Improved Sparse Coding Algorithm with Device-Free Localization Technique for Intrusion Detection and Monitoring

Symmetry

Shuxue Ding¹  Huakun Huang²  Zhaoyang Han²  Lingjun Zhao²  Chunhua Su² 

[1] School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China;School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-0006, Japan;

关键词: intrusion detection;    device-free localization;    Internet-of-Things;    monitoring;    sparse coding;    l_2,1 norm;   

DOI  :  10.3390/sym11050637

来源: DOAJ

【 摘 要 】

Device-free localization (DFL) locates target in a wireless sensors network (WSN) without equipping with wireless devices or tags, which is an emerging technology in the fields of intrusion detection and monitoring. In order to achieve an accurate result of DFL, the conventional works adopt $l_1$ norm as a regularizer to take the full potential of sparsity for locating targets. Contrasting to the previous works, we exploit the $l_{2,1}$ norm as the regularizer and devise an efficient optimization method with a proximal operator-based scheme, which leads the proposed improved-sparse-coding algorithm with proximal operator (ISCPO). Compared with the state-of-the-art methods that adopt $l_1$ norm as the regularizer, the proposed algorithm can improve the joint sparsity of sparse solution. Experimental results on our real testbeds of indoor DFL show that, in scenarios of living room and corridor, the proposed approach can achieve high localization accuracies of about 100% and 90%, respectively. In addition, the proposed ISCPO algorithm outperforms the compared state-of-the-art methods and has a more robust performance in challenged environments for target localization.

【 授权许可】

Unknown