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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18 Mar 2020
TL;DR: This poster presents a probabilistic procedure to estimate the intensity of earthquake-triggered landslides in China over a 25-year period from 1991 to 2002.
Abstract: Recently, significant efforts are made to explore device-free human activity recognition techniques that utilize the information collected by existing indoor wireless infrastructures without the need for the monitored subject to carry a dedicated device. Most of the existing work, however, focuses their attention on the analysis of the signal received by a single device. In practice, there are usually multiple devices "observing" the same subject. Each of these devices can be regarded as an information source and provides us an unique "view" of the observed subject. Intuitively, if we can combine the complementary information carried by the multiple views, we will be able to improve the activity recognition accuracy. Towards this end, we propose DeepMV, a unified multi-view deep learning framework, to learn informative representations of heterogeneous device-free data. DeepMV can combine different views' information weighted by the quality of their data and extract commonness shared across different environments to improve the recognition performance. To evaluate the proposed DeepMV model, we set up a testbed using commercialized WiFi and acoustic devices. Experiment results show that DeepMV can effectively recognize activities and outperform the state-of-the-art human activity recognition methods.
49 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Because of the release of Linux 802.11n CSI Tool [32], recently a lot of research work [22, 30, 47, 50, 69, 79, 81, 84, 96] have been conducted to utilize CSI to implement human activity recognition....
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...11n CSI extraction toolkit provided in [32]....
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...11n CSI Tool [32], recently a lot of research work [22, 30, 47, 50, 69, 79, 81, 84, 96] have been conducted to utilize CSI to implement human activity recognition....
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TL;DR: A three-phase system Wi-multi that targets at recognizing multiple human activities in a wireless environment and is able to achieve a desirable tradeoff between accuracy and efficiency in different phases is proposed.
Abstract: Channel state information-based activity recognition has gathered immense attention over recent years. Many existing works achieved desirable performance in various applications, including healthcare, security, and Internet of Things, with different machine learning algorithms. However, they usually fail to consider the availability of enough samples to be trained. Besides, many applications only focus on the scenario where only single subject presents. To address these challenges, in this paper, we propose a three-phase system Wi-multi that targets at recognizing multiple human activities in a wireless environment. Different system phases are applied according to the size of available collected samples. Specifically, distance-based classification using dynamic time warping is applied when there are few samples in the profile. Then, support vector machine is employed when representative features can be extracted from training samples. Lastly, recurrent neural networks is exploited when a large number of samples are available. Extensive experiments results show that Wi-multi achieves an accuracy of 96.1% on average. It is also able to achieve a desirable tradeoff between accuracy and efficiency in different phases.
49 citations
Cites background from "Tool release: gathering 802.11n tra..."
...Therefore, we are able to record 2 × 3 CSI streams between different pair of transmit– receive antennas by installing the tool on the Intel 5300 NIC on the laptop....
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...11n network [11], we are able to get CSI values of 30 subcarriers between one pair of transmit-receive antennas....
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...By modifying the driver of Intel 5300 network interface card (NIC) [11], many existing papers proposed various systems to detect human activities, such as keystroke [12], gestures [13], and breathing [14]....
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...By modifying the driver of Intel 5300 NIC in 802.11n network [11], we are able to get CSI values of 30 subcarriers between one pair of transmit-receive antennas....
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14 Jun 2015
TL;DR: This work proposes to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model.
Abstract: Indoor localization systems become more interesting for researchers because of the attractiveness of business cases in various application fields. A WiFi-based passive localization system can provide user location information to third-party providers of positioning services. However, indoor localization techniques are prone to multipath and Non-Line Of Sight (NLOS) propagation, which lead to significant performance degradation. To overcome these problems, we provide a passive localization system for WiFi targets with several improved algorithms for localization. Through Software Defined Radio (SDR) techniques, we extract Channel Impulse Response (CIR) information at the physical layer. CIR is later adopted to mitigate the multipath fading problem. We propose to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model. To mitigate the influence of ranging errors, a new trilateration algorithm is designed as well by combining Weighted Centroid and Constrained Weighted Least Square (WC-CWLS) algorithms. Experiment results show that our algorithm is robust against ranging errors and outperforms the linear least square algorithm and weighted centroid algorithm.
