A Robust Passive Intrusion Detection System with Commodity WiFi Devices

Ding, Enjie; Li, Xiansheng; Zhao, Tong; Zhang, Lei; Hu, Yanjun

doi:10.1155/2018/8243905

Cited by 18 publications

(17 citation statements)

References 26 publications

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“…Time-domain Pros: relatively simple calculation; Cons: vulnerable to environmental changes and noise maximum, minimum, mean, standard deviation, kurtosis, skewness, variance, median and median absolute deviation, percentiles, root sum square, interquartile range [13,18,37,40,42,43,46,[48][49][50]62,72,73,75,76,[78][79][80][83][84][85][88][89][90]97,108,127,130], time lag [37], power decline ratio [37], amplitude sequence [20,54,74,91], phase sequence [39,51,107], CV [38] Frequency-domain Pros: capture the periodical characteristics of human motion; Cons: large amount of calculation FFT coefficient [12,83], dominant frequency [43], power spectrum density [88], spectral entropy [43,57,70,72,…”

Section: Category Pros and Cons Featuresmentioning

confidence: 99%

“…Template matching recognition is often training-free [10,19,20,39,55,74,91,93,94]. Training-once classification requires the valid features robust to the variations in the surrounding environment [12,13,32,37,40,42,44,45,[47][48][49]56,59,60,70,72,73,75,76,78,79,83,84,89,90,92,127,130]. Deep learning automatically extracts features, which often requires only one time of training [16,17,21,48,59,67,77,88,95,96,108].…”

Section: Activity Classificationmentioning

confidence: 99%

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Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.

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Section: Category Pros and Cons Featuresmentioning

confidence: 99%

Section: Activity Classificationmentioning

confidence: 99%

Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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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%

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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“…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%

“…1. Precision is defined as positive predictive value, mathematically described as: (38) where TP and FP are true positive and false positive respectively. True Positive (TP) is the probability that a model correctly predicts the positive class.…”

Section: Performance Evaluationmentioning

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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A Robust Passive Intrusion Detection System with Commodity WiFi Devices

Cited by 18 publications

References 26 publications

Human Activity Sensing with Wireless Signals: A Survey

Human Activity Sensing with Wireless Signals: A Survey

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

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

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