Robust WLAN-Based Indoor Intrusion Detection Using PHY Layer Information

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

doi:10.1109/access.2017.2785444

Cited by 49 publications

(22 citation statements)

References 41 publications

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“…This method tracks door intrusion by tracking the fluctuations of human motion in an indoor environment. Lv et al (2017) also presented an indoor intrusion system based on CSI. This system uses a hidden Markov model to classify human intrusion actions.…”

Section: Csimentioning

confidence: 99%

Device-free human micro-activity recognition method using WiFi signals

Al-qaness

2019

Geo-spatial Information Science

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Human activity tracking plays a vital role in human-computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macro-and micro-activities. Recently, wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment. This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals. Different from existed CSI-based microactivity recognition methods, the proposed method extracts both amplitude and phase information from CSI, thereby providing more information and increasing detection accuracy. The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity. We applied a machine learning algorithm to recognize the proposed micro-activities. The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users.

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Section: Csimentioning

confidence: 99%

Device-free human micro-activity recognition method using WiFi signals

Al-qaness

2019

Geo-spatial Information Science

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“…Over the years, researchers have proposed methods that realize RF-based device-free localization from many different approaches including radio grid, fingerprinting, RF backscatter and radar. Methods based on radio-grid use multiple transceivers to form a radio grid and detecting the target by analyzing the attenuation between the links [28,29]. A training phase is often required to calibrate the parameters of the links.…”

Section: Related Workmentioning

confidence: 99%

“…Methods based on radio-grid use multiple transceivers to form a radio grid and detecting the target by analyzing the attenuation between the links [28,29]. A training phase is often required to calibrate the parameters of the links.…”

Section: Related Workmentioning

confidence: 99%

WallSense: Device-Free Indoor Localization Using Wall-Mounted UHF RFID Tags

Liu

Wang

et al. 2018

Sensors

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To achieve device-free indoor localization without the active participation of the users, this paper presents WallSense, a device-free indoor localization system based on off-the-shelf Radio RFID (Radio-Frequency Identification) equipment. The system deploys two orthogonal tag arrays in adjoining walls and uses the RSSI and phase information measured by RFID readers to localize the target. By differentiating the backscattered signal between adjacent tag pairs, WallSense is able to eliminate most undesirable factors and extract information directly related to the location of the target. By applying Particle Swarm Optimization (PSO) with a novel weighted fitness function and combining the localization result of two orthogonal tag arrays, the system is able to localize the target with high accuracy. Experiments show that the system is able to localize human target with 0.24 m median error. Also, WallSense has low deployment overhead and do not require the users to carry any devices.

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“…Most WiFi-based DFP approaches take advantage of the transmission characteristics of opportunistic WiFi signal in wireless channels, such as a received signal strength indicator (RSSI) [5] and channel state information (CSI) [6,7,8,9], to capture changes in a region [10]. RSSI suffers from dramatic performance degradation due to multipath fading [11].…”

Section: Introductionmentioning

confidence: 99%

“…Among existing CSI-based systems of intrusion detection, considerable effort has been devoted to data preprocessing. Some do preprocessing to gain useful phase information [8,9], others for de-noising [6,7]. However, through our analysis and experiment observations, we think that although the raw phase of CSI is meaningless [12], the raw amplitude of CSI is informative and relatively robust.…”

Section: Introductionmentioning

confidence: 99%

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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Robust WLAN-Based Indoor Intrusion Detection Using PHY Layer Information

Cited by 49 publications

References 41 publications

Device-free human micro-activity recognition method using WiFi signals

Device-free human micro-activity recognition method using WiFi signals

WallSense: Device-Free Indoor Localization Using Wall-Mounted UHF RFID Tags

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

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