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

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 the 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 an 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 the 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.

Automated summary

Device-free localization (DFL) is an emerging technology under the Internet-of-Things architecture with applications in intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning algorithms face challenges of low localization accuracy and weak dependability. To address these issues, a dependable block-sparse scheme named block-sparse coding with the proximal operator (BSCPO) is proposed in this work, showing improved robustness and accuracy in noisy conditions compared to state-of-the-art DFL methods.