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Journal ArticleDOI

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.

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Zhihui Gao, Yunfan Gao, Sulei Wang, Dan Li, Yuedong Xu1, Hongbo Jiang 
TL;DR: In this paper, the authors presented CRISLoc, the first CSI fingerprinting based indoor localization prototype system using ubiquitous smartphones, which operates in a completely passive mode, overhearing the packets on-the-fly for his own CSI acquisition.
Abstract: Channel state information (CSI) based fingerprinting for WIFI indoor localization has attracted lots of attention very recently.The frequency diverse and temporally stable CSI better represents the location dependent channel characteristics than the coarsereceived signal strength (RSS). However, the acquisition of CSI requires the cooperation of access points (APs) and involves only dataframes, which imposes restrictions on real-world deployment. In this paper, we present CRISLoc, the first CSI fingerprinting basedlocalization prototype system using ubiquitous smartphones. CRISLoc operates in a completely passive mode, overhearing thepackets on-the-fly for his own CSI acquisition. The smartphone CSI is sanitized via calibrating the distortion enforced by WiFi amplifiercircuits. CRISLoc tackles the challenge of altered APs with a joint clustering and outlier detection method to find them. A novel transferlearning approach is proposed to reconstruct the high-dimensional CSI fingerprint database on the basis of the outdated fingerprintsand a few fresh measurements, and an enhanced KNN approach is proposed to pinpoint the location of a smartphone. Our studyreveals important properties about the stability and sensitivity of smartphone CSI that has not been reported previously. Experimentalresults show that CRISLoc can achieve a mean error of around 0.29m in a6m times 8mresearch laboratory. The mean error increases by 5.4 cm and 8.6 cm upon the movement of one and two APs, which validates the robustness of CRISLoc against environment changes.

29 citations

Proceedings ArticleDOI
15 Nov 2021
TL;DR: In this paper, the authors proposed a WiFi-based human gesture recognition (HGR) system, which can recognize unseen gestures with only one (or few) labeled samples, and employed a lightweight one-shot learning framework based on transductive fine-tuning to eliminate model re-training.
Abstract: WiFi-based Human Gesture Recognition (HGR) becomes increasingly promising for device-free human-computer interaction. However, existing WiFi-based approaches have not been ready for real-world deployment due to the limited scalability, especially for unseen gestures. The reason behind is that when introducing unseen gestures, prior works have to collect a large number of samples and re-train the model. While the recent advance of few-shot learning has brought new opportunities to solve this problem, the overhead has not been effectively reduced. This is because these methods still require enormous data to learn adequate prior knowledge, and their complicated training process intensifies the regular training cost. In this paper, we propose a WiFi-based HGR system, namely OneFi, which can recognize unseen gestures with only one (or few) labeled samples. OneFi fundamentally addresses the challenge of high overhead. On the one hand, OneFi utilizes a virtual gesture generation mechanism such that the massive efforts in prior works can be significantly alleviated in the data collection process. On the other hand, OneFi employs a lightweight one-shot learning framework based on transductive fine-tuning to eliminate model re-training. We additionally design a self-attention based backbone, termed as WiFi Transformer, to minimize the training cost of the proposed framework. We establish a real-world testbed using commodity WiFi devices and perform extensive experiments over it. The evaluation results show that OneFi can recognize unseen gestures with the accuracy of 84.2, 94.2, 95.8, and 98.8% when 1, 3, 5, 7 labeled samples are available, respectively, while the overall training process takes less than two minutes.

29 citations

Journal ArticleDOI
TL;DR: This work proposes a novel Wi-Fi signals based fatigue detection approach, called WiFind, which can detect the fatigue symptoms in the vehicle without relying on any visual image or video, and can achieve the recognition accuracy of 89.6 percent in a single driver scenario.
Abstract: Driver fatigue is a leading factor in road accidents that can cause severe fatalities. Existing fatigue detection works focus on vision and electroencephalography (EEG) based means of detection. However, vision-based approaches suffer from view-blocking or vision distortion problems and EEG-based systems are intrusive, and the drivers have to use/wear the devices with inconvenience or additional costs. In our work, we propose a novel Wi-Fi signals based fatigue detection approach, called WiFind to overcome the drawbacks as associated with the current works. WiFind is simple and (wearable) device-free. It can detect the fatigue symptoms in the vehicle without relying on any visual image or video. By applying self-adaptive method, it can recognize the body features of drivers in multiple modes. It applies Hilbert-Huang transform (HHT) based pattern extract method results in accuracy increase in motion detection mode. WiFind can be easily deployed in a commodity Wi-Fi infrastructure, and we have evaluated its performance in real driving environments. The experimental results have shown that WiFind can achieve the recognition accuracy of 89.6 percent in a single driver scenario.

