scispace - formally typeset
Search or ask a question
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.

Content maybe subject to copyright    Report

Citations
More filters
Proceedings ArticleDOI
01 Oct 2018
TL;DR: This work proposes WiFit, a bodyweight exercises monitoring system that supports accurate repetition counting using a pair of commodity Wi-Fi devices without attaching anything to the human body, and develops an impulse-based method to segment and count the number of repeats.
Abstract: Bodyweight exercises are effective and efficient ways to improve one's balance, flexibility, and strength without machinery or extra equipment. Prior works have been successful in monitoring aerobic exercises and free-weight exercises, but are not suitable for ubiquitous bodyweight exercise monitoring in order to provide fine-grained repetition counting information in each exercise set. In this work, we propose WiFit, a bodyweight exercises monitoring system that supports accurate repetition counting using a pair of commodity Wi-Fi devices without attaching anything to the human body. We first analyze the movement patterns of bodyweight exercises and couple them with detailed Doppler effect modeling to determine the most effective system settings. Then, by leveraging the human activity Doppler displacement stream extracted from Wi-Fi CSI signal, we have developed an impulse-based method to segment and count the number of repetitions, and analyzed specific features for classifying different types of bodyweight exercises. Extensive experiments show that WiFit achieves 99% accuracy for repetition counting and 95.8% accuracy for exercise type classification.

15 citations

Journal ArticleDOI
TL;DR: In this paper, a machine learning method, Gaussian Mixture Hidden Markov Model (GMM-HMM), was used for device-free activity recognition using WiFi channel state information (CSI).
Abstract: This paper presents a machine learning method, Gaussian Mixture Hidden Markov Model (GMM-HMM), for device-free activity recognition using WiFi channel state information (CSI). The basic concept of CSI is introduced and signal changes caused by human activity are described, which demonstrates that human activity can be identified using a unique mapping between action and signal variations. The phase difference expanded matrix is built by the mean and standard deviation of phase difference as feature matrix after linear correction and Savitzky-Golay filter is performed on the CSI raw phase information. The GMM-HMM is used for classification as the human activity can be modeled as the Markov process and the complex activity patterns can be fitted by multiple Gaussian density functions, respectively. The proposed system is verified on the self-collected datasets and several factors affecting the recognition accuracy are analyzed. Furthermore, the system has compared with the previous work. High accuracy and robustness in universal scenarios are realized. Experimental results show that the average recognition accuracy of the proposed system is over 97%.

15 citations

Proceedings ArticleDOI
21 Sep 2020
TL;DR: SDR-Lite is presented, the first zero-cost, software-only software defined radio (SDR) receiver that empowers commodity WiFi to retrieve the In-phase and Quadrature of an ambient signal and shows that the reconstructed signal closely reassembles the original ambient signal.
Abstract: With the explosive increase in wireless devices, physical-layer signal analysis has become critically beneficial across distinctive domains including interference minimization in network planning, security and privacy (e.g., drone and spycam detection), and mobile health with remote sensing. While SDR is known to be highly effective in realizing such services, they are rarely deployed or used by the end-users due to the costly hardware ~1K USD (e.g., USRP). Low-cost SDRs (e.g., RTL-SDR) are available, but their bandwidth is limited to 2-3 MHz and operation range falls well below 2.4 GHz - the unlicensed band holding majority of the wireless devices. This paper presents SDR-Lite, the first zero-cost, software-only software defined radio (SDR) receiver that empowers commodity WiFi to retrieve the In-phase and Quadrature of an ambient signal. With the full compatibility to pervasively-deployed WiFi infrastructure (without any change to the hardware and firmware), SDR-Lite aims to spread the blessing of SDR receiver functionalities to billions of WiFi users and households to enhance our everyday lives. The key idea of SDR-Lite is to trick WiFi to begin packet reception (i.e., the decoding process) when the packet is absent, so that it accepts ambient signals in the air and outputs corresponding bits. The bits are then reconstructed to the original physical-layer waveform, on which diverse SDR applications are performed. Our comprehensive evaluation shows that the reconstructed signal closely reassembles the original ambient signal (>85% correlation). We extensively demonstrate SDR-Lite effectiveness across seven distinctive SDR receiver applications under three representative categories: (i) RF fingerprinting, (ii) spectrum monitoring, and (iii) (ZigBee) decoding. For instance, in security applications of drone and rogue WiFi AP detection, SDR-Lite achieves 99% and 97% accuracy, which is comparable to USRP.

14 citations

Proceedings ArticleDOI
11 Mar 2019
TL;DR: This paper tries to answer the question, "How do the authors install a transmitter-receiver pair?" based on an analysis of the experimental results and provides open discussions regarding the optimum installation of WLAN sensing devices.
Abstract: In recent years, the functionality of wireless communications has not only been limited to communication between users, but has also been extended to sensing applications. In fact, several WLAN-based sensing technologies, which rely on changes in multipath radio propagation derived from a channel state information (CSI) in indoor environments, have already been developed. In this paper, we present a WLAN-based outdoor human detection system – the first in IEEE 802.11ac WLAN CSI-based outdoor sensing attempts. The number of multipaths in an outdoor environment is limited, thus, making sensing difficult. To realize outdoor CSI sensing, we present an IEEE 802.11ac WLAN-based sensing system. Specifically, this paper tries to answer the question, "How do we install a transmitter-receiver pair?" based on an analysis of the experimental results. Moreover, we provide open discussions regarding the optimum installation of WLAN sensing devices.

14 citations

Posted Content
TL;DR: This paper proposes a simple yet efficient deep convolutional neural network, i.e., Temporal Unet, for WiFi-based sample-level action recognition, which is a critical technique in precise action localization, continuous action segmentation, and real-time action recognition.
Abstract: Human doing actions will result in WiFi distortion, which is widely explored for action recognition, such as the elderly fallen detection, hand sign language recognition, and keystroke estimation. As our best survey, past work recognizes human action by categorizing one complete distortion series into one action, which we term as series-level action recognition. In this paper, we introduce a much more fine-grained and challenging action recognition task into WiFi sensing domain, i.e., sample-level action recognition. In this task, every WiFi distortion sample in the whole series should be categorized into one action, which is a critical technique in precise action localization, continuous action segmentation, and real-time action recognition. To achieve WiFi-based sample-level action recognition, we fully analyze approaches in image-based semantic segmentation as well as in video-based frame-level action recognition, then propose a simple yet efficient deep convolutional neural network, i.e., Temporal Unet. Experimental results show that Temporal Unet achieves this novel task well. Codes have been made publicly available at this https URL.

14 citations


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

  • ...1 Overview Channel state information [51] is used as WiFi distortion for sample-level action recognition, which is comprised of the information of all Orthogonal Frequency Division Multiplexing [52] carriers between the WiFi sender and the WiFi receiver....

    [...]

References
More filters
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