Machine Learning

Collaborative Learning at the Edge for Air Pollution Prediction

The rapid growth of connected sensing devices has resulted in enormous amounts of data being collected and processed. Air quality data collected from different monitoring stations is spatially and temporally correlated, and hence, collaborative …

TinyML Models for a Low-cost Air Quality Monitoring Device

Low-cost air quality monitoring devices can provide high-density spatiotemporal pollution data, thus offering a better opportunity to apply machine learning (ML). Low-cost sensor nodes usually utilize microcontrollers as the main processors, and …

REFL: Resource-Efficient Federated Learning

Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device …

Streaming Overlay Architecture for Lightweight LSTM Computation on FPGA SoCs

Long-Short Term Memory (LSTM) networks, and Recurrent Neural Networks (RNNs) in general, have demonstrated their suitability in many time series data applications, especially in Natural Language Pro- cessing (NLP). Computationally, LSTMs introduce …

Estimation of Missing Air Pollutant Data Using a Spatiotemporal Convolutional Autoencoder

A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be …

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In …