Principal component analysis (PCA) is a powerful data reduction method for Structural Health Monitoring. However, its computational cost and data memory footprint pose a significant challenge when PCA has to run on limited capability embedded platforms in low-cost IoT gateways. This paper presents a memory-efficient parallel implementation of the streaming History PCA algorithm. On our dataset, it achieves 10× compression factor and 59× memory reduction with less than 0.15 dB degradation in the reconstructed signal-to-noise ratio (RSNR) compared to standard PCA. Moreover, the algorithm benefits from parallelization on multiple cores, achieving a maximum speedup of 4.8× on Samsung ARTIK 710.
Burrello A., Marchioni A., Brunelli D., Benini L. (2019). Embedding Principal Component Analysis for Data Reduction in Structural Health Monitoring on Low-Cost IoT Gateways. 1515 BROADWAY, NEW YORK, NY 10036-9998 USA : Association for Computing Machinery, Inc [10.1145/3310273.3322822].
Embedding Principal Component Analysis for Data Reduction in Structural Health Monitoring on Low-Cost IoT Gateways
Burrello A.;Marchioni A.;Brunelli D.;Benini L.
2019
Abstract
Principal component analysis (PCA) is a powerful data reduction method for Structural Health Monitoring. However, its computational cost and data memory footprint pose a significant challenge when PCA has to run on limited capability embedded platforms in low-cost IoT gateways. This paper presents a memory-efficient parallel implementation of the streaming History PCA algorithm. On our dataset, it achieves 10× compression factor and 59× memory reduction with less than 0.15 dB degradation in the reconstructed signal-to-noise ratio (RSNR) compared to standard PCA. Moreover, the algorithm benefits from parallelization on multiple cores, achieving a maximum speedup of 4.8× on Samsung ARTIK 710.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
