  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.