29 citations


Cites methods from "Tool release: gathering 802.11n tra..."

  • ...04 LTS with a modified Intel driver [6] to collect CSI data and is connected to a TP-LINK TL-WR842N wire-...

    [...]

  • ...Especially, with the advantages of non-intrusion and device-free, Wi-Fi signals contribute to human activities recognition by the received signal strength(RSS)-based method [5] and the Channel State Information(CSI)-based method [6]....

    [...]

Journal ArticleDOI
TL;DR: This work proposes a novel phase decomposition method to obtain the phase of multipath provided by a AP and use the decomposed phase as a fingerprint after the feature exaction by principal component analysis (PCA).
Abstract: WiFi-based indoor localization techniques are critical for location-based services. Among them, fingerprint-based method gains considerable interest due to its high accuracy and low equipment requirement. One of the major challenges faced by fingerprint-based position system is that in some places there are not enough access points (AP) to provide features for accurate location. To address that, we propose a novel fingerprint-based system using only a single AP. We propose a novel phase decomposition method to obtain the phase of multipath provided by a AP and use the decomposed phase as a fingerprint after the feature exaction by principal component analysis (PCA). Performance in the laboratory, meeting room, and corridor is investigated, and our system is also compared with a RSSI-based and a CSI-based fingerprint localization system. As the experimental results suggest, the minimum mean distance error is 0.6 m in the laboratory, 0.45 m in the meeting room, and 1.08 m in the corridor, outperforming the other two systems.

29 citations

Journal ArticleDOI
26 Feb 2019-Sensors
TL;DR: The experimental results show that housing environments, combined with various environmental factors, generate a significant difference in the accuracy of the applied CSI-based ADL-recognition systems, and provides insights into how such ADL systems should be configured for various home environments.
Abstract: Recently, device-free human activity–monitoring systems using commercial Wi-Fi devices have demonstrated a great potential to support smart home environments. These systems exploit Channel State Information (CSI), which represents how human activities–based environmental changes affect the Wi-Fi signals propagating through physical space. However, given that Wi-Fi signals either penetrate through an obstacle or are reflected by the obstacle, there is a high chance that the housing environment would have a great impact on the performance of a CSI-based activity-recognition system. In this context, this paper examines whether and to what extent housing environment affects the performance of the CSI-based activity recognition systems. Activities in daily living (ADL)–recognition systems were implemented in two typical housing environments representative of the United States and South Korea: a wood-frame apartment (Unit A) and a reinforced concrete-frame apartment (Unit B), respectively. The experimental results show that housing environments, combined with various environmental factors (i.e., structural building materials, surrounding Wi-Fi interference, housing layout, and population density), generate a significant difference in the accuracy of the applied CSI-based ADL-recognition systems. This outcome provides insights into how such ADL systems should be configured for various home environments.

29 citations


Cites methods from "Tool release: gathering 802.11n tra..."

  • ...11n tool [16], the CSI data were captured and extracted for 30 subcarriers for the first AP-Receiver antenna pair....

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References
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Proceedings ArticleDOI
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.
Abstract: RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show 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. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (

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....

    [...]

Journal ArticleDOI
01 Oct 2001
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).
Abstract: At some point in the future, how far out we do not exactly know, wireless access to the Internet will outstrip all other forms of access bringing the freedom of mobility to the way we access the we...

615 citations

Journal ArticleDOI
07 Jan 2010
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.
Abstract: The use of multiple antennas and MIMO techniques based on them is the key feature of 802.11n equipment that sets it apart from earlier 802.11a/g equipment. It is responsible for superior performance, reliability and range. In this tutorial, we provide a brief introduction to multiple antenna techniques. We describe the two main classes of those techniques, spatial diversity and spatial multiplexing. To ground our discussion, we explain how they work in 802.11n NICs in practice.

89 citations