49 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...IEEE 802.11N PRELIMINARIES Before introducing our system, we will introduce some preliminary knowledges for the IEEE 802.11n standard, which are relevant to our work....
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...Limited by the current WiFi bandwidth and CSI information from the network card (30 CSI information from 64 subcarriers), the CIR information remains only sufficient to distinguish clusters of paths rather than individual multipath components....
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TL;DR: A public dataset for WiFi-based Activity Recognition named WiAR with sixteen activities operated by ten volunteers in three indoor environments is constructed and the accuracy of WiAR dataset is higher than 80% using machine learning algorithms and 90% using deep learning algorithms in different indoor environments.
Abstract: We construct a public dataset for WiFi-based Activity Recognition named WiAR with sixteen activities operated by ten volunteers in three indoor environments. It aims to provide public signal data for researchers to reduce the cost of collected signal data and conveniently evaluate the performance of WiFi-based human activity recognition in different domains. First, we introduce the basic knowledge of WiFi signals regarding RSSI, CSI, and wireless hardware. Second, we explain the characteristics of WiAR dataset in terms of activities types, data format, data acquisition ways, and influence factors. Third, the proposed framework can estimate the quality of the shared signal data provided by other peers. Finally, we select and use five classification algorithms and two deep learning algorithms to evaluate the performance of WiAR dataset on human activity recognition. The results show that the accuracy of WiAR dataset is higher than 80% using machine learning algorithms and 90% using deep learning algorithms in different indoor environments.
48 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...11n MIMO radios [19] and uses custom modified firmware....
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...With the CSI-tool being proposed by Halperin [19], researchers begin to use CSI to recognize human activities in terms of speed, direction, granularity....
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...Leveraging the offthe-shelf Intel 5300 NIC with a modified driver, a group of sampled versions of channel frequency response (CFR) within the WiFi bandwidth is revealed to upper layers in the format of channel state information [19]....
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19 Aug 2019
TL;DR: RIM turns a commodity WiFi device into an Inertial Measurement Unit (IMU) that can accurately track moving distance, heading direction, and rotating angle, requiring no additional infrastructure but a single arbitrarily placed Access Point (AP) whose location is unknown.
Abstract: Inertial measurements are critical to almost any mobile applications. It is usually achieved by dedicated sensors (e.g., accelerometer, gyroscope) that suffer from significant accumulative errors. This paper presents RIM, an RF-based Inertial Measurement system for precise motion processing. RIM turns a commodity WiFi device into an Inertial Measurement Unit (IMU) that can accurately track moving distance, heading direction, and rotating angle, requiring no additional infrastructure but a single arbitrarily placed Access Point (AP) whose location is unknown. RIM makes three key technical contributions. First, it presents a spatial-temporal virtual antenna retracing scheme that leverages multipath profiles as virtual antennas and underpins measurements of distance and orientation using commercial WiFi. Second, it introduces a super-resolution virtual antenna alignment algorithm that resolves sub-centimeter movements. Third, it presents an approach to handle measurement noises and thus delivers an accurate and robust system. Our experiments, over a multipath rich area of > 1,000 m2 with one single AP, show that RIM achieves a median error in moving distance of 2.3 cm and 8.4 cm for short-range and long-distance tracking respectively, and 6.1° mean error in heading direction, all significantly outperforming dedicated inertial sensors. We also demonstrate multiple RIM-enabled applications with great performance, including indoor tracking, handwriting, and gesture control.
47 citations
Additional excerpts
...To study higher sampling rates, we employ the 802.11 CSI Tool [10] for the Intel 5300 WiFi card equipped on laptops....
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...11 CSI Tool [10] for the Intel 5300 WiFi card equipped on laptops....
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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