Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi

Wang, Tao; Yang, Dandan; Zhang, Shunqing; Wu, Yating; Xu, Shugong

doi:10.3390/s19102335

Cited by 39 publications

(14 citation statements)

References 18 publications

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“…In Fig. 7 (d), (e), the algorithm without Side-lobe Cancellation can detect the location of target (4,1, 2) and target (2,4). However, even after zeroing in Fig.…”

Section: B Multi-person Scenariomentioning

confidence: 95%

“…P Assive indoor localization without any device attached to the human has received great attention recently, which supports a growing list of location-based services and applications, including intrusion detection [1], [2], elderly monitoring [3], [4], and real-time positioning of criminals [5]. Visionbased technologies [6], [7] are limited by lighting conditions and privacy concerns for real-life deployment, while radar equipment [8]- [11] is too expensive for large-scale deployment.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Zhang¹,

Zhang²,

He³

et al. 2022

Preprint

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The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices. However, due to the fundamental limited spatial resolution of WiFi signals, it is still very difficult to achieve accurate localization with existing commodity WiFi devices. To tackle this problem, in this paper, we propose to exploit the degree of freedom provided by the Intelligent Reflecting Surface (IRS), which is composed of a large number of controllable reflective elements, to modulate the spatial distribution of WiFi signals and thus break down the spatial resolution limitation of WiFi signals to achieve accurate localization. Specifically, in the single-person scenario, we derive the closed-form solution to optimally control the phase shift of the IRS elements. In the multi-person scenario, we propose a Side-lobe Cancellation Algorithm to eliminate the near-far effect to achieve accurate localization of multiple persons in an iterative manner. Extensive simulation results demonstrate that without any change to the existing WiFi infrastructure, the proposed framework can locate multiple moving persons passively with sub-centimeter accuracy under multipath interference and random noise.

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“…In Fig. 7 (d), (e), the algorithm without Side-lobe Cancellation can detect the location of target (4,1, 2) and target (2,4). However, even after zeroing in Fig.…”

Section: B Multi-person Scenariomentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Zhang¹,

Zhang²,

He³

et al. 2022

Preprint

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show abstract

“…In general, Wi-Alarm [42] investigated a low computational cost WiFi-based intrusion detection system by eliminating data pre-processing cost. A WiFi-based elderly activity recognition system was demonstrated in Reference [12].…”

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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“…Specifically, they developed techniques for indoor respiration detection and movement direction estimation using commodity devices. A Passive Intrusion Detection system based on CSI amplitude variation is proposed in [26], however it is still built on the 802.11n technology. Similar to our work, CSI information is collected to train a SVM classifier aiming to distinguish between static and dynamic scenarios and triggers an alarm in case of dynamic scenarios.…”

Section: Related Workmentioning

confidence: 99%

PASID: Exploiting Indoor mmWave Deployments for Passive Intrusion Detection

Devoti

Sciancalepore²,

Filippini

et al. 2020

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications

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As 5G deployments start to roll-out, indoor solutions are increasingly pressed towards delivering a similar user experience. Wi-Fi is the predominant technology of choice indoors and major vendors started addressing this need by incorporating the mmWave band to their products. In the near future, mmWave devices are expected to become pervasive, opening up new business opportunities to exploit their unique properties.In this paper, we present a novel PASsive Intrusion Detection system, namely PASID, leveraging on already deployed indoor mmWave communication systems. PASID is a software module that runs in off-the-shelf mmWave devices. It automatically models indoor environments in a passive manner by exploiting regular beamforming alignment procedures and detects intruders with a high accuracy. We model this problem analytically and show that for dynamic environments machine learning techniques are a cost-efficient solution to avoid false positives. PASID has been implemented in commercial off-the-shelf devices and deployed in an office environment for validation purposes. Our results show its intruder detection effectiveness (∼ 99% accuracy) and localization potential (∼ 2 meters range) together with its negligible energy increase cost (∼ 2%).

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Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi

Cited by 39 publications

References 18 publications

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

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

PASID: Exploiting Indoor mmWave Deployments for Passive Intrusion Detection

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