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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01 Oct 2019
TL;DR: This paper proposes a Channel State Information (CSI) based human action counting and recognition method, which is named Wi-CR, which takes advantage of an activity indicator and a threshold to detect the start and end times of a set of continuous actions and recognizes the action of each action period by k-Nearest Neighbors.
Abstract: Human continuous activity recognition, i.e. automatic inference of human behavior, plays an increasingly important role in many fields, such as smart home, somatic games, and health care. The widening application of wireless technology in sensing is making human continuous activity recognition more unobtrusive and user-friendly. In this paper, we propose a Channel State Information (CSI) based human action counting and recognition method, which is named Wi-CR. Wi-CRtakes advantage of an activity indicator and a threshold to detect the start and end times of a set of continuous actions, then counts the number of actions through a peak-finding algorithm, and determines the start and end times of each action. After that, Wi-CRemploys Discrete Wavelet Transformation (DWT) to extract features to analyze correlation of action waveforms and perform best-fit matching based on dynamic time warping (DTW). Finally, it recognizes the action of each action period by k-Nearest Neighbors (KNN). The experimental results show that Wi-CRcan achieve action counting accuracy of 95% and recognition accuracy of 90%, in the scenarios with two types of actions (squat and walk) occurring simultaneously.
10 citations
TL;DR: This article decomposes the AP placement problem into two subproblems, namely AP selection problem and error minimization problem, and designs a centralized and a distributederror minimization algorithm to further decrease the localization error.
Abstract: The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.
10 citations
Cites background from "Tool release: gathering 802.11n tra..."
...digit analog-to-digital converter in the Wi-Fi card makes the exported CSI subject to excessive noise [27]....
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TL;DR: PHYAlert is proposed, an identity spoofing attack alert system designed to protect a Wi-Fi-based edge network and can achieve an 8x improvement in the false positive rate over the conventional signal-strength-based solution.
Abstract: Delivering service intelligence to billions of connected devices is the next step in edge computing. Wi-Fi, as the de facto standard for high-throughput wireless connectivity, is highly vulnerable to packet-injection-based identity spoofing attacks (PI-ISAs). An attacker can spoof as the legitimate edge coordinator and perform denial of service (DoS) or even man-in-the-middle (MITM) attacks with merely a laptop. Such vulnerability leads to serious systematic risks, especially for the core edge/cloud backbone network.In this paper, we propose PHYAlert, an identity spoofing attack alert system designed to protect a Wi-Fi-based edge network. PHYAlert profiles the wireless link with the rich dimensional Wi-Fi PHY layer information and enables real-time authentication for Wi-Fi frames. We prototype PHYAlert with commercial off-the-shelf (COTS) devices and perform extensive experiments in different scenarios. The experiments verify the feasibility of spoofing detection based on PHY layer information and show that PHYAlert can achieve an 8x improvement in the false positive rate over the conventional signal-strength-based solution.
10 citations
TL;DR: In this article , a graph-based few-shot learning method with dual attention mechanism (CSI-GDAM) is proposed to perform CSI-based human activity recognition (HAR).
Abstract: Human activity recognition (HAR) based on channel state information (CSI) plays an increasingly important role in the research of human–computer interaction. Many CSI HAR models based on traditional machine learning methods and deep learning methods have encountered two challenges. A lot of CSI activity data is needed to train the HAR models, which is time consuming. When the indoor environment or scene changes, the recognition accuracy of the model drops significantly, so it is necessary to recollect data to train the model. The existing few-shot learning-based method can solve the above problems to some extent, but when there are more kinds of new activities or fewer shots, the recognition accuracy will decrease significantly. In this article, considering the relationship between various activity data, a graph-based few-shot learning method with dual attention mechanism (CSI-GDAM) is proposed to perform CSI-based HAR. The model uses a feature extraction layer, including the convolutional block attention module (CBAM), to extract activity-related information in CSI data. The difference and inner product of the feature vector of the CSI activity samples are used to realize the graph convolutional network with a graph attention mechanism. The experiments proved that under the learning task of recognizing new activities in the new environment, the recognition accuracy rates reached 99.74% and 98.42% in the 5-way 5-shot and 5-way 1-shot cases, respectively. The proposed method is also compared with other few-shot learning and transfer learning methods.
10 citations
TL;DR: In this article , two distinct features from a hand-oriented view (rather than from a transceiver's view), namely, the dynamic phase vector (DPV) and motion rotation variable (MRV), are constructed to characterize a big set of handwriting gestures, despite significant change in locations of transceivers, the relative location and orientation of the hand with respect to transceiver, and the drawing sizes.
Abstract: Recent advances in wireless sensing techniques have made it possible to recognize hand gestures using channel state information (CSI) in commodity WiFi devices. Existing WiFi-based gesture recognition systems mainly use learning-based pattern recognition methods to recognize different gestures, however, these methods fail to work well when the locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and/or the hand gesturing size change, leading to inconsistent signal patterns caused by those factors. Although some recent efforts have been made to address the so-called “domain-dependent” gesture recognition problem, they either require prior knowledge on initial locations of the hand and WiFi devices or need to train several classifiers for the specific domains. Different from the state-of-the-art methods, we construct two distinct features from a hand-oriented view (rather than from a transceiver’s view), namely, the dynamic phase vector (DPV) and motion rotation variable (MRV), which are quite consistent in characterizing a big set of handwriting gestures, despite significant change in locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and the drawing sizes. We further incorporate a hierarchical sensing framework and develop HandGest—a real-time handwriting gesture recognition system using commodity WiFi devices, to precisely recognize a great number of “in-the-air” handwritings based on the aforementioned two domain-independent features and a pipeline of specific features. Extensive experiments have been done in practical settings with 20 volunteers, evaluation results demonstrate that HandGest outperforms state-of-the-art methods on a large number of handwritings with different transceivers’ location, different initial hand locations and orientations, as well as different drawing sizes. Given its superior performance, we believe that HandGest paves a new way to enhance the real-world practicality of WiFi-based gesture recognition.
10 citations
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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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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