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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09 May 2019
TL;DR: This paper proposes a device-free localization approach using an ensemble of ELMs in which each ELM has the same number of hidden nodes, and demonstrates the effectiveness of the approach and of CSI.
Abstract: A bstract-Device-free localization is a new and developing technology which estimates an object's locations without requiring it to equip any devices. Channel State Information (CSI), containing more fine-grained information than Received Signal Strength Indication (RSSI), is a natural candidate for localization application and has been studied in many works. Extreme Learning Machine (ELM) is a fast and robust algorithm, but it has only one hidden layer, which limits its capacity. One of the most popular ways of improving accuracy is to use multiple different models to obtain better predictive performance. In this paper, we propose a device-free localization approach using an ensemble of ELMs in which each ELM has the same number of hidden nodes. The proposed approach models the localization task as a regression problem. First, we leverage a modified driver to collect CSI and extract phase information. The Principal Component Analysis (PCA) is then applied to reduce the dimensionality of the phase features. After that, the processed features are fed into an ensemble of ELMs to output their respective predictions. The final prediction is an average combination of them. We conducted experiments in a typical indoor environment to verify its performance, and the results demonstrated the effectiveness of our approach and of CSI.
9 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...By utilizing CSITOOL [6], one can easily obtain CSI in an offshelf intel 5300 Wi-Fi NIC....
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...CSI can be easily extracted from a modified driver by using CSITOOL....
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...With the CSITOOL presented in [6], one can easily retrieve this information from a laptop....
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11 Sep 2017
TL;DR: A generic Fresnel Penetration Model (FPM) based real-time device-free localization system called MFDL, using only three to four commodity Wi-Fi devices, can localize a metal plate reflector with 6cm median error in the open space andLocalize a moving person with 45cm medianerror in an outdoor space of 36m2.
Abstract: Commodity Wi-Fi based device-free localization has attracted a great attention in recent years. Previous related work is either fingerprint-based or model-based. In this demo, we will demonstrate a generic Fresnel Penetration Model (FPM) based real-time device-free localization system called MFDL. Using only three to four commodity Wi-Fi devices, it can localize a metal plate reflector with 6cm median error in the open space and localize a moving person with 45cm median error in an outdoor space of 36m2 and 50-75cm median error in indoor environments with a size ranging from 25m2 to 72m2, outperforming the state-of-the-art device-free localization approaches in similar settings.
8 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Both receivers are mini-PCs equipped with an Intel Wi-Fi link 5300 NIC to collect Channel State Information(CSI) [1] as shown in Fig....
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...The fingerprint-based approaches [8] collect CSI measurement [1](or RSSI in earlier works [4]) at each location and build a sitemap of the whole environment....
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01 Mar 2017
TL;DR: A super-resolution-aided fingerprinting (S- FP) is proposed to estimate the position by finding the reference points (RPs) with the highest similarity of the cumulative pseudo-spectrum based on the alignment of the maximal power path.
Abstract: Position fingerprinting (FP), in which the signature of a position is captured from the radio frequency signal, is one of the most efficient indoor positioning algorithms. Besides the received signal strength (RSS), the channel impulse response (CIR) is regarded as a linear temporal filter, which characterizes the multipath channel of the operating environment. Since the CIR requires a larger system bandwidth to distinguishing individual paths along which the signal waves travel, a smaller bandwidth may limit the performance of CIR- based fingerprinting. In this paper, we utilize a super-resolution method, i.e. the multiple signal classification (MUSIC) algorithm, to obtain the pseudo-spectrum for the enhanced resolution of the arriving paths. We create an offline database by the implementation of an OFDM-based channel sounder and obtain the cumulative pseudo-spectrum based on the alignment of the maximal power path. Based on the online#x002F;offline measurements with enhancing resolutions, we propose a super-resolution-aided fingerprinting (S- FP) to estimate the position by finding the reference points (RPs) with the highest similarity of the cumulative pseudo-spectrum. The experimental results show that S-FP reduces the localization error compared with the conventional CIR FP.
8 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...For instance, an Intel Wi-Fi 5300 network interface card can record CSI with the modified firmware in [7]....
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01 Oct 2020
TL;DR: This work proposes WiRelax; the first non-contact respiratory biofeedback system that relies solely on WiFi availability and proposes algorithms that map the changes in the Channel State Information (CSI) to the instantaneous breathing state.
Abstract: Respiratory pattern tracking proved to be critical for many applications ranging from well-being monitoring and stress management to dealing with chronic breathing abnormalities. Specific breathing and meditation exercises have been designed to improve well-being of users based on monitoring the complete breathing waveform. While wearable systems had leveraged a wealth of information available from respiration stream in a variety of applications, contact-less sensing systems are lagging behind when it comes to capturing detailed breathing metrics. In this work we propose WiRelax; the first non-contact respiratory biofeedback system that relies solely on WiFi availability. We propose algorithms that map the changes in the Channel State Information (CSI) to the instantaneous breathing state. The key contribution is a model that relates relative phase of the received signal and the micro-motion of the chest during breathing. A novel processing pipeline is developed to extract a single breathing waveform from CSI data captured across noisy multiple sub-carriers in real-time. Our evaluation in a real-world setup shows that WiRelax can estimate real-time breath-by-breath cycle time with median error less than 0.25 s (
8 citations
Posted Content•
TL;DR: Off-the-shelf WiFi devices are capable of capturing fine-grained human pose figures, similar to cameras, even through a wall and track accurate respiration status, thus demonstrating the effectiveness and feasibility of the approach for in-home monitoring.
Abstract: As elderly population grows, social and health care begin to face validation challenges, in-home monitoring is becoming a focus for professionals in the field. Governments urgently need to improve the quality of healthcare services at lower costs while ensuring the comfort and independence of the elderly. This work presents an in-home monitoring approach based on off-the-shelf WiFi, which is low-costs, non-wearable and makes all-round daily healthcare information available to caregivers. The proposed approach can capture fine-grained human pose figures even through a wall and track detailed respiration status simultaneously by off-the-shelf WiFi devices. Based on them, behavioral data, physiological data and the derived information (e.g., abnormal events and underlying diseases), of the elderly could be seen by caregivers directly. We design a series of signal processing methods and a neural network to capture human pose figures and extract respiration status curves from WiFi Channel State Information (CSI). Extensive experiments are conducted and according to the results, off-the-shelf WiFi devices are capable of capturing fine-grained human pose figures, similar to cameras, even through a wall and track accurate respiration status, thus demonstrating the effectiveness and feasibility of our approach for in-home monitoring.
8 citations
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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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