Searching Neural Architectures for Sensor Nodes on IoT Gateways
This enables privacy-preserving machine learning for healthcare and industrial IoT applications, though it is incremental as it builds on existing neural architecture search methods.
The paper tackles the problem of designing neural networks for IoT sensor nodes without sharing data externally, achieving state-of-the-art results on the Visual Wake Words dataset with a search procedure that runs in under 10 hours on a Raspberry Pi Zero 2.
This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT) applications. The proposed method runs on IoT gateways and designs NNs for connected sensor nodes without sharing the collected data outside the local network, keeping the data in the site of collection. This approach has the potential to enable ML for Healthcare Internet of Things (HIoT) and Industrial Internet of Things (IIoT), designing hardware-friendly and custom NNs at the edge for personalized healthcare and advanced industrial services such as quality control, predictive maintenance, or fault diagnosis. By preventing data from being disclosed to cloud services, this method safeguards sensitive information, including industrial secrets and personal data. The outcomes of a thorough experimental session confirm that -- on the Visual Wake Words dataset -- the proposed approach can achieve state-of-the-art results by exploiting a search procedure that runs in less than 10 hours on the Raspberry Pi Zero 2.