Device-Free Home Intruder Detection and Alarm System Using Wi-Fi Channel State Information

Al-qaness, Mohammed A. A.; Li, Fangmin; Ma, Xiaolin; Liu, Guo

doi:10.18178/ijfcc.2016.5.4.468

Cited by 17 publications

(11 citation statements)

References 28 publications

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“…Support vector machine (SVM), as a low cost threshold based classification, is one of the most popular machine learning techniques. A considerable number of CSI-based detection systems [8,14,16,17,18] have applied SVM to classify different states and achieved high accuracy. Experiments show that SVM outperforms other machine leaning method like k-nearest neighbor (KNN) [17] and Bayesian algorithm [18].…”

Section: Methodsmentioning

confidence: 99%

“…Speed independent device-free entity detection (SIED) [7] extracts the distribution of the variance of variances of CSI among all the subcarriers as feature and leverages a probability technique hidden Markov model (HMM) as the classifier to make it more accurate in human detection, it performs well when the moving speed is very slow. WiGarde [16] was proposed to detect an intruder through door or window for home safety, in WiGarde, a naive Bayesian classifier was used to eliminate bad stream caused by surrounding electromagnetic noise, wavelet was used to get the width of the dynamic time window for extracting the best feature, one-class SVM was adopted to classify human intrusion. In [17], Zhou et al applied support vector classify (SVC) to solve the presence detection and applied support vector regression (SVR) to solve the localization problem through regression.…”

Section: Related Workmentioning

confidence: 99%

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

Wang

Yang

Zhang

et al. 2019

Sensors

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In this paper, we present a WiFi-based intrusion detection system called Wi-Alarm. Motivated by our observations and analysis that raw channel state information (CSI) of WiFi is sensitive enough to monitor human motion, Wi-Alarm omits data preprocessing. The mean and variance of the amplitudes of raw CSI data are used for feature extraction. Then, a support vector machine (SVM) algorithm is applied to determine detection results. We prototype Wi-Alarm on commercial WiFi devices and evaluate it in a typical indoor scenario. Results show that Wi-Alarm reduces much computational expense without losing accuracy and robustness. Moreover, different influence factors are also discussed in this paper.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Wang

Yang

Zhang

et al. 2019

Sensors

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“…Therefore, such streams must be eliminated. To this end, we use the bad stream elimination algorithm, which was described in the previous work [22], to eliminate bad streams. The bad stream elimination algorithm calculates the difference between the maximum peaks and valleys (Max-Min), the mean value (Means) and standard deviation (STD) as features in every spatial diversity.…”

Section: Preprocessingmentioning

confidence: 99%

Device-Free Indoor Activity Recognition System

Al-qaness

et al. 2016

Applied Sciences

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Abstract:In this paper, we explore the properties of the Channel State Information (CSI) of WiFi signals and present a device-free indoor activity recognition system. Our proposed system uses only one ubiquitous router access point and a laptop as a detection point, while the user is free and neither needs to wear sensors nor carry devices. The proposed system recognizes six daily activities, such as walk, crawl, fall, stand, sit, and lie. We have built the prototype with an effective feature extraction method and a fast classification algorithm. The proposed system has been evaluated in a real and complex environment in both line-of-sight (LOS) and none-line-of-sight (NLOS) scenarios, and the results validate the performance of the proposed system.

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“…Device-free detection can locate unknown targets without communication capabilities. The received signal strength (RSS) was first employed in the device-free detection in an indoor environment; e.g., Nuzzer [ 21 ]. Because of the low location accuracy and poor anti-interference of RSS, the CSI from the physical layer replaces the RSS in device-free detection.…”

Section: Introductionmentioning

confidence: 99%

Foreign Object Intrusion Detection on Metro Track Using Commodity WiFi Devices with the Fast Phase Calibration Algorithm

Xie

Zhou

et al. 2020

Sensors

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With continuous development in the scales of cities, the role of the metro in urban transportation is becoming more and more important. When running at a high speed, the safety of the train in the tunnel is significantly affected by any foreign objects. To address this problem, we propose a foreign object intrusion detection method based on WiFi technology, which uses radio frequency (RF) signals to sense environmental changes and is suitable for lightless tunnel environments. Firstly, based on extensive experiments, the abnormal phase offset between the RF chains of the WiFi network card and its offset law was observed. Based on this observation, a fast phase calibration method is proposed. This method only needs the azimuth information between the transmitter and the receiver to calibrate the the phase offset rapidly through the compensation of the channel state information (CSI) data. The time complexity of the algorithm is lower than the existing algorithm. Secondly, a method combining the MUSIC algorithm and static clutter suppression is proposed. This method utilizes the incoherence of the dynamic reflection signal to improve the efficiency of foreign object detection and localization in the tunnel with a strong multipath effect. Finally, experiments were conducted using Intel 5300 NIC in the indoor environment that was close to the tunnel environment. The performance of the detection probability and localization accuracy of the proposed method is tested.

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Device-Free Home Intruder Detection and Alarm System Using Wi-Fi Channel State Information

Cited by 17 publications

References 28 publications

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

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

Device-Free Indoor Activity Recognition System

Foreign Object Intrusion Detection on Metro Track Using Commodity WiFi Devices with the Fast Phase Calibration Algorithm

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