Journal ArticleDOI
Tool release: gathering 802.11n traces with channel state information
Daniel Halperin,Wenjun Hu,Anmol Sheth,David Wetherall +3 more
- Vol. 41, Iss: 1, pp 53-53
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.read more
Citations
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Proceedings ArticleDOI
Exploiting Residual Channel for Implicit Wi-Fi Backscatter Networks
Taekyung Kim,Wonjun Lee +1 more
TL;DR: A flicker detector is proposed that achieves per-symbol in-band backscatter by exploiting residual channel of Wi-Fi packets and shows robust performance without any modification on the hardware and any side effect on wireless channels.
Proceedings ArticleDOI
EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching
TL;DR: EyeFi integrates a WiFi chipset to an overhead camera and fuses motion trajectories obtained from both vision and RF modalities to identify individuals and improves WiFi CSI based AoA estimation accuracy by more than 30% and offers 3,800 times computational speed over the state-of-the-art solution.
Journal ArticleDOI
WiTraj: Robust Indoor Motion Tracking With WiFi Signals
TL;DR: Wu et al. as mentioned in this paper proposed WiTraj, a device-free indoor motion tracking system using commodity WiFi devices, which leverages multiple receivers placed at different viewing angles to capture human walking and then intelligently combines the best views to achieve a robust trajectory reconstruction, and distinguishes walking from in-place activities, which are typically interleaved in daily life, so that non-walking activities do not cause tracking errors.
Proceedings ArticleDOI
Human Detection For Crowd Count Estimation Using CSI of WiFi Signals
Omotayo Oshiga,Hussein U. Suleiman,Sadiq Thomas,Petrus Nzerem,Labaran Farouk,Steve A. Adeshina +5 more
TL;DR: A framework for crowd count estimation is presented which utilizes Chebyshev filter and SVD to remove background noise in the CSI data, PCA to reduce the dimensionality of the CSIData and spectral descriptors for feature extraction and depicts that the estimation becomes more–rather than less–accurate when the crowd count increases.
Journal ArticleDOI
LSTM-CNN network for human activity recognition using WiFi CSI data
TL;DR: In this paper, the authors proposed a WiFi-based human activity recognition system that can identify different activities via the channel state information from WiFi devices, and a special deep learning framework, Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), is designed for accurate recognition.
References
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