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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TL;DR: A multi-view features fusion and AdaBoost-based indoor localization scheme by exploiting channel state information (CSI) of WiFi signals is proposed, realizing the purpose of localizing a user relying solely on WiFi signals with a high level of granularity, without any active engagement from the users.
Abstract: With the increasing demand for location-based service (LBS), WiFi-based localization has attracted considerable attentions due to the wide deployment and low cost of WiFi devices. However, most of existing approaches need to work within line-of-sight (LoS) range or require users to take dedicated devices. Thus, a device-free and passive indoor localization scheme is still desired. In this paper, a multi-view features fusion and AdaBoost-based indoor localization scheme by exploiting channel state information (CSI) of WiFi signals is proposed. To this end, we firstly formulate a shared consistent representation by using CSI measurements from all WiFi receivers, modeling the common properties among all views. Based on this, a features extraction is applied to the consistent representation for charactering channel properties. Meanwhile, these features extracted are optimized via a t-distributed stochastic neighbor embedding (t-SNE) algorithm for selecting the effective features, contributing to system performance. AdaBoost then builds a non-linear mapping between the CSI features and locations, realizing the purpose of localizing a user relying solely on WiFi signals with a high level of granularity, without any active engagement from the users. We implement a prototype on commodity WiFi devices and conduct comprehensive experiments in indoor scenarios. Based on real-world CSI measurements, the results confirm that the proposed scheme can achieve average localization errors of 0.8 m and 1.1 m in two indoor scenarios.
5 citations
TL;DR: Experimental evaluations demonstrate the effectiveness of the method WiFlowCount, able to detect people flows and subflows accurately and count the number of people in continuous flows with high accuracy, outperforming the existing work.
Abstract: People flow counting is to count the number of people passing through a passage or a gate. Conventional vision-based approaches require line-of-sight (LoS) and impose privacy concerns, while most radio-based approaches require dedicated equipment and incur high cost. In this article, we propose to exploit commodity WiFi to count the number of people of continuous flows in a device-free way, requiring one pair of WiFi transmitter and receiver. Leveraging the Doppler effect induced by human passing, the proposed method, named WiFlowCount, first constructs the spectrogram of Doppler shifts from channel state information. Based on the spectrogram, WiFlowCount detects the people flows and the subflows according to the power distribution in the spectrogram. An optimal rotation and segmentation algorithm is proposed to segment the spectrogram of a continuous flow into the subspectrograms of its subflows. The number of people in each subflow is estimated from its subspectrogram via convolutional neural networks, which add up to the total people count in the continuous flow. Experimental evaluations demonstrate the effectiveness of the method WiFlowCount, able to detect people flows and subflows accurately and count the number of people in continuous flows with high accuracy, outperforming the existing work.
5 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...Leveraging commodity network interface card with modified firmware and driver [21], the amplitude and phase of each subcarrier can be revealed to the upper layers in the format of CSI....
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...modity wireless adapter supporting OFDM and MIMO as well, with modified firmware and driver to reveal CSI to upper layers [21]....
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TL;DR: A computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information of WiFi signals is explored, demonstrating an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy.
Abstract: Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver's activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93 . 1 % in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse scale of applications. In the realm of intelligent vehicles and assisted driving systems, the proposed wireless solution can effectively characterize the driving maneuvers, primary tasks, driver distraction, and fatigue by exploiting radio-image descriptors.
5 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...We conduct experiments using 802.11n-based CSI Tool as described in Reference [41] on the receiver to acquire WiFi CSI measurements on 30 subcarriers....
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...The authors of Reference [27] examined a novel WiFi-based wireless driver head tracking system....
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...Each Tx-Rx antenna pair of WiFi CSI system supports 30 OFDM subcarriers to record channel variations, available on commercial WiFi devices in the form of CSI measurements [41]....
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...Let N is the sum of all element values describing the total number of concurrent time of gray level values, then normalized GLCM is described as: Pn(i, j|d, θ) = P(i, j|d, θ) N , where θ = {0, π 4 , π 2 , 3π 4 } (21) In this paper, we particularly select four co-occurrence statistics including entropy, inverse difference moment, energy and correlation, as described in Reference [36]....
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...The channel variations are readily available in the form of CSI measurements on commercial WiFi devices [41]....
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TL;DR: It is proved that the HHI-AttentionNet is the best model providing an average accuracy, F1 score, Cohen’s Kappa, and Matthews correlation coefficient of 95.47%, 95.45%, 0.951%, and 0.950%, respectively, for recognition of 13 HHIs.
Abstract: Nowadays WiFi based human activity recognition (WiFi-HAR) has gained much attraction in an indoor environment due to its various benefits, including privacy and security, device free sensing, and cost-effectiveness. Recognition of human-human interactions (HHIs) using channel state information (CSI) signals is still challenging. Although some deep learning (DL) based architectures have been proposed in this regard, most of them suffer from limited recognition accuracy and are unable to support low computation resource devices due to having a large number of model parameters. To address these issues, we propose a dynamic method using a lightweight DL model (HHI-AttentionNet) to automatically recognize HHIs, which significantly reduces the parameters with increased recognition accuracy. In addition, we present an Antenna-Frame-Subcarrier Attention Mechanism (AFSAM) in our model that enhances the representational capability to recognize HHIs correctly. As a result, the HHI-AttentionNet model focuses on the most significant features, ignoring the irrelevant features, and reduces the impact of the complexity on the CSI signal. We evaluated the performance of the proposed HHI-AttentionNet model on a publicly available CSI-based HHI dataset collected from 40 individual pairs of subjects who performed 13 different HHIs. Its performance is also compared with other existing methods. These proved that the HHI-AttentionNet is the best model providing an average accuracy, F1 score, Cohen’s Kappa, and Matthews correlation coefficient of 95.47%, 95.45%, 0.951%, and 0.950%, respectively, for recognition of 13 HHIs. It outperforms the best existing model’s accuracy by more than 4%.
5 citations
06 Nov 2020
TL;DR: In this paper, the authors designed a system to recognize different human behaviors based on the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) for WiFi Channel State Information (CSI).
Abstract: Nowadays, with the continuous development of WiFi technology, more researchers come to realize that human behavior can be recognized by the application of WiFi Channel State Information (CSI) When human behavior has some changes, it will influence reflections of WiFi signals, which will also cause some changes to the CSI Using the Intel WiFi Link 5300 network interface controller (NIC) and CSI-Tool, we can obtain the CSI data of corresponding behaviors In this paper, we design a system to recognize different human behaviors based on the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) Firstly, we use the original data collected by the CSI-Tool, then extract the CSI amplitude values of different behaviors as features and input them into neural network structures where the GRU and CNN are connected in parallel Based on the above works, we can successfully identify different human behaviors
5 citations
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.
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....
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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
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