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.
Citations
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01 Aug 2019
TL;DR: A new passive human identification method named HumanFi based on fine-grained gait patterns captured by commercial WiFi device and long short term memory network (LSTM) to solve the effects of short-term anomalous fluctuation.
Abstract: Because of the uniqueness of human gait, the WiFi signal reflected by a walking person can generate a distinctive variation in the received WiFi channel state information (CSI). In this paper, we present a new passive human identification method named HumanFi based on fine-grained gait patterns captured by commercial WiFi device and long short term memory network (LSTM). Firstly, CSI measurements are collected by a commercial WiFi device, and then a buffer and filtering mechanism-based gait detection algorithm is proposed to solve the effects of short-term anomalous fluctuation. After that, a recurrent neural network, LSTM, is used to identify different people by discriminating the temporal characteristics of automatically extracted human gait features. We evaluated the proposed HumanFi using a dataset with 1920 gait instances collected from 24 human subjects walking in two different scenes. Experimental results showed that HumanFi achieved more than 96% human identification accuracy, which demonstrated the good performance of HumanFi on non-intrusive human identification tasks.
8 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...[9] proposed a channel state information (CSI) extraction method using Intel 5300 NIC in 2011....
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25 Oct 2021
TL;DR: In this paper, a 3D human pose tracking system for free-form activity using commodity WiFi devices is presented, where the authors first identify the moving limbs by leveraging the signals reflected off the human body and separate the entangled signals for each limb.
Abstract: WiFi human sensing has become increasingly attractive in enabling emerging human-computer interaction applications. The corresponding technique has gradually evolved from the classification of multiple activity types to more fine-grained tracking of 3D human poses. However, existing WiFi-based 3D human pose tracking is limited to a set of predefined activities. In this work, we present Winect, a 3D human pose tracking system for free-form activity using commodity WiFi devices. Our system tracks free-form activity by estimating a 3D skeleton pose that consists of a set of joints of the human body. In particular, Winect first identifies the moving limbs by leveraging the signals reflected off the human body and separates the entangled signals for each limb. Then, our system tracks each limb and constructs a 3D skeleton of the body by modeling the inherent relationship between the movements of the limb and the corresponding joints. Our evaluation results show that Winect achieves centimeter-level accuracy for free-form activity tracking under various environments.
8 citations
30 Nov 2020
TL;DR: RF-Veil is proposed, a radiometric fingerprinting solution that not only is robust against impersonation attacks but also protects user privacy by obfuscating the radiometric fingerprints of the transmitter for non-legitimate receivers.
Abstract: The intrinsic hardware imperfection of WiFi chipsets manifests itself in the transmitted signal, leading to a unique radiometric fingerprint. This fingerprint can be used as an additional means of authentication to enhance security. In fact, recent works propose practical fingerprinting solutions that can be readily implemented in commercial-off-the-shelf devices. In this paper, we prove analytically and experimentally that these solutions are highly vulnerable to impersonation attacks. We also demonstrate that such a unique device-based signature can be abused to violate privacy by tracking the user device, and, as of today, users do not have any means to prevent such privacy attacks other than turning off the device. We propose RF-Veil, a radiometric fingerprinting solution that not only is robust against impersonation attacks but also protects user privacy by obfuscating the radiometric fingerprint of the transmitter for non-legitimate receivers. Specifically, we introduce a randomized pattern of phase errors to the transmitted signal such that only the intended receiver can extract the original fingerprint of the transmitter. In a series of experiments and analyses, we expose the vulnerability of adopting naive randomization to statistical attacks and introduce countermeasures. Finally, we show the efficacy of RF-Veil experimentally in protecting user privacy and enhancing security. More importantly, our proposed solution allows communicating with other devices, which do not employ RF-Veil.
8 citations
Cites background from "Tool release: gathering 802.11n tra..."
...Recently, CSI-based radiometric fingerprinting gained popularity due to the availability of CSI extraction tools [14, 17, 38]....
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01 Oct 2018
TL;DR: This paper describes WiCapture, a novel approach which leverages commodity WiFi infrastructure, which is ubiquitous today, for tracking purposes, and demonstrates that it achieves an accuracy of 0.88 cm compared to sophisticated infrared-based tracking systems like the Oculus Rift.
Abstract: Today, experiencing virtual reality (VR) is a cumbersome experience which either requires dedicated infrastructure like infrared cameras to track the headset and hand-motion controllers (e.g. Oculus Rift, HTC Vive), or provides only 3-DoF (Degrees of Freedom) tracking which severely limits the user experience (e.g. Samsung Gear VR). To truly enable VR everywhere, we need position tracking to be available as a ubiquitous service. This paper describes WiCapture, a novel approach which leverages commodity WiFi infrastructure, which is ubiquitous today, for tracking purposes. We prototyped WiCapture using off-the-shelf WiFi radios and demonstrated that it achieves an accuracy of 0.88 cm compared to sophisticated infrared-based tracking systems like the Oculus Rift, while providing much higher range, resistance to occlusion, ubiquity and ease of deployment.
8 citations
11 Mar 2016
TL;DR: This paper presents a device-free human tracking system only using existing commercial WiFi supported devices and smart appliances, and considers that a human walking trajectory consists of a series of moving behaviors and can determine the trajectory by detect those behaviors.
Abstract: Human tracking in indoor environment plays an important role in intelligent housing system that enables the smart appliances to provide fine-grained services with the information of users' walking trajectories. This paper presents a device-free human tracking system only using existing commercial WiFi supported devices and smart appliances. We consider that a human walking trajectory consists of a series of moving behaviors and we can determine the trajectory by detect those behaviors. Those behaviors can be sensed according to their influence on the signals of neighboring WiFi devices (we call these devices Check Points). A machine learning method is used to train a classifier to identify the above behaviors. Furthermore, an uniform trace recording format consisting of the moving behaviors series, is designed for thewalking trajectories comparison. By calculating the similarity between the measured human trace and stored trace profiles, we can determine the specific trajectory. Our experimental evaluation in three different scenarios shows that our approach can achieve over 95% average accuracy on moving behaviors identification and over 90% average accuracy on trace determination.
8 citations
References
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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....
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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
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