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 Wi-Fi-enabled Infrared-like Device-free (WIDE) method for target tracking to realize a lightweight target-tracking method that can track targets rapidly as well as achieve satisfactory tracking results.
Abstract: Target tracking is a critical technique for localization in an indoor environment. Current target-tracking methods suffer from high overhead, high latency, and blind spots issues due to a large amount of data needing to be collected or trained. On the other hand, a lightweight tracking method is preferred in many cases instead of just pursuing accuracy. For this reason, in this paper, we propose a Wi-Fi-enabled Infrared-like Device-free (WIDE) method for target tracking to realize a lightweight target-tracking method. We first analyze the impact of target movement on the physical layer of the wireless link and establish a near real-time model between the Channel State Information (CSI) and human motion. Secondly, we make full use of the network structure formed by a large number of wireless devices already deployed in reality to achieve the goal. We validate the WIDE method in different environments. Extensive evaluation results show that the WIDE method is lightweight and can track targets rapidly as well as achieve satisfactory tracking results.
1 citations
09 Aug 2020
TL;DR: This paper prototypes a new method for user authentication by leveraging commodity WiFi, and explores four classifiers including K Nearest Neighbor(KNN), Support Vector Machine (SVM), Random Forest, and Decision Tree for recognizing users, showing that KNN provides the best performance.
Abstract: User authentication is a major area of interest within the field of Human Computer Interaction (HCI). Meanwhile, it prevents unauthorized accesses to certain the security of data. Personal Identification Number (PIN) and biometrics are the main approaches for identifying the user on the basis of his/her identity. However, PIN can be easily leaked to others, and biometrics usually require specialized devices. In this paper, we prototype our system, a new method for user authentication by leveraging commodity WiFi. The basic methodology is to explore the typing habit of users from Channel State Information (CSI). The design and implementation of our system face two challenges, i.e. extracting keystroke features from wireless channel data and authenticating the user via typing habit from the corresponding keystroke features. For the former, we capture signal fluctuations caused by the micro movements like typing and extract the keystroke features on channel response obtained from commodity WiFi devices. For the latter, we design a computational intelligence driven mechanism to authenticate users from the corresponding keystroke feature. We prototype our system on the low-cost off-the-shelf WiFi devices and evaluate its performance in real-world experiments. We have explored four classifiers including K Nearest Neighbor(KNN), Support Vector Machine (SVM), Random Forest, and Decision Tree for recognizing users. Empirical results show that KNN provides the best performance, i.e., 85.2% authentication accuracy, 12.8% false accept rate, and 11.2% false reject rate on average over 9 participants.
1 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...We obtain the raw signal propagation data from the Intel 5300 card through CSI tools [16]....
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TL;DR: In this paper , the channel state information (CSI) of Wi-Fi signals is used as inputs for a deep neural network tasked with establishing a driver recognition model, which achieved an average recognition accuracy of roughly 95%.
Abstract: Driver identification is a key factor in attributing liability for car accident insurance claims and assessing driver competency. Existing driver recognition systems use mechanisms based on identity keys (e.g., car keys and identity cards) or biometric characteristics (e.g., fingerprints, voiceprints, and face recognition). However, identity keys are prone to loss or misappropriation; biometric methods are prone to driver substitution and raise issues pertaining to privacy; and neither approach is applicable to the majority of commercial applications (e.g., hiring delivery drivers and renting out vehicles). This paper presents a novel driver identity recognition system based on the channel state information (CSI) of Wi-Fi signals, which tend to vary with the user, even when performing identical tasks. CSI values corresponding to driver maneuvers (e.g., turning or going straight) are used as inputs for a deep neural network tasked with establishing a driver recognition model. The feasibility of this approach was verified through simulations in the laboratory and with a vehicle, both of which achieved average recognition accuracy of roughly 95%.
1 citations
TL;DR: In this article, a method of generalization through adversarial data augmentation was proposed to improve the performance of deep learning-based fall detection systems in unseen environments. But the performance was not significant.
Abstract: Recent WiFi-based fall detection systems have drawn much attention due to their advantages over other sensory systems. Various implementations have achieved impressive progress in performance, thanks to machine learning and deep learning techniques. However, many of such high accuracy systems have low reliability as they fail to achieve robustness in unseen environments. To address that, this paper investigates a method of generalization through adversarial data augmentation. Our results show a slight improvement in deep learning-systems in unseen domains, though the performance is not significant.
1 citations
TL;DR: In this paper , the channel state information (CSI) of Wi-Fi signals is used as inputs for a deep neural network tasked with establishing a driver recognition model, which achieved an average recognition accuracy of roughly 95%.
Abstract: Driver identification is a key factor in attributing liability for car accident insurance claims and assessing driver competency. Existing driver recognition systems use mechanisms based on identity keys (e.g., car keys and identity cards) or biometric characteristics (e.g., fingerprints, voiceprints, and face recognition). However, identity keys are prone to loss or misappropriation; biometric methods are prone to driver substitution and raise issues pertaining to privacy; and neither approach is applicable to the majority of commercial applications (e.g., hiring delivery drivers and renting out vehicles). This paper presents a novel driver identity recognition system based on the channel state information (CSI) of Wi-Fi signals, which tend to vary with the user, even when performing identical tasks. CSI values corresponding to driver maneuvers (e.g., turning or going straight) are used as inputs for a deep neural network tasked with establishing a driver recognition model. The feasibility of this approach was verified through simulations in the laboratory and with a vehicle, both of which achieved average recognition accuracy of roughly 95%.
1 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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