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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17 Dec 2020
TL;DR: In this paper, the authors proposed to translate YouTube gym activity videos to instant simulated RF data for training any human-motion-based RF sensing system, in any given setup, without the need for massive RF data collection.
Abstract: In this paper, we propose a novel, generalizable, and scalable idea that eliminates the need for collecting Radio Frequency (RF) measurements, when training RF sensing systems for human-motion-related activities. Existing learning-based RF sensing systems require collecting massive RF training data, which depends heavily on the particular sensing setup/involved activities. Thus, new data needs to be collected when the setup/activities change, significantly limiting the practical deployment of RF sensing systems. On the other hand, recent years have seen a growing, massive number of online videos involving various human activities/motions. In this paper, we propose to translate such already-available online videos to instant simulated RF data for training any human-motion-based RF sensing system, in any given setup. To validate our proposed framework, we conduct a case study of gym activity classification, where CSI magnitude measurements of three WiFi links are used to classify a person's activity from 10 different physical exercises. We utilize YouTube gym activity videos and translate them to RF by simulating the WiFi signals that would have been measured if the person in the video was performing the activity near the transceivers. We then train a classifier on the simulated data, and extensively test it with real WiFi data of 10 subjects performing the activities in 3 areas. Our system achieves a classification accuracy of 86% on activity periods, each containing an average of 5.1 exercise repetitions, and 81% on individual repetitions of the exercises. This demonstrates that our approach can generate reliable RF training data from already-available videos, and can successfully train an RF sensing system without any real RF measurements. The proposed pipeline can also be used beyond training and for analysis and design of RF sensing systems, without the need for massive RF data collection.
22 citations
TL;DR: A model to characterize the relationship between the phase and amplitude measurements and the vehicle speed is proposed and an efficient method to detect the vehicle and estimate its speed accordingly using the frequency-domain information involved in the spectrogram is developed.
Abstract: Vehicle networks is a promising technique, which could provide ubiquitous wireless access for vehicles and realize intelligent transportation. Motivated by the recent advances on device-free wireless sensing, this paper explores a novel technique, which could estimate the speed of a vehicle by analyzing its influence on surrounding wireless signals from roadside wireless infrastructures, such as WiFi. This technique could enable vehicle networks with new moving speed sensing ability and evolve it into a large-scale sensing network, which could monitor the traffic status seamlessly. To achieve this goal, in this paper, we propose and formulate a model to characterize the relationship between the phase and amplitude measurements and the vehicle speed. Based on the model, we develop an efficient method to detect the vehicle and estimate its speed accordingly using the frequency-domain information involved in the spectrogram. We carry out extensive experiments utilizing commodity WiFi. The experimental results reveal that the proposed method could estimate the speed with an error of less than 2.6 km/h, which meets the United Nations regulation for speedometer.
22 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...We develop a prototype device-free vehicle speed estimation system using Intel 5300 commodity WiFi hardware [7], and carry out experiments on a road....
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..., Intel 5300 wireless network interface card [7], could measure the physical layer channel frequency response information on multiple subcarriers and acquire the CSI measurement vector Ht as follows...
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TL;DR: This work proposes a novel communication framework that enables more flexible and concurrent communications among IoT devices, and demonstrates that it is possible to enable the BLE to Wi-Fi cross-technology communication while supporting the concurrent BLEToBLE to BLE and Wi-fi toWi-Fi communications.
Abstract: The exponentially increasing number of Internet of Things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4-GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose $B^{2}W^{2}$ , a novel communication framework that enables $N$ -way concurrent communication among Wi-Fi and Bluetooth low energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to Wi-Fi cross-technology communication while supporting the concurrent BLE to BLE and Wi-Fi to Wi-Fi communications. We conducted extensive experiments under different real-world settings, and results show that its throughput is more than 85 $\times$ times higher than that of the most recently reported cross-technology communication system, which only supports one-way communication (i.e., broadcasting) at any specific time.
22 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...5300 [4]) calculate CSI by using the predefined bits in the preamble which means the WiFi receiver only samples the...
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TL;DR: In this article, the authors proposed a Wi-Fi-based gait recognition method for human identification. But the method is not suitable for the use of wearable sensors and does not work well in outdoor environments.
Abstract: Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is mor...
22 citations
Posted Content•
TL;DR: Using CSI-Net, a unified Deep Neural Network~(DNN), to learn the representation of WiFi signals, this work jointly solved two body characterization problems: biometrics estimation and person recognition.
Abstract: We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the representation of WiFi signals. Using CSI-Net, we jointly solved two body characterization problems: biometrics estimation (including body fat, muscle, water, and bone rates) and person recognition. We also demonstrated the application of CSI-Net on two distinctive pose recognition tasks: the hand sign recognition (fine-scaled action of the hand) and falling detection (coarse-scaled motion of the body).
22 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Both of them were installed with Linux 802.11n CSI Tool[50] for parsing CSI....
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...11n CSI Tool [50] to record CSI sequences of 30 subcarriers....
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...In our experiments, we deploy Linux 802.11n CSI Tool [50] to record CSI sequences of 30 subcarriers....
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"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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