Embedding principal component analysis for data reduction in structural health monitoring on low-cost IoT gateways

Alessio Burrello, Alex Marchioni, Davide Brunelli, Luca Benini

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 10x compression factor and 59x 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 4.8x on Samsung ARTIK 710.