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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05 Jul 2016
TL;DR: This paper presents fine-grained finger gesture recognition by using a single commodity WiFi device without requiring user to wear any sensors and proposes to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency.
Abstract: Gesture recognition has become increasingly important in human-computer interaction (HCI) and can support a broad array of emerging applications, such as smart home, virtual reality, and mobile gaming. Traditional approaches usually rely on dedicated sensors that are worn by the user or cameras that require line of sight. In this paper, we present fine-grained finger gesture recognition by using a single commodity WiFi device without requiring user to wear any sensors. Our low-cost system, WiFinger, takes advantages of the fine-grained Channel State Information (CSI) available from commodity WiFi devices and the prevalence of WiFi network infrastructures. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. In WiFigner, we devise environmental noise removal mechanism to mitigate the effect of signal dynamic due to the environment changes. Moreover, we propose to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency. Our experimental evaluation in both home and office environments demonstrates that our system can achieve over 93% recognition accuracy and is robust to both environment changes and individual diversity. Results also show that our system can work with WiFi beacon signals and provides accurate gesture recognition under NLOS scenarios.
224 citations
13 Aug 2012
TL;DR: JMB is implemented and tested with both software radio clients and off-the-shelf 802.11n cards, and evaluated in a dense congested deployment resembling a conference room, showing a linear increase in network throughput with a median gain of 8.4x.
Abstract: We present joint multi-user beamforming (JMB), a system that enables independent access points (APs) to beamform their signals, and communicate with their clients on the same channel as if they were one large MIMO transmitter. The key enabling technology behind JMB is a new low-overhead technique for synchronizing the phase of multiple transmitters in a distributed manner. The design allows a wireless LAN to scale its throughput by continually adding more APs on the same channel. JMB is implemented and tested with both software radio clients and off-the-shelf 802.11n cards, and evaluated in a dense congested deployment resembling a conference room. Results from a 10-AP software-radio testbed show a linear increase in network throughput with a median gain of 8.1 to 9.4x. Our results also demonstrate that JMB's joint multi-user beamforming can provide throughput gains with unmodified 802.11n cards.
220 citations
14 Apr 2013
TL;DR: This paper shows that exploiting the channel response from multiple Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers can provide fine-grained channel information and achieve higher bit generation rate for both static and mobile cases in real-world scenarios, and develops a Channel Gain Complement (CGC) assisted secret key extraction scheme to cope with channel non-reciprocity encountered in practice.
Abstract: Securing wireless communication remains challenging in dynamic mobile environments due to the shared nature of wireless medium and lacking of fixed key management infrastructures. Generating secret keys using physical layer information thus has drawn much attention to complement traditional cryptographic-based methods. Although recent work has demonstrated that Received Signal Strength (RSS) based secret key extraction is practical, existing RSS-based key generation techniques are largely limited in the rate they generate secret bits and are mainly applicable to mobile wireless networks. In this paper, we show that exploiting the channel response from multiple Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers can provide fine-grained channel information and achieve higher bit generation rate for both static and mobile cases in real-world scenarios. We further develop a Channel Gain Complement (CGC) assisted secret key extraction scheme to cope with channel non-reciprocity encountered in practice. Our extensive experiments using WiFi networks in both indoor as well as outdoor environments demonstrate that our approach can achieve significantly faster secret bit generation rate at 60 ~ 90bit/packet, and is resilient to malicious attacks identified to be harmful to RSS-based techniques including predictable channel attack and stalking attack.
215 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...For each packet, we extract CSI for 30 subcarrier groups, which are evenly distributed in the 56 subcarriers of a 20MHz channel [10]....
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...In particular, we show that by using the Intel 5300 WiFi card [10], multiple subcarriers information can be extracted from a single 802....
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...Figure 1 (a) depicts the amplitude of channel response across 30 subcarriers at four different time points and positions extracted from an Intel WiFi 5300 card in a laptop [10]....
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26 May 2016
TL;DR: For the first time WiFi signals can also be used to uniquely identify people and a system called WiFi-ID is proposed that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow for uniquely identify that person.
Abstract: Prior research has shown the potential of device-free WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual's gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. We implement WiFi-ID on commercial off-the-shelf devices. We conduct extensive experiments to demonstrate that our system can uniquely identify people with average accuracy of 93% to 77% from a group of 2 to 6 people, respectively. We envisage that this technology can find many applications in small office or smart home settings.
214 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...10 with modified Intel NIC driver [5] in the HP laptop....
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...The WiFi NICs continuously monitor the frequency response of OFDM subcarriers as Channel State Information (CSI) [28]....
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...We installed Ubuntu 10.10 with modified Intel NIC driver [5] in the HP laptop....
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...The customised driver of the Intel 5300 NIC [5] which is used in our experiments reports 30 OFDM subcarriers of 802....
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...The customised driver of the Intel 5300 NIC [5] which is used in our experiments reports 30 OFDM subcarriers of 802.11n between each antenna pair (i.e....
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03 Oct 2016
TL;DR: LIFS, a Low human-effort, device-free localization system with fine-grained subcarrier information, which can localize a target accurately without offline training, outperforming the state-of-the-art systems.
Abstract: Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization. We design, implement and evaluate LiFS with extensive experiments in three different environments. Without knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios respectively, outperforming the state-of-the-art systems. Besides single target localization, LiFS is able to differentiate two sparsely-located targets and localize each of them at a high accuracy.
212 citations
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....
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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