Develop an Adaptive Real-Time Indoor Intrusion Detection System Based on Empirical Analysis of OFDM Subcarriers
Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-di...
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| Published in | Sensors (Basel, Switzerland) Vol. 21; no. 7; p. 2287 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
25.03.2021
MDPI |
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| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s21072287 |
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| Abstract | Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder’s motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions. |
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| AbstractList | Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder’s motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions. Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder's motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions.Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder's motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions. |
| Author | Gao, Chunming Dai, Dong Shen, Yixian Zhuang, Wei Li, Lu |
| AuthorAffiliation | 3 School of Engineering & Technology, University of Washington, Tacoma, WA 98402, USA; chunming@uw.edu 1 School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China; syx@nuist.edu.cn (Y.S.); lilu7qi@163.com (L.L.) 2 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing 210044, China 4 School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China; daidong@seu.edu.cn |
| AuthorAffiliation_xml | – name: 2 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing 210044, China – name: 3 School of Engineering & Technology, University of Washington, Tacoma, WA 98402, USA; chunming@uw.edu – name: 4 School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China; daidong@seu.edu.cn – name: 1 School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China; syx@nuist.edu.cn (Y.S.); lilu7qi@163.com (L.L.) |
| Author_xml | – sequence: 1 givenname: Wei surname: Zhuang fullname: Zhuang, Wei – sequence: 2 givenname: Yixian surname: Shen fullname: Shen, Yixian – sequence: 3 givenname: Lu surname: Li fullname: Li, Lu – sequence: 4 givenname: Chunming orcidid: 0000-0001-7428-5166 surname: Gao fullname: Gao, Chunming – sequence: 5 givenname: Dong surname: Dai fullname: Dai, Dong |
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| Copyright | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 by the authors. 2021 |
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| Keywords | OFDM subcarriers indoor intrusion detection Wi-Fi sensing device-free detection |
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| SubjectTerms | Algorithms Commodities Design device-free detection Eigenvalues Human mechanics indoor intrusion detection Intrusion detection systems Network interface cards OFDM subcarriers Personal computers Sensors Systems design Wi-Fi sensing |
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| Title | Develop an Adaptive Real-Time Indoor Intrusion Detection System Based on Empirical Analysis of OFDM Subcarriers |
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