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Título:
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Block-Sparse Coding Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment
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Autores:
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Zhao, Lingjun ;
Huang, Huakun ;
Su, Chunhua ;
Ding, Shuxue ;
Huang, Huawei ;
Tan, Zhiyuan ;
Li, Zhenni
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Tipo de documento:
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texto impreso
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Editorial:
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Institute of Electrical and Electronics Engineers, 2021-01-17T01:02:50Z
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Palabras clave:
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Ethics collections
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Cyber Ethics
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Resumen:
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Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to a undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds-10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases.
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En línea:
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oai:napier-surface.worktribe.com:2682243
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