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Journal ArticleDOI

Tool release: gathering 802.11n traces with channel state information

22 Jan 2011-Vol. 41, Iss: 1, pp 53-53
TL;DR: The measurement setup comprises the customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab scripts for data analysis.
Abstract: We are pleased to announce the release of a tool that records detailed measurements of the wireless channel along with received 802.11 packet traces. It runs on a commodity 802.11n NIC, and records Channel State Information (CSI) based on the 802.11 standard. Unlike Receive Signal Strength Indicator (RSSI) values, which merely capture the total power received at the listener, the CSI contains information about the channel between sender and receiver at the level of individual data subcarriers, for each pair of transmit and receive antennas.Our toolkit uses the Intel WiFi Link 5300 wireless NIC with 3 antennas. It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel. The measurement setup comprises our customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab (or Octave) scripts for data analysis. We are releasing the binary of the modified firmware, and the source code to all the other components.

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Citations
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Journal ArticleDOI
TL;DR: In this article , a device-free human continuous activity recognition system based on WiFi CSI is proposed, where the CSI phase difference expansion matrix is constructed as a more obvious activity recognition feature, and a method based on threshold combined with labeling is used to achieve continuous activity segmentation.
Abstract: Recently, device-free human activity recognition has become a research hotspot, and great progress has been made in ubiquitous computing. Among the different kinds of implementations, activity recognition based on WiFi channel state information (CSI) has attracted enormous attention for its superiority compared with conventional approaches. In this article, a device-free human continuous activity recognition system based on WiFi CSI is proposed. First, the CSI phase difference expansion matrix is constructed as a more obvious activity recognition feature, and a method based on threshold combined with labeling is used to achieve continuous activity segmentation. Then, the Gaussian mixture model–hidden Markov model (GMM–HMM) is used to model the CSI feature data of each activity, which is originally used for human 3-D skeleton-based activity recognition. The approach is of great value not only for its high accuracy compared with other classification approaches, such as long short-term memory (LSTM) and convolutional neural network (CNN), but also for its tremendous advantage that a pretty short CSI time series could be used to identify human activities, thus saving computer memory, reducing system calculation time greatly, and improving the error tolerance rate of the segmentation. Experiments on measured activity datasets and methods comparison demonstrate the effectiveness and superiority of the proposed system. The factors affecting system performance, such as the length of the CSI time series, have been discussed in this article.

5 citations

Journal ArticleDOI
TL;DR: In this article , the authors review the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges.
Abstract: WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users’ bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the technology and discusses its use-cases from a perspective that considers hardware, advanced signal processing, and data acquisition.

5 citations

Proceedings ArticleDOI
01 Oct 2018
TL;DR: This work designs a quantization preprocessing as well as an online quantization scheme which favours i.i.d. and uniform distribution of the generated values to achieve high entropy and key rates, and calculates the resulting mutual information between communication partners in a large, realistic measurement study.
Abstract: Quantization, and the fact that channel characteristics are independent and identically distributed so far have received only little attention in reports about actual implementations of physical layer key generation schemes. They are merely assumed for channel reciprocity based key generation, although the secret key generation significantly relies on them.We set out to design a quantization preprocessing as well as an online quantization scheme which favours i.i.d. and uniform distribution of the generated values to achieve high entropy and key rates, and calculate the resulting mutual information between communication partners in a large, realistic measurement study. Our experiments indicate a remarkable increase in mutual information, and underline the applicability to various quantization and key generation schemes.

5 citations


Cites methods from "Tool release: gathering 802.11n tra..."

  • ...Since efforts like [11] make CSI available on COTS hardware, we will focus on the usage of CSI....

    [...]

Journal ArticleDOI
25 Mar 2021-Sensors
TL;DR: In this article, an adaptive real-time intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers is proposed.
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.

5 citations

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed an activity-related feature extraction and enhancement (AFEE) method and matching network to enhance channel state information (CSI)-based human activity recognition (HAR).
Abstract: Deep learning has demonstrated its great potential in channel state information (CSI)-based human activity recognition (HAR), and hence has attracted increasing attention in both the industry and academic communities. While promising, most existing high-accuracy methodologies require to retrain their models when applying the previous-trained ones to a new/unseen environment. This issue has limited their practical usabilities. In order to overcome this challenge, this article proposes an innovative scheme, which combines an activity-related feature extraction and enhancement (AFEE) method and matching network (AFEE-MatNet). The proposed scheme is “one-fits-all,” meaning that the trained model can be directly applied in new/unseen environments without any retraining. We introduce the AFEE method to enhance CSI quality by eliminating noise. Specifically, the approach mitigates environmental noises unrelated to activity while better compressing and preserving the behavior-related information. Moreover, the size of feature signals generated by AFEE are reduced, which in turn significantly shortens the training time. For effective feature extraction, we propose to use the MatNet architecture to learn transferable features shared among source environments. To further improve the recognition performance, we introduce a prediction checking and correction scheme to rectify some classification errors that do not abide by the state transition of human behaviors. Extensive experimental results demonstrate that our proposed AFEE-MatNet significantly outperforms existing state-of-the-art HAR methods, in terms of both recognition accuracy and training time.

5 citations

References
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Proceedings ArticleDOI
30 Aug 2010
TL;DR: It is shown that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide, and the rate prediction is as good as the best rate adaptation algorithms for 802.
Abstract: RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (

697 citations


"Tool release: gathering 802.11n tra..." refers methods in this paper

  • ...It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel....

    [...]

Journal ArticleDOI
01 Oct 2001
TL;DR: The Internet is going mobile and wireless, perhaps quite soon, with a number of diverse technologies leading the charge, including, 3G cellular networks based on CDMA technology, a wide variety of what is deemed 2.5G cellular technologies (e.g., EDGE, GPRS and HDR), and IEEE 802.11 wireless local area networks (WLANs).
Abstract: At some point in the future, how far out we do not exactly know, wireless access to the Internet will outstrip all other forms of access bringing the freedom of mobility to the way we access the we...

615 citations

Journal ArticleDOI
07 Jan 2010
TL;DR: This tutorial provides a brief introduction to multiple antenna techniques, and describes the two main classes of those techniques, spatial diversity and spatial multiplexing.
Abstract: The use of multiple antennas and MIMO techniques based on them is the key feature of 802.11n equipment that sets it apart from earlier 802.11a/g equipment. It is responsible for superior performance, reliability and range. In this tutorial, we provide a brief introduction to multiple antenna techniques. We describe the two main classes of those techniques, spatial diversity and spatial multiplexing. To ground our discussion, we explain how they work in 802.11n NICs in practice.

89 citations