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DEEP NEURAL NETWORK BASED MATRIX COMPLETION FOR INTERNET OF THINGS NETWORK LOCALIZATION

Cited 3 time in Web of Science Cited 7 time in Scopus
Authors

Kim, Sunwoo; Luong Trung Nguyen; Shim, Byonghyo

Issue Date
2020-05
Publisher
IEEE
Citation
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, pp.3427-3431
Abstract
In this paper, we propose a deep neural network based matrix completion approach for Internet of Things (IoT) localization. In the proposed method, we recast Euclidean distance matrix completion problem into the alternating minimization problem. By using a cascade of multiple deep neural networks to recover the location map of sensors (and the original distance matrix) from the noisy observed matrix, the proposed method can achieve an accurate reconstruction performance of the distance matrix. The numerical simulations demonstrate that the proposed method outperforms state-of-the-art matrix completion algorithms both in noisy and noiseless scenarios.
ISSN
1520-6149
URI
https://hdl.handle.net/10371/186519
DOI
https://doi.org/10.1109/ICASSP40776.2020.9053773
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