Distributed Principal Component Analysis for Wireless IoT Grid Networks 


Vol. 47,  No. 7, pp. 953-962, Jul.  2022
10.7840/kics.2022.47.7.953


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  Abstract

In this study, the performance of the distributed principal component analysis (PCA) algorithm through bidirectional matrix division in a wireless channel environment was evaluated. Dividing the data matrix in both directions into a row direction and a column direction has an advantage of reducing the dimension of the matrix assigned to each device. The assigned matrices can be flexibly adjusted according to computing capability and environments. The principal component of the entire original matrix is estimated through communications between devices, but the existing distributed PCA algorithm cannot be applied to wireless enviornments due to wireless channel fading and noise. Therefore, in order to address the wireless channel effect, a time diversity scheme and a maximum ratio combiner at each receiving node are exploited. Utilizing signal combining techniques, diversity in the wireless channels, power methods, average consensus algorithm, this paper develops a power method-based distributed PCA for wireless IoT network. Our simulation results show that the distributed PCA still converges to it’s principal components in wireless grid networks.

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  Cite this article

[IEEE Style]

H. Heo, K. Son, W. Choi, "Distributed Principal Component Analysis for Wireless IoT Grid Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 7, pp. 953-962, 2022. DOI: 10.7840/kics.2022.47.7.953.

[ACM Style]

Hongjun Heo, Kyungrak Son, and Wan Choi. 2022. Distributed Principal Component Analysis for Wireless IoT Grid Networks. The Journal of Korean Institute of Communications and Information Sciences, 47, 7, (2022), 953-962. DOI: 10.7840/kics.2022.47.7.953.

[KICS Style]

Hongjun Heo, Kyungrak Son, Wan Choi, "Distributed Principal Component Analysis for Wireless IoT Grid Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 7, pp. 953-962, 7. 2022. (https://doi.org/10.7840/kics.2022.47.7.953)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880