Optimal Augmentation: Machine Learning-Based Terminal Mobility Prediction Algorithm for Handover between Terrestrial-CubeSat in IoST 


Vol. 47,  No. 2, pp. 387-397, Feb.  2022
10.7840/kics.2022.47.2.387


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  Abstract

In IoST, terminal mobility causes overhead to reset links along with handover between terrestrial-cubesat. If handover fails in the process of processing such overhead, IoST loses the stability of communication along with link disconnection. Accordingly, in order to efficiently and stably support handover, this paper proposes grid labeling algorithm, which defines the terminal mobility prediction problem as a multiclass classification problem, and a data optimal augmentation algorithm that optimizes the performance of machine learning models.

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  Cite this article

[IEEE Style]

J. Oh, D. Lee, T. Ha, Y. Lee, S. Cho, "Optimal Augmentation: Machine Learning-Based Terminal Mobility Prediction Algorithm for Handover between Terrestrial-CubeSat in IoST," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 2, pp. 387-397, 2022. DOI: 10.7840/kics.2022.47.2.387.

[ACM Style]

Junsuk Oh, Donghyun Lee, Taeyun Ha, Yunseong Lee, and Sungrae Cho. 2022. Optimal Augmentation: Machine Learning-Based Terminal Mobility Prediction Algorithm for Handover between Terrestrial-CubeSat in IoST. The Journal of Korean Institute of Communications and Information Sciences, 47, 2, (2022), 387-397. DOI: 10.7840/kics.2022.47.2.387.

[KICS Style]

Junsuk Oh, Donghyun Lee, Taeyun Ha, Yunseong Lee, Sungrae Cho, "Optimal Augmentation: Machine Learning-Based Terminal Mobility Prediction Algorithm for Handover between Terrestrial-CubeSat in IoST," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 2, pp. 387-397, 2. 2022. (https://doi.org/10.7840/kics.2022.47.2.387)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880