Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals

Lv, Jiguang; Man, Dapeng; Yang, Wu; Gong, Liangyi; Du, Xiaojiang; Yu, Miao

doi:10.3390/app9010175

Cited by 25 publications

(19 citation statements)

References 41 publications

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“…They described a correlation between a specific activity and human body movement speed. During the previous decades, WiFi-based micro-activity recognition [45] and intrusion detection systems [18,46] have been explored with very good performance. Wireless indoor localization has been permeated into an advanced era of life [47].…”

Section: Related Workmentioning

confidence: 99%

“…The corresponding constant offset is estimated by a long-term averaging over CSI stream [16]. The human activities have a relatively low frequency as compared to the noise [18]. Therefore, we filter out high frequencies from the received signal.…”

Section: Csi Pre-processing Modulementioning

confidence: 99%

“…During the previous few years, WiFi-based device-free localization, activity, and gesture recognition systems have been successfully used in various applications including assisted living, health monitoring, and emergency surveillance [15][16][17][18][19][20][21][22]. WiFi-based wireless scheme opened a new window for scientists to further investigate device-free activity recognition for the safety benefits of drivers.…”

Section: Introductionmentioning

confidence: 99%

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WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

Akhtar

Wang

2020

Sensors

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Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver’s activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93.1 % in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse scale of applications. In the realm of intelligent vehicles and assisted driving systems, the proposed wireless solution can effectively characterize the driving maneuvers, primary tasks, driver distraction, and fatigue by exploiting radio-image descriptors.

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Section: Related Workmentioning

confidence: 99%

Section: Csi Pre-processing Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

Akhtar

Wang

2020

Sensors

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“…A speed independent device-free entity detection system is proposed [50]. Reference [51] proposes a robust device-free intrusion detection system leveraging principle component analysis and a low pass filter to remove noise and continuous wavelet transform to extract the feature. AR-Alarm is investigated by utilizing the Doppler effects incurred by human motion on multiple Wi-Fi devices [52].…”

Section: ) Intrusion Detection and Othersmentioning

confidence: 99%

Leveraging TPDV to Direction Estimation With a Single Pair of Commodity Devices

et al. 2020

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In this paper, we propose an indoor passive human tracking system equipped with only one pair of commodity Wi-Fi devices, namely WiBlinker. The key insight is that the motion of moving target not only introduce Doppler frequency shift, but also changes the value of phase difference between two adjacent antennas of the receiver. First of all, a lightweight interpolation scheme is presented to remove the random phase noise in channel state information for improving the accuracy of the trajectory tracing. Instead of using AoA to obtain the moving direction, WiBlinker employs the trend of phase difference variation in direction estimation which is no need multiple Wi-Fi devices and easier to be achieved. Experimental results show that WiBlinker outperforms the conventional multiple Wi-Fi links methods, especially in the case of a sharp turn. INDEX TERMS Indoor human tracking, commodity devices, Angle-of-Arrival, Doppler frequency shift, channel state information.

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“…The emerging device-free localization and activity recognition relies on CSI for better characterization of WiFi signals influenced by the human activities [31][32][33][34]. In recent years, CSI based micro-activity recognition [35] and intrusion detection [36][37][38] systems have emerged with good recognition results.…”

Section: Related Workmentioning

confidence: 99%

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

Akhtar

Wang

2019

Applied Sciences

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In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.

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Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals

Cited by 25 publications

References 41 publications

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

Leveraging TPDV to Direction Estimation With a Single Pair of Commodity Devices

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

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