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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07 Dec 2015
TL;DR: This work proposes a novel authentication framework for 802.11 that utilizing the communication history information readily available at the transmitter and receiver in establishing a shared key and protects all management frames from spoofing attacks, and evaluates its authentication scheme using application layer history with and without RSSI information.
Abstract: Wi-Fi technology is one of the key enablers of the Internet-of-Things (IoT) revolution that will allow the interaction of tens of billions of smart devices. The lack of authentication for management frames in IEEE 802.11 creates a major cyber-security vulnerability for the IoT and the whole Wi-Fi ecosystem. While in recent amendments WPA2 may be used to encrypt and authenticate deauthentication/disassociation management frames if a shared secret key has already been established, no such mechanism exists to verify the legitimacy of an AP with a known name, i.e, an AP with a history of legitimate connections in the past, at the time a client is trying to establish a connection. We propose a novel authentication framework for 802.11 that solves this problem by utilizing the communication history information readily available at the transmitter and receiver in establishing a shared key and protects all management frames from spoofing attacks. A management frame authentication code is generated at the transmitter using this information, and attached to the management frame in order to be verified at the receiver. Our framework is flexible enough to work with various types of application and PHY layer data, and is scalable enough to be applied to devices with different computing and processing capabilities. We have adapted the well-known Knapsack cryptosystem to our communication history-based authentication scheme, as it is simple to implement and provides strong security for our application scenarios. We evaluate our authentication scheme using application layer history with and without RSSI information, and show that using RSSI in addition to data packet history can provide the combined benefits of high entropy secret bit generation and increased protection against packet sniffing attacks. Through experimental work, we show that our communication history based approach provides a robust, low-complexity, and scalable authentication method for management frames in 802.11, and potentially other wireless networks.
6 citations
Cites background from "Tool release: gathering 802.11n tra..."
...…is worth noting that in OFDM-based 802.11 systems, samples of the channel frequency response are already built into the channel state information on a subcarrier group basis [20], and can be utilized for improving WiFi security using either temporal or frequency-domain channel responses [17], [19]....
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01 Jan 2021
TL;DR: In this article, the authors analyzed different modalities of contactless human activity analysis and arranged them into three major categories: RF-based, sound-based and vision-based modalities.
Abstract: Human Activity Analysis (HAA) is a prominent research field in this modern era which has enlightened us with the opportunities of monitoring regular activities or the surrounding environment as per our desire. In recent times, Contactless Human Activity Analysis (CHAA) has added a new dimension in this domain as these systems perform without any wearable device or any kind of physical contact with the user. We have analyzed different modalities of CHAA and arranged them into three major categories: RF-based, sound-based, and vision-based modalities. In this chapter, we have presented state-of-the-art modalities, frequently faced challenges with some probable solutions, and currently used applications of CHAA with future directions.
6 citations
12 Nov 2017
TL;DR: The proposed system is able to authenticate in-air signatures which are captured through Wi-Fi signals and shows an average equal error rate on an in-house dataset which consists of 1040 samples collected from 13 subjects.
Abstract: This paper presents a Wi-Fi based system for in-air signature verification. The proposed system is able to authenticate in-air signatures which are captured through Wi-Fi signals. The system consists of four main stages namely, data acquisition, preprocessing, feature extraction and matching. The proposed system shows an average equal error rate of 4.31% on an in-house dataset which consists of 1040 samples collected from 13 subjects. This experiment shows that the Wi-Fi signals can be applied to in-air signature verification effectively.
6 citations
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TL;DR: GoPose is presented, a 3D skeleton-based human pose estimation system that uses WiFi devices at home that does not require a user to wear or carry any sensors and can reuse the WiFi devices that already exist in a home environment for mass adoption.
Abstract: This paper presents GoPose, a 3D skeleton-based human pose estimation system that uses WiFi devices at home. Our system leverages the WiFi signals reflected off the human body for 3D pose estimation. In contrast to prior systems that need specialized hardware or dedicated sensors, our system does not require a user to wear or carry any sensors and can reuse the WiFi devices that already exist in a home environment for mass adoption. To realize such a system, we leverage the 2D AoA spectrum of the signals reflected from the human body and the deep learning techniques. In particular, the 2D AoA spectrum is proposed to locate different parts of the human body as well as to enable environment-independent pose estimation. Deep learning is incorporated to model the complex relationship between the 2D AoA spectrums and the 3D skeletons of the human body for pose tracking. Our evaluation results show GoPose achieves around 4.7cm of accuracy under various scenarios including tracking unseen activities and under NLoS scenarios.
6 citations
10 Nov 2019
TL;DR: Results show channel state information is accurate for single participants, but sensitive to different participants and fluctuating WiFi signals over days, and both clustering and transfer learning can be applied to increase the performance to 0.80 when using minimal resources and retraining.
Abstract: Unobtrusive sensing is receiving much attention in recent years, as it is less obtrusive and more privacy-aware compared to other monitoring technologies. Human activity recognition is one of the fields in which unobtrusive sensing is heavily researched, as this is especially important in health care. In this regard, investigating WiFi signals, and more specifically 802.11n channel state information, is one of the more prominent research fields. However, there is a challenge in scaling it up. Transfer learning is rarely applied, and when applied, it is done on filtered/modified data or extracted features. This paper focuses on two aspects. First, convolutional networks are used across multiple participants, days and activities and analysis is done based on these results. Secondly, it looks into the possibility of applying transfer learning based on raw channel state information over multiple participants and activities over multiple days. Results show channel state information is accurate for single participants (F1-score of 0.90), but sensitive to different participants and fluctuating WiFi signals over days (F1-score of 0.25-0.35). Furthermore, results show both clustering and transfer learning can be applied to increase the performance to 0.80 when using minimal resources and retraining.
6 citations
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