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

Huang, Hua; Zhao, Haoli; Li, Xiang; Ding, Shuxue; Zhao, Lingjun; Li, Zhenni

doi:10.1109/access.2018.2876034

Cited by 32 publications

(26 citation statements)

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“…Localization is performed using a Convolutional Neural Network (CNN), which is shown to outperform a traditional approach based on RSSI wavelet features and Bayes classification. Huang et al [5] model DFL as a spare representation problem which they solve using a variant of the iterative shrinkagethresholding algorithm. Zhang et al [24] implemented a parameterized extreme learning machine (ELM) approach to DFL which was shown to outperform existing WKNN, SVM and RTI techniques.…”

Section: A Fingerprintingmentioning

confidence: 99%

“…This can lead to high implementation cost due to the large number of sensors required, and the requirement of easily accessible power across the whole environment [1]- [4]. The second shortcoming is that DFL implementations also require a large number of offline measurements to calibrate the system to the target environment [5]- [10]. This restricts their usability, as standard end users cannot be expected to install new power sockets and undertake excessive calibration procedures to facilitate localization within their home.…”

Section: Introductionmentioning

confidence: 99%

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SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

et al. 2019

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Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density. INDEX TERMS Device-free localization (DFL), histogram distance, indoor positioning systems (IPS), smart homes, spring-relaxation.

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Section: A Fingerprintingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

et al. 2019

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“…However, the corresponding relationship of the location-RSS is not accessible directly. To solve this problem, many previous research works regarded the DFL problem as a classification problem, arranged the collected wireless signals into vectors, and then employed the machine learning methods [16,17] to extract features for classification. The commonly-used algorithms [18,19] include support vector machines (SVM), K-nearest-neighbor (KNN), sparse coding, and deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection Based on Device-Free Localization in the Era of IoT

Zhao

Huang

et al. 2019

Symmetry

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Device-free localization (DFL) locates targets without being equipped with the attached devices, which is of great significance for intrusion detection or monitoring in the era of the Internet-of-Things (IoT). Aiming at solving the problems of low accuracy and low robustness in DFL approaches, in this paper, we first treat the RSS signal as an RSS-image matrix and conduct a process of eliminating the background to dig out the variation component with distinguished features. Then, we make use of these feature-rich images by formulating DFL as an image classification problem. Furthermore, a deep convolutional neural network (CNN) is designed to extract features automatically for classification. The localization performance of the proposed background elimination-based CNN (BE-CNN) scheme is validated with a real-world dataset of outdoor DFL. In addition, we also validate the robust performance of the proposal by conducting numerical experiments with different levels of noise. Experimental results demonstrate that the proposed scheme has an obvious advantage in terms of improving localization accuracy and robustness for DFL. Particularly, the BE-CNN can maintain the highest localization accuracy of 100%, even in noisy conditions when the SNR is over −5 dB. The BE-based methods can outperform all the corresponding raw data-based methods in terms of the localization accuracy. In addition, the proposed method can outperform the comparison methods, deep neural network with autoencoder, K-nearest-neighbor (KNN), support vector machines (SVM), etc., in terms of the localization accuracy and robustness.

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“…Sparse coding is a method which concentrates on presenting sparse estimations from underdetermined linear measurements, based on predefined overcomplete vector sets. Utilizing sparse coding can perform parameter estimation and feature selection simultaneously, making it a powerful tool in processing high dimensional data, which is commonly appeared in biology, economy and industry [1]- [5]. The approach of sparse coding is implemented by choosing appropriate sparse regularization, which is the guarantee for obtaining results efficiently and accurately.…”

Section: Introductionmentioning

confidence: 99%

$\ell_P$ Norm Independently Interpretable Regularization Based Sparse Coding for Highly Correlated Data

Zhao

Ding

et al. 2019

IEEE Access

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Sparse coding, which aims at finding appropriate sparse representations of data with an overcomplete dictionary set, is a well-established signal processing methodology and has good efficiency in various areas. The varying sparse constraint can influence the performances of sparse coding algorithms greatly. However, commonly used sparse regularization may not be robust in high-coherence condition. In this paper, inspired from independently interpretable lasso (IILasso), which considers the coherence of sensing matrix columns in constraint to implement the strategy of selecting uncorrelated variables, we propose a new regularization by introducing p norm (0 < p < 1) into the regularization part of IILasso. The new regularization can efficiently enhance the performances in obtaining sparse and accurate coefficient. To solve the optimization problem with the new regularization, we propose to use the coordinate descent algorithm with weighted 1 norm, named independently interpretable weighted lasso (IIWLasso), and the proximal operator, named independently interpretable iterative shrinkage thresholding algorithm (II-ISTA) and independently interpretable proximal operator for 2 3 norm regularization (II2/3PO). We present synthetic data experiments and gene expression data experiments to validate the performance of our proposed algorithms. The experiment results show that all independently interpretable algorithms can perform better than their original ones in different coherence conditions. Among them, IIWLasso can obtain relatively best performance both in relative norm error and support error of synthetic data and misclassification error of tenfold cross-validating gene expression data. INDEX TERMS Sparse coding, highly correlated data, p norm, weighted 1 norm, independently interpretable weighted lasso.

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

Cited by 32 publications

References 50 publications

SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

Intrusion Detection Based on Device-Free Localization in the Era of IoT

$\ell_P$ Norm Independently Interpretable Regularization Based Sparse Coding for Highly Correlated Data

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