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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Journal ArticleDOI
AoA-Aware Probabilistic Indoor Location Fingerprinting Using Channel State Information
TL;DR: In this paper, an autoregressive (AR) modeling entropy of CSI amplitude was adopted as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information.
Journal ArticleDOI
A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments.
TL;DR: This dataset provides researchers with the ability to test their developed methodologies on both LOS and NLOS environments, in addition to many different variations of human movements, such as walking, falling, turning, and pen pick up from the ground.
Journal ArticleDOI
Device-Free Localization Systems Utilizing Wireless RSSI: A Comparative Practical Investigation
TL;DR: This paper implements and investigates the performance of all three major RSSI approaches in two test environments, and demonstrates how different environments and walking trajectories can have significant effects on the localization accuracy.
Journal ArticleDOI
R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi.
TL;DR: A novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest and achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively.
Using Wi-Fi channel state information (CSI) for human activity recognition and fall detection
TL;DR: This work proposes a novel signal segmentation method to accurately determine the start and end of a human activity, and uses several signal pre-processing and noise attenuation techniques not commonly used in CSI-based HARs to improve the features obtained from the amplitude and phase signals.
References
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Predictable 802.11 packet delivery from wireless channel measurements
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