scholarly journals An Accurate and Efficient Device-Free Localization Approach Based on Sparse Coding in Subspace

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 61782-61799 ◽  
Author(s):  
Huakun Huang ◽  
Haoli Zhao ◽  
Xiang Li ◽  
Shuxue Ding ◽  
Lingjun Zhao ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Device Free ◽  
Localization Approach

DAFI

2021 ◽  
Vol 5 (4) ◽  
pp. 1-21
Author(s):  
Hang Li ◽  
Xi Chen ◽  
Ju Wang ◽  
Di Wu ◽  
Xue Liu
Keyword(s):  
Indoor Localization ◽  
Domain Adaptation ◽  
Target Domain ◽  
Localization Accuracy ◽  
Source Domain ◽  
Fine Grained ◽  
Time Signals ◽  
Environmental Variations ◽  
Device Free ◽  
Localization Approach

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

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

2020 ◽  
pp. 1-1
Author(s):  
Lingjun Zhao ◽  
Huakun Huang ◽  
Chunhua Su ◽  
Shuxue Ding ◽  
Huawei Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sparse Coding ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Device Free

scholarly journals Multi-targets device-free localization based on sparse coding in smart city

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985822
Author(s):  
Min Zhao ◽  
Danyang Qin ◽  
Ruolin Guo ◽  
Guangchao Xu
Keyword(s):  
Sparse Coding ◽  
Smart City ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Localization Accuracy ◽  
Threshold Algorithm ◽  
Single Target ◽  
Iterative Shrinkage ◽  
Device Free

With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city.

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

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 637 ◽  
Author(s):  
Huakun Huang ◽  
Zhaoyang Han ◽  
Shuxue Ding ◽  
Chunhua Su ◽  
Lingjun Zhao
Keyword(s):  
Intrusion Detection ◽  
Sparse Coding ◽  
State Of The Art ◽  
Optimization Method ◽  
Living Room ◽  
Full Potential ◽  
Wireless Devices ◽  
Proximal Operator ◽  
Art Methods ◽  
Device Free

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.

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