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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TL;DR: A device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals and harnesses an effective signal processing technique to reveal the unique patterns of each activity.
Abstract: Human activity tracking plays a vital role in human–computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macr...
38 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Herein, CSI data collected from a wireless network that composed of a transmitter with two transmitted antennas (WiFi router) as an access point (AP) and a laptop installed IWL 5300 NIC as a DP with three received antennas....
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...Each stream has 30 subcarriers, as reported by the IWL 5300 NIC (Halperin et al. 2011)....
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...The CSI-Tool (Halperin et al. 2011) can be used to extract CSI from commodity wireless network interface controllers (NIC)....
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...First, CSI is collected at detection point (DP) (i.e. wireless receiver), which is a laptop installed with Ubuntu and the open source CSI-Tool (Halperin et al. 2011)....
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...Factor Value AP One TP-link WR845N with two antennas DP One laptop with IW5300 NIC with three antennas Collected samples 500 sample for each motion Sample rates 100 sample/ms for each measurements....
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01 Jun 2017
TL;DR: ROArray, a Robust Array based system that accurately localizes a target even with low SNRs, significantly outperforms state-of-the-art solutions in terms of localization accuracy; when medium or high SNRs are present, it achieves comparable accuracy.
Abstract: With the multi-antenna design of WiFi interfaces, phased array has become a promising mechanism for accurateWiFi localization. State-of-the-art WiFi-based solutions using AoA (Angle-of-Arrival), however, face a number of critical challenges. First, their localization accuracy degrades dramatically when the Signal-to-Noise Ratio (SNR) becomes low. Second, they do not fully utilize coherent processing across all available domains. In this paper, we present ROArray, a Robust Array based system that accurately localizes a target even with low SNRs. In the spatial domain, ROArray can produce sharp AoA spectrums by parameterizing the steering vector based on a sparse grid. Then, to expand into the frequency domain, it jointly estimates the ToAs (Time-of-Arrival) and AoAs of all the paths using multi-subcarrier OFDM measurements. Furthermore, through multi-packet fusion, ROArray is enabled to perform coherent estimation across the spatial, frequency, and time domains. Such coherent processing not only increases the virtual aperture size, which enlarges the number of maximum resolvable paths, but also improves the system robustness to noise. Our implementation using off-the-shelf WiFi cards demonstrates that, with low SNRs, ROArray significantly outperforms state-of-the-art solutions in terms of localization accuracy; when medium or high SNRs are present, it achieves comparable accuracy.
38 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Linux CSI Tools [26] are employed to obtain CSI measurements....
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TL;DR: A novel empirical-mode-decomposition-based general DFI framework is proposed, which decomposes raw noisy CSI measurements into intrinsic mode functions (IMF) and extracts intrinsic features from the IMF components accordingly and develops two DFI systems based on the respiration and gait biometric features.
Abstract: Device-free identification (DFI) is a promising technique, which could recognize human identity using his/her unique influence on surrounding wireless signals in a device-free and contact-free manner. It could maintain the privacy of a user and enable smart applications to provide customized service for a specific user. Despite its advantages over other person's identification systems, one fundamental problem to solve is that the accuracy of the DFI system is a little bit low due to the extremely noisy wireless measurements. The goal of this work is to explore and exploit a method to extract intrinsic features from the noisy channel state information (CSI) so as to realize high-performance DFI. To this end, we propose a novel empirical-mode-decomposition-based general DFI framework, which decomposes raw noisy CSI measurements into intrinsic mode functions (IMF) and extracts intrinsic features from the IMF components accordingly. Under the proposed framework, we also develop two DFI systems based on the respiration and gait biometric features. Extensive experiments carried out on commodity WiFi reveal that the developed systems could identify a person with an accuracy of over 90% from a group of ten persons.
37 citations
Cites background from "Tool release: gathering 802.11n tra..."
...Compared with traditional channel state metric such as the received signal strength which could provide only the amplitude information on one channel, CSI could provide both the amplitude and phase information on multichannels simultaneously [33]....
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TL;DR: The presented dataset can be exploited to advance Wi-Fi-based human activity recognition in different aspects, such as the use of various machine learning algorithms to recognize different human-to-human interactions.
37 citations
11 Apr 2016
TL;DR: This paper designs and develops HeadScan, a first- of-its-kind wearable for radio-based sensing of a number of human activities that involve head and mouth movements and incorporates a radio signal processing pipeline that converts the fine-grained CSI measurements extracted from the radio signals into the targeted human activities.
Abstract: The popularity of wearables continues to rise. However, their functionalities and applications are constrained by the types of sensors that are currently available. Accelerometers and gyroscopes struggle to capture complex user activities. Microphones and image sensors are more powerful but capture privacy sensitive information. Physiological sensors are obtrusive to users since they often require skin contact and must be placed at certain body positions to function. In contrast, radio- based sensing uses wireless radio signals to capture movements of different parts of body caused by human activities and therefore provides a contactless and privacy-preserving approach to detect and monitor human activities. In this paper, we contribute to the search for a new sensing modality for the next generation of wearable devices by exploring the feasibility of radio-based human activity sensing and recognition in the context of wearable setting. We envision radio-based sensing has the potential to fundamentally transform wearables as we currently know them. As the first step to achieve our vision, we have designed and developed HeadScan, a first- of-its-kind wearable for radio-based sensing of a number of human activities that involve head and mouth movements. HeadScan only requires a pair of small antennas placed on the shoulder and collar and one wearable unit worn on the arm or the belt of the user. HeadScan uses the fine-grained CSI measurements extracted from the radio signals and incorporates a radio signal processing pipeline that converts the raw CSI measurements into the targeted human activities. To examine the feasibility and performance of HeadScan, we have collected about 50.5 hours data from seven users. Our wide-range experiments including comparisons to a conventional skin-contact audio-based sensing approach to tracking the same set of head and mouth-related activities highlight the enormous potential of our radio-based sensing approach and provide guidance to future explorations.
37 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...We use the sparse representation framework because it has been proved to be robust to the noise caused by radio frequency interference (RFI) in radio signal processing [2]....
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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.
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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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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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89 citations