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
More filters
TL;DR: TransTrack is an accurate wireless indoor gesture tracking system that can adjust to different users quickly and proposes an online meta-transfer learning method that collects unlabeled data transparently to train the tracking model for different tasks.
7 citations
TL;DR: A pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors is proposed, which is improved from three aspects: channel state information instead of received signal strength indicator (RSSI) is used, and fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results.
Abstract: Pedestrian-to-vehicle communications, where pedestrian devices transmit their position information to nearby vehicles to indicate their presence, help to reduce pedestrian accidents. Satellite-based systems are widely used for pedestrian positioning, but have much degraded performance in urban canyon, where satellite signals are often obstructed by roadside buildings. The authors propose a pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors. The performance of pedestrian positioning is improved from three aspects: (i) channel state information instead of received signal strength indicator (RSSI) is used to estimate pedestrian-vehicle distance with higher precision. (ii) Only signals with line-of-sight path are used, and the property of distance error is considered. (iii) Fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results. Extensive evaluations, via trace-based simulation, confirm that (i) fixing rate of positions can be much improved. (ii) Horizontal positioning error can be greatly reduced, nearly by one order compared with off-the-shelf receivers, by almost half compared with RSSI-based method, and can be reduced further to about 80 cm when vehicle transmission period is 100 ms and Kalman filter is applied. Generally, positioning performance increases with the number of available vehicles and their transmission frequency.
7 citations
Cites background from "Tool release: gathering 802.11n tra..."
...The process of updating state Xt and its variance Pt for a pedestrian is briefly described as follows [25]:...
[...]
01 Aug 2019
TL;DR: In Deep-WiID, the Gated Recurrent Unit is combined with average pooling to extract gait features automatically from CSI data and to identify persons, which effectively reduces the overhead of data processing than traditional manual feature extraction.
Abstract: With the widespread popularization of commercial off-the-shelf WiFi devices, the device-free WiFi sensing has attracted attention extensively. At present, some studies have explored the feasibility of WiFi-based human identification, but existing methods are facing the problem of heavy workload and low recognition accuracy. Aiming at these issues, we propose a deep learning method, named Deep-WiID, to analyze the gait feature using Channel State Information(CSI) so as to identify persons. In Deep-WiID, the Gated Recurrent Unit is combined with average pooling to extract gait features automatically from CSI data and to identify persons, which effectively reduces the overhead of data processing than traditional manual feature extraction. Experimental results conducted on CSI data collected from different situations indicate that Deep-WiID has desirable identification accuracy and good robustness. The average identification accuracy of our model is ranging from 99.7% to 97.7% when the number of persons is from 2 to 6, and there is still a desirable performance of 92.5% in larger group of 15 persons.
7 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...04 with custom Intel NIC driver [26] in the laptop to collect the raw CSI data....
[...]
TL;DR: New preprocessing and filtering techniques are used, new features to be extracted from the data and new classification method that have not been used in this field before are proposed and proved to work well for different humans and different gestures.
Abstract: Purpose
Recently, many researches have been devoted to studying the possibility of using wireless signals of the Wi-Fi networks in human-gesture recognition. They focus on classifying gestures despite who is performing them, and only a few of the previous work make use of the wireless channel state information in identifying humans. This paper aims to recognize different humans and their multiple gestures in an indoor environment.
Design/methodology/approach
The authors designed a gesture recognition system that consists of channel state information data collection, preprocessing, features extraction and classification to guess the human and the gesture in the vicinity of a Wi-Fi-enabled device with modified Wi-Fi-device driver to collect the channel state information, and process it in real time.
Findings
The proposed system proved to work well for different humans and different gestures with an accuracy that ranges from 87 per cent for multiple humans and multiple gestures to 98 per cent for individual humans’ gesture recognition.
Originality/value
This paper used new preprocessing and filtering techniques, proposed new features to be extracted from the data and new classification method that have not been used in this field before.
7 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...The tool in Halperin et al. (2011) collects the CSI between the transmitter and receiver in the form of 3D matrix that is represented as Nt Nr 30, where Nt is the number of transmitter antennas (three in our case), and theNr is the number of receiver antennas (three in our case), and 30 represents…...
[...]
...Figure 1 shows a sample of the used equipment in CSI data collection after installing the modified Wi-Fi-device drivers from Halperin et al. (2011) in the wireless local area network (WLAN) cards....
[...]
...…in the form of 3D matrix that is represented as Nt Nr 30, where Nt is the number of transmitter antennas (three in our case), and theNr is the number of receiver antennas (three in our case), and 30 represents the number of subcarriers in the OFDM channel that it collects (Halperin et al., 2011)....
[...]
...CSI of the wireless signals are available in many commercial devices like the Intel 5300 (Halperin et al., 2011) and the Atheros 9390 network interface cards (NIC) (Sen et al....
[...]
...(2011) collects the CSI between the transmitter and receiver in the form of 3D matrix that is represented as Nt Nr 30, where Nt is the number of transmitter antennas (three in our case), and theNr is the number of receiver antennas (three in our case), and 30 represents the number of subcarriers in the OFDM channel that it collects (Halperin et al., 2011)....
[...]
01 Jun 2017
TL;DR: A novel synthetic aperture RFID localization method which combines RFID phase based ranging with synthetic aperture technology, to achieve a higher radial accuracy than the existing systems, suitable for locating tags placed densely in many IoT applications, such as test tubes in hospitals.
Abstract: Internet of Things (IoT) is rather prevalent in many manufacturing and smart city applications, while localization is a premise for many other processes, varying from ordering objects in manufacturing lines to locating books on bookshelves. Radio Frequency Identification (RFID) based localization is of great interest in many IoT applications. Synthetic aperture RFID, due to its anti-noise capability and robustness against multipath distortion, is becoming a rising star in the field of localization. Existing systems achieve finer lateral resolution, whereas their radial accuracy is limited by the narrow bandwidth of RFID signal. In this paper, we present a novel synthetic aperture RFID localization method which combines RFID phase based ranging with synthetic aperture technology, to achieve a higher radial accuracy than the existing systems. With only one reader antenna and one 1-dimensional (1D) trajectory, a synthetic array is constructed to get an accurate localization result both in lateral and radial direction. Its core idea is to make full use of the coherence of all multi-frequency phase data and merge them into a unique ranging based likelihood function. To improve the accuracy, the relative phase is leveraged to eliminate phase offsets caused by the reader antenna, and the phase deviation from the angle-of-arrival response is calibrated by pre-processing. Then a weighted enhancement is fully exploited to further improve the localization performance. We evaluate its performance with commercial-off-the-shelf (COTS) RFID devices and the results show that it achieves median accuracy of 3cm in both lateral and radial direction. This novel promising method is suitable for locating tags placed densely in many IoT applications, such as test tubes in hospitals.
7 citations
Cites background from "Tool release: gathering 802.11n tra..."
...Finer-grained Channel state information (CSI) can be captured by some Wi-Fi devices, which contains the signal phase and amplitude of each sub-carrier [22]....
[...]
References
More filters
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....
[...]
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