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Journal ArticleDOI

Tool release: gathering 802.11n traces with channel state information

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TLDR
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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Dynamic-MUSIC: accurate device-free indoor localization

TL;DR: MaTrack proposes a novel Dynamic-MUSIC method to detect the subtle reflection signals from human body and further differentiate them from those reflected signals from static objects to identify the human target's angle for localization.
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ProxiMate: proximity-based secure pairing using ambient wireless signals

TL;DR: ProxiMate, a system that allows wireless devices in proximity to securely pair with one another autonomously by generating a common cryptographic key directly from their shared time-varying wireless environment, is presented.
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IndoTrack: Device-Free Indoor Human Tracking with Commodity Wi-Fi

TL;DR: Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.
Journal ArticleDOI

SignFi: Sign Language Recognition Using WiFi

TL;DR: SignFi is able to recognize 276 sign gestures, which involve the head, arm, hand, and finger gestures, with high accuracy, using Channel State Information measured by WiFi packets as the input and a Convolutional Neural Network as the classification algorithm.
Proceedings ArticleDOI

WiFinger: talk to your smart devices with finger-grained gesture

TL;DR: WiFinger is deigned to recognize a set of finger-grained gestures, which are further used to realize continuous text input in off-the-shelf WiFi devices, and achieves up to 90.4% average classification accuracy for recognizing 9 digits finger- grained gestures from American Sign Language (ASL).
References
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Proceedings ArticleDOI

Predictable 802.11 packet delivery from wireless channel measurements

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.
Journal ArticleDOI

ACM SIGCOMM computer communication review

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).
Journal ArticleDOI

802.11 with multiple antennas for dummies

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.
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