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
Journal ArticleDOI
TL;DR: MoBreath as mentioned in this paper uses the channel state information (CSI) readings extracted from the end-user device, a smartphone, to monitor the respiration rate for the first time.
Abstract: Respiration rate is an essential vital indicator for health monitoring. While traditional sensor-based methods support acceptable sensing performance, the recent advance in wireless sensing could enable sensor-free and contact-free respiration sensing, which is particularly important during the practice of social distancing against a pandemic like COVID-19. Among a variety of wireless technologies employed for respiration sensing, Wi-Fi-based solutions are most popular due to the pervasive development of infrastructure. However, the existing Wi-Fi-based approaches need to retrieve Wi-Fi readings from access points, which are not often accessible for the end users. In this article, we propose a novel system, MoBreath, in which we utilize the Wi-Fi channel state information (CSI) readings extracted from the end-user device, a smartphone, to monitor the respiration rate for the first time. We introduce and address unique technical challenges, such as selecting the optimum CSI subcarriers from many noisy candidates and providing smartphone placement strategies for both single and multiple human target scenarios based on the Fresnel zone model to support highly accurate respiration sensing. Our evaluation of MoBreath using commodity smartphones in different environments shows that it can accurately estimate the respiration rate at a low error rate of 0.34 breaths per minute and support the sensing range of up to 3–4 m. Even for challenging scenarios such as the target is covered by a quilt and multiple targets are in the sensing area, MoBreath can still support highly accurate results.

8 citations

Patent
23 Nov 2016
TL;DR: In this paper, a method of detecting the walking direction of a person through utilization of radio frequency signals was proposed, where a signal transmitting device Tx and two signal receiving devices R*1 and R*2 are respectively placed at known fixed positions.
Abstract: The invention discloses a method of detecting the walking direction of a person through utilization of radio frequency signals A signal transmitting device Tx and two signal receiving devices R*1 and R*2 are respectively placed at known fixed positions; A connecting line between the Tx and R*1 is as perpendicular to a connecting line between Tx and R*2 as possible, and no block exists between the device pairs of Tx-R*1 and Tx-R*2 ; signals are subjected to OFDM modulation, and information of signal strength changes is obtained by measuring channel state information; and multi-frequency wireless channel changes are extracted through measuring radio frequency (RF) signals so as to obtain the walking direction of the person through calculating The technical scheme of the invention is not limited by the fact in sensing methods based on sensing devices that the walking direction of a person can only be detected when the person carries a sensing device The method can use existing wireless channel CSI values, so that no hardware change in wireless transceivers is needed, and the cost is saved The devices of the method can continuously operate in real time, and the method is advantaged by being convenient and practical, being small in detection error and high in precision

7 citations

DOI
01 Feb 2018
TL;DR: A self-adaptive MUSIC algorithm is proposed, which improves the accuracy of the angle of the indoor wireless device by eliminating the phase offset in channel state information (CSI), and designs different types’ detection algorithm of Sybil attacks and spoofing attacks based on different Sybil attack models.
Abstract: Single authentication mechanisms and broadcast characteristics of wireless networks make the Access Point (AP) vulnerable to spoofing attacks and Sybil attacks. However, Sybil attacks seriously affect network performance. Sybil nodes act with different identity, and prevent the normal clients from transmission. In this paper, a self-adaptive MUSIC algorithm is proposed, which improves the accuracy of the angle of the indoor wireless device by eliminating the phase offset in channel state information (CSI), and designs different types’ detection algorithm of Sybil attacks and spoofing attacks based on different Sybil attack models. And we experiment on mobile and commercial WiFi devices. The average detection error of angle is below 6.3°. After combining analysis of received signal strength indicator (RSSI), our detection algorithm can effectively detect whether the nodes launched by Sybil attacks, and the identity of other clients disguised by spoofing attacks. According to the experimental results, the scheme can distinguish the Sybil clients and the normal clients accurately, and the average success rate of the Sybil attack detection system is 98.5%.

7 citations


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

  • ...Recently, Halperin [8] obtain the the channel state information (CSI) by modifying the Intel 5300 network card driver from the ordinary WiFi device....

    [...]

  • ...We installed CSI tools[8] on these miniPCs, which can receive CSI of 30 subcarriers....

    [...]

19 Aug 2020
TL;DR: The advent of GPS positioning at the turn of the millennium provided consumers with worldwide access to outdoor location information, but for the purposes of indoor positioning, the GPS signal rarely penetrates buildings well enough to maintain the same level of positioning granularity as outdoors.
Abstract: The advent of GPS positioning at the turn of the millennium provided consumers with worldwide access to outdoor location information. For the purposes of indoor positioning, however, the GPS signal rarely penetrates buildings well enough to maintain the same level of positioning granularity as outdoors. Arriving around the same time, wireless local area networks (WLAN) have gained widespread support both in terms of infrastructure deployments and client proliferation. A promising approach to bridge the location context then has been positioning based on WLAN signals. In addition to being readily available in most environments needing support for location information, the adoption of a WLAN positioning system is financially low-cost compared to dedicated infrastructure approaches, partly due to operating on an unlicensed frequency band. Furthermore, the accuracy provided by this approach is enough for a wide range of location-based services, such as navigation and location-aware advertisements. In spite of this attractive proposition and extensive research in both academia and industry, WLAN positioning has yet to become the de facto choice for indoor positioning. This is despite over 20 000 publications and the foundation of several companies. The main reasons for this include: (i) the cost of deployment, and redeployment, which is often significant, if not prohibitive, in terms of work hours; (ii) the complex propagation of the wireless signal, which – through interaction with the environment – renders it inherently stochastic;

7 citations

Journal ArticleDOI
TL;DR: A pretrained convolutional neural network optimized for binary classification of the spectrogram images of the fall and nonfall motions is used and it is confirmed that the proposed method outperforms the conventional one and reaches over 0.92 accuracy.
Abstract: Wi-Fi channel state information (CSI)-based fall detection systems have a great potential compared with other alternatives since they are nonintrusive and nonspace limited. However, in the conventional work on Wi-Fi CSI-based fall detection, a phenomenon is commonly observed: the classification performance degrades when data in different environments are used for learning and testing. Nonetheless, when the signal-to-noise-power ratio (SNR) is small, the conventional methods cannot capture features of motion and cannot segment signals accurately. Therefore, there is a need to address these problems in order to build a robust fall detection system. In this article, we propose a spectrogram-image-based fall detection using Wi-Fi CSI. Unlike the conventional method, CSI is segmented with a certain sliding-time window, and then the classifier detects fall by using the spectrogram image generated from the segmented CSI. We use a pretrained convolutional neural network (CNN) optimized for binary classification of the spectrogram images of the fall and nonfall motions. We carried out experiments to evaluate the classification performance of our proposed method against the conventional one by using motion data in two different rooms for learning and testing. As a result, we confirmed that our proposed method outperforms the conventional one and reaches over 0.92 accuracy. In addition, compared with the conventional method, the fall detection performance of our method does not degrade even when using different environment data for learning and testing.

7 citations

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