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arXiv · 1905.01093

Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways

Alessio Burrello·Alex Marchioni·Davide Brunelli·Luca Benini
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Abstract

Principal component analysis (PCA) is a powerful data reductionmethod for Structural Health Monitoring. However, its computa-tional cost and data memory footprint pose a significant challengewhen PCA has to run on limited capability embedded platformsin low-cost IoT gateways. This paper presents a memory-efficientparallel implementation of the streaming History PCA algorithm.On our dataset, it achieves 10x compression factor and 59x memoryreduction with less than 0.15 dB degradation in the reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over, the algorithm benefits from parallelization on multiple cores,achieving a maximum speedup of 4.8x on Samsung ARTIK 710.

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Alessio Burrello, Alex Marchioni, Davide Brunelli, Luca Benini. 2019-05-03. Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways. https://arxiv.org/abs/1905.01093

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