Searching Neural Architectures for Sensor Nodes on IoT Gateways

Abstract--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. Index T erms--Neural Architecture Search, Edge AI, Healthcare Internet of Things, Industrial Internet of Things. Neural Networks (NNs) are widely used in Internet of Things (IoT) applications [1]. In this context, often the data collected by the available sensors are added to the training set with the purpose of improving generalization performances. On the other hand, in some cases, the data can be sensitive; healthcare data [2], industrial data [3] and biometric data [4] provide possible examples. Privacy concerns prevent some entities from accessing the benefits of machine learning (ML), as they may be unable or unwilling to share their data with cloud services that can train or even automatically design a custom neural network (NN) [5].