Machine Learning for IoT: Datasets, Perception, and Understanding

As the emerging Internet of Things (IoT) brings a massive population of multi-modal sensors in the environment, there is a growing need in developing new Machine Learning (ML) techniques to analyze the data and unleash its power. A data-driven IoT ecosystem forms the basis of Ambient Intelligence, i.e., smart environment that is sensitive to the presence of humans and can ultimately help automate human life. IoT data are highly heterogeneous, involving not only the traditional audio-visual modalities, but also many emerging sensory dimensions that go beyond human perception. The rich IoT sensing paradigms pose vast new challenges and opportunities that call for coordinated research efforts between the ML and IoT communities. On one hand, the IoT data require new ML hardware/software platforms and innovative processing/labeling methods for efficient collection, curation, and analysis. On the other hand, compared with traditional audio/visual/textual data that have been widely studied in ML, the new IoT data often exhibit unique challenges due to the highly heterogeneous modalities, disparate dynamic distributions, sparsity, intensive noise, etc. Besides, the involved rich environment and human interactions pose challenges for privacy and security. All those properties hence require new paradigms of ML based perception and understanding. The objective of this workshop is to bring together leading researchers in the ML/IoT industry and academia to address these challenges. The workshop will also solicit benchmark IoT datasets, as a basis for ML researchers to design and benchmark new modeling and data analytic tools.