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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08 Oct 2018
TL;DR: This work proposes a diffraction-based sensing model to investigate how to effectively sense human respiration in FFZ, and deploys the system using COTS Wi-Fi devices to observe that the respiration sensing results match the theoretical model well.
Abstract: Recent work has revealed the sensing theory of human respiration outside the First Fresnel Zone (FFZ) using commodity Wi-Fi devices. However, there is still no theoretical model to guide human respiration detection when the subject locates in the FFZ. In our work [10], we propose a diffraction-based sensing model to investigate how to effectively sense human respiration in FFZ. We present this demo system to show human respiration sensing performance varies based on different human locations and postures. By deploying the respiration detection system using COTS Wi-Fi devices, we can observe that the respiration sensing results match the theoretical model well.
22 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Each transceiver is a mini-pc equipped with a cheap Intel 5300 Wi-Fi card which allows us to record fine-grained CSI information [1]....
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01 Dec 2017
TL;DR: A novel multiple kernel representation learning (MKRL) framework that automatically extracts and combines informative patterns from the Channel State Information (CSI) measurements and achieves a 98\% activity recognition accuracy.
Abstract: Human activity recognition is becoming the vital underpinning for a myriad of emerging applications in the field of human-computer interaction, mobile computing, and smart grid. Besides the utilization of up-to-date sensing techniques, modern activity recognition systems also require a machine learning (ML) algorithm that leverages the sensory data for identification purposes. In view of the unique characteristics of the measurement data and the ML challenges thereof, we propose a non-intrusive human activity recognition system that only uses existing commodity WiFi routers. The core of our system is a novel multiple kernel representation learning (MKRL) framework that automatically extracts and combines informative patterns from the Channel State Information (CSI) measurements. The MKRL firstly learns a kernel string representation from time, frequency, wavelet, and shape domains with an efficient greedy algorithm. Then it performs information fusion from diverse perspectives based on multi-view kernel learning. Moreover, different stages of MKRL can be seamlessly integrated into a multiple kernel learning framework to build up a robust and comprehensive activity classifier. Extensive experiments are conducted in typical indoor environments and the experimental results demonstrate that the proposed system outperforms existing methods and achieves a 98\% activity recognition accuracy.
22 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Intel 5300 NIC tool [15] to extract the CSI data from modified WiFi NIC card equipped with laptop or PC....
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TL;DR: A new approach that uses deep transfer learning techniques to recognize gestures based on the channel state information (CSI) extracted from WiFi signals and demonstrates that the proposed method outperformed other state-of-the-art WiFi-based gesture recognition methods.
Abstract: Different gestures have different action speeds, directions, and trajectories that can cause distinctive effects on the propagation of WiFi signals. In this paper, we present a new approach that uses deep transfer learning techniques to recognize gestures based on the channel state information (CSI) extracted from WiFi signals. Firstly, the CSI streams of gestures are captured and the gesture segments are extracted based on the CSI amplitude changes, and then the WiFi-based gesture recognition problem is innovatively converted to an image classification problem by expressing CSI streams as an image matrix. After that, two deep transfer learning methods are applied to recognize gestures using high-level features extracted by deep convolutional neural network (CNN) and fine-tuned CNN models. We evaluated our method using a collected dataset with 12 gestures in two environments, and the experimental results demonstrated that the proposed method outperformed other state-of-the-art WiFi-based gesture recognition methods.
22 citations
TL;DR: MuDLoc is proposed, the first multi-view discriminant learning approach for device free indoor localization using both amplitude and phase features of Channel State Information (CSI) from multiple Access Points (APs).
Abstract: Location Based Service (LBS) is one of the important aspects of a smart city. Accurate indoor localization plays a vital role in LBS. The ability to localize various subjects in the area of interest facilitates further ubiquitous environments. Specifically, device free localization using wireless signals is getting increased attention as human location is estimated using its impact on the surrounding wireless signals without any active device tagged with subject. In this paper, we propose MuDLoc, the first multi-view discriminant learning approach for device free indoor localization using both amplitude and phase features of Channel State Information (CSI) from multiple Access Points (APs). The same location oriented CSI data can be observed by different APs, thus generating multiple distinct even heterogeneous samples. Multi-view learning is an emerging technique in machine learning which improve performance by utilizing diversity from different view data. In MuDLoc, the localization is modeled as a pattern matching problem, where the target location is predicted based on similarity measure of CSI features of an unknown location with those of the training locations. MuDLoc implements Generalized Inter-view and Intra-view Discriminant Correlation Analysis (GI
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DCA), a discriminative feature extraction approach that incorporates inter-view and intra-view class associations while maximizing pairwise correlations across multi-view data sets. Experimental results from two cluttered environments show that MuDLoc can estimate location with high accuracy which outperforms other benchmark approaches.
22 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...11n tool [12], [13], for each AP, the DP collects CSI data for 30 subcarriers for each Tx-Rx antenna pair....
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...Leveraging off-the-shelf commodity devices, CSI is available in several Wi-Fi network interface cards (NIC), such as Intel 5300 NIC [12], [13]....
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14 May 2017
TL;DR: NotiFi automatically learns the number of human body activity categories for abnormal detection and experimental results in three typical indoor environments indicate that NotiFi can achieve satisfactory performance in accuracy, robustness and stability.
Abstract: We build an ubiquitous abnormal activity detection system, namely NotiFi, for accurately detecting the abnormal activities on commercial off-the-shelf (COTS) IEEE 802.11 devices. In contrast to the traditional wearable sensor based and computer vision based systems which require additional sensors or enough lighting in line-of-sight (LoS) scenario, we proceed directly with abnormal activity characterization and activity modeling at the WiFi signal level based on Channel State Information (CSI). The intuition of NotiFi is that whenever the human body occludes the wireless signal transmitting from the access point to the receiver, the phase and the amplitude information of Channel State Information (CSI) will change sensitively. By creating a multiple hierarchical Dirichlet processes, NotiFi automatically learns the number of human body activity categories for abnormal detection. Experimental results in three typical indoor environments indicate that NotiFi can achieve satisfactory performance in accuracy, robustness and stability.
22 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...11 data frames using a modified driver as described in [28]....
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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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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