Self-Learning Pipeline for Low-Energy Resource-Constrained Devices

Fouad Sakr, Riccardo Berta, Joseph Doyle, Alessandro De Gloria and Francesco Bellotti
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Fouad Sakr: Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy
Riccardo Berta: Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy
Joseph Doyle: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Alessandro De Gloria: Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy
Francesco Bellotti: Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy

Energies, 2021, vol. 14, issue 20, 1-19

Abstract: The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and challenging to annotate. This paper introduces the self-learning autonomous edge learning and inferencing pipeline (AEP), deployable in a resource-constrained embedded system, which can be used for unsupervised local training and classification. AEP uses two complementary approaches: pseudo-label generation with a confidence measure using k-means clustering and periodic training of one of the supported classifiers, namely decision tree (DT) and k-nearest neighbor (k-NN), exploiting the pseudo-labels. We tested the proposed system on two IoT datasets. The AEP, running on the STM NUCLEO-H743ZI2 microcontroller, achieves comparable accuracy levels as same-type models trained on actual labels. The paper makes an in-depth performance analysis of the system, particularly addressing the limited memory footprint of embedded devices and the need to support remote training robustness.

Keywords: machine learning; self-learning; edge computing; resource-constrained devices; autonomous systems; on-device training; k-NN; decision tree; STM32 NUCLEO (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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