time series sensor data, yet a lot of this potential is currently not utilized, due to privacy concerns of parties in charge of this data. In this work I want to apply privacy-preserving techniques to machine learning for time series data, in order to unleash the dormant potential of this type of data.

In this work, we show that sensor data may leak a sensor's location even if the latter is not explicitly included in the data set. The sensors are localized by linking sensor data, in particular activity data, with publicly available environmental data such as weather data. We show that using a linkage attack a cow can be localized within an entire country with an average accuracy of up to 32.6 km solely from activity traces recorded with a tracker in the cow's stomach.