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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30 Sep 2013
TL;DR: The experimental results show that in a busy office environment, SecureArray is orders of magnitude more accurate than current techniques, mitigating 100% of WiFi spoofing attack attempts while at the same time triggering false alarms on just 0.6% of legitimate traffic.
Abstract: Despite the important role that WiFi networks play in home and enterprise networks they are relatively weak from a security standpoint. With easily available directional antennas, attackers can be physically located off-site, yet compromise WiFi security protocols such as WEP, WPA, and even to some extent WPA2 through a range of exploits specific to those protocols, or simply by running dictionary and human-factors attacks on users' poorly-chosen passwords. This presents a security risk to the entire home or enterprise network. To mitigate this ongoing problem, we propose SecureArray, a system designed to operate alongside existing wireless security protocols, adding defense in depth against active attacks. SecureArray's novel signal processing techniques leverage multi-antenna access point (AP) to profile the directions at which a client's signals arrive, using this angle-of-arrival (AoA) information to construct highly sensitive signatures that with very high probability uniquely identify each client. Upon overhearing a suspicious transmission, the client and AP initiate an AoA signature-based challenge-response protocol to confirm and mitigate the threat. We also discuss how SecureArray can mitigate direct denial-of-service attacks on the latest 802.11 wireless security protocol. We have implemented SecureArray with an eight-antenna WARP hardware radio acting as the AP. Our experimental results show that in a busy office environment, SecureArray is orders of magnitude more accurate than current techniques, mitigating 100% of WiFi spoofing attack attempts while at the same time triggering false alarms on just 0.6% of legitimate traffic. Detection rate remains high when the attacker is located only five centimeters away from the legitimate client, for AP with fewer numbers of antennas and when client is mobile.
137 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...[21] D. Halperin, W. Hu, A. Sheth, and D. Wetherall....
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...This is made possible on a commodity NIC by a CSI measurement tool previously released by Halperin [21]....
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...This is made possible on a commodity NIC by a CSI measurement tool previously released by Halperin [21]....
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05 Oct 2015
TL;DR: WiG is proposed, a device-free gesture recognition system based solely on Commercial Off-The-Shelf (COTS) WiFi infrastructures and devices that stands out for its systematic simplicity, extremely low cost and high practicability.
Abstract: Most recently, gesture recognition has increasingly attracted intense academic and industrial interest due to its various applications in daily life, such as home automation, mobile games. Present approaches for gesture recognition, mainly including vision-based, sensor-based and RF-based, all have certain limitations which hinder their practical use in some scenarios. For example, the vision-based approaches fail to work well in poor light conditions and the sensor-based ones require users to wear devices. To address these, we propose WiG in this paper, a device-free gesture recognition system based solely on Commercial Off-The-Shelf (COTS) WiFi infrastructures and devices. Compared with existing Radio Frequency (RF)-based systems, WiG stands out for its systematic simplicity, extremely low cost and high practicability. We implemented WiG in indoor environment and conducted experiments to evaluate its performance in two typical scenarios. The results demonstrate that WiG can achieve an average recognition accuracy of 92% in line-of-sight scenario and average accuracy of 88% in the none-line-of sight scenario.
136 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Based on the method proposed by Dan [15],...
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...Based on the method proposed by Dan [15], (a) Confusion Matrix for LOS Scenario (b) Confusion Matrix for NLOS Scenario....
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...In OFDM system, channel response can be extracted in the format of Channel State Information(CSI) [15], which is a fine-grained PHY layer information that estimates the channel property of a communication link at the subcarrier level....
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TL;DR: An accurate device-free passive (DfP) indoor location tracking system that adopts channel state information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards and demonstrates that this complex channel information enables more accurate localization of nonequipped individuals.
Abstract: The research on indoor localization has received great interest in recent years. This has been fueled by the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface. Analyzing the signal fluctuation on the RF-interface can, for instance, solve the still open issue of ubiquitous reliable indoor localization and tracking. Device bound and device free approaches with remarkable accuracy have been reported recently. In this paper, we present an accurate device-free passive (DfP) indoor location tracking system that adopts channel state information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for multiple input multiple output orthogonal frequency-division multiplexing PHY layer parameters are exploited to improve localization and tracking accuracy. To enable precise positioning in the presence of heavy multipath effects in cluttered indoor scenarios, we experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based technique as an accurate solution. Our scheme further boosts the localization efficiency by using principal component analysis to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we continuously track the trajectory of a moving subject. We have evaluated the performance of our system in four indoor environments and compared it with state-of-the-art indoor localization schemes. Our experimental results demonstrate that this complex channel information enables more accurate localization of nonequipped individuals.
130 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...[16], we can aggregate both the amplitude and phase information for each MIMO-OFDM subcarrier....
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...By using the modified driver released by [16], the receivers can probe one CSI reading per packet....
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...In the context of MIMO-OFDM, since the RSS is no longer a reliable indicator for the entire channel quality [16], in this section, we investigate the challenges posed by CSI measurements in solving the location estimate problem of cluttered environments....
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16 Apr 2020
TL;DR: WiPose is the first 3D human pose construction framework using commercial WiFi devices that addresses a series of technical challenges and can localize each joint on the human skeleton with an average error, achieving a 35% improvement in accuracy over the state-of-the-art posture construction model designed for dedicated radar sensors.
Abstract: This paper presents WiPose, the first 3D human pose construction framework using commercial WiFi devices. From the pervasive WiFi signals, WiPose can reconstruct 3D skeletons composed of the joints on both limbs and torso of the human body. By overcoming the technical challenges faced by traditional camera-based human perception solutions, such as lighting and occlusion, the proposed WiFi human sensing technique demonstrates the potential to enable a new generation of applications such as health care, assisted living, gaming, and virtual reality. WiPose is based on a novel deep learning model that addresses a series of technical challenges. First, WiPose can encode the prior knowledge of human skeleton into the posture construction process to ensure the estimated joints satisfy the skeletal structure of the human body. Second, to achieve cross environment generalization, WiPose takes as input a 3D velocity profile which can capture the movements of the whole 3D space, and thus separate posture-specific features from the static objects in the ambient environment. Finally, WiPose employs a recurrent neural network (RNN) and a smooth loss to enforce smooth movements of the generated skeletons. Our evaluation results on a real-world WiFi sensing testbed with distributed antennas show that WiPose can localize each joint on the human skeleton with an average error of 2.83cm, achieving a 35% improvement in accuracy over the state-of-the-art posture construction model designed for dedicated radar sensors.
129 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...11 CSI tools [13] are used on our testbed to log CSI data....
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01 Oct 2012
TL;DR: It is found that this approach to radio environment mapping is feasible and produces maps that are more accurate and informative than both explicitly tuned path loss models and basic data fitting approaches.
Abstract: In this paper we present results from the first application of robust geostatistical modeling techniques to radio environment and coverage mapping of wireless networks We perform our analysis of these methods with a case study mapping the coverage of a 25 GHz WiMax network at the University of Colorado, Boulder Drawing from our experiences, we propose several new methods and extensions to basic geostatistical theory that are necessary for use in a radio mapping application We also derive a set of best practices and discuss potential areas of future work We find that this approach to radio environment mapping is feasible and produces maps that are more accurate and informative than both explicitly tuned path loss models and basic data fitting approaches
128 citations
Cites background from "Tool release: gathering 802.11n tra..."
..., [28], [29]), suggest that future systems may be largely implemented with inexpensive and easily obtainable hardware, which may already be available in some end-user mobile devices....
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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.
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