Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

Zhao, Lingjun; Huang, Huakun; Su, Chunhua; Ding, Shuxue; Huang, Huawei; Tan, Zhiyuan; Li, Zhenni

Abstract

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.

Citation

Zhao, L., Huang, H., Su, C., Ding, S., Huang, H., Tan, Z., & Li, Z. (2021). Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment. IEEE Internet of Things Journal, 8(5), 3211-3223. https://doi.org/10.1109/jiot.2020.3019732

• Journal Article Type: Article
• Acceptance Date: Aug 19, 2020
• Online Publication Date: Aug 26, 2020
• Publication Date: Mar 1, 2021
• Deposit Date: Aug 19, 2020
• Publicly Available Date: Aug 26, 2020
• Journal: IEEE Internet of Things Journal
• Electronic ISSN: 2327-4662
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 5
• Pages: 3211-3223
• DOI: https://doi.org/10.1109/jiot.2020.3019732
• Keywords: Device-Free Localization; Internet of Things; Machine Learning; Block; Sparse Coding; Multiple Targets
• Public URL: http://researchrepository.napier.ac.uk/Output/2682243