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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31 Dec 2017
TL;DR: This paper takes the first attempt to achieve non-invasive abnormal activity detection with only commodity off-the-shelf (COTS) WiFi devices, namely NotiFi, that can accurately detect the abnormal activities.
Abstract: Abnormal activity detection has increasingly attracted significant research attention due to its potential applications in numerous scenarios, such as patient monitoring, health care of children and elderly, military surveillance, etc. Pioneer systems usually rely on computer vision or wearable sensors which pose unacceptable privacy risks, or wireless signals which require the priori learning of wireless signals to recognize a set of predefined activities. In this paper, we take the first attempt to achieve non-invasive abnormal activity detection with only commodity off-the-shelf (COTS) WiFi devices, namely NotiFi, that can accurately detect the abnormal activities. The intuition of NotiFi is that whenever the human body occludes the wireless signal transmitting from the access point to the receiver, the phase and the amplitude information of Channel State Information (CSI) will experience a sensitive variation. By creating a multiple hierarchical Dirichlet processes, NotiFi automatically learn the number of human body activity categories for abnormal detection. Extensive experiments in typical realworld environments indicate that NotiFi can achieve satisfactory performance in abnormal activity detection.
1 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...11 data frames by modifying the driver as described in [26]....
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02 Feb 2023
TL;DR: In this paper , the authors performed an extensive CSI data collection campaign involving 3 individuals, 3 environments, and 12 activities, using Wi-Fi 6 signals, which contains almost two hours of CSI data from three collectors.
Abstract: Thanks to the ubiquitous deployment of Wi-Fi hotspots, channel state information (CSI)-based Wi-Fi sensing can unleash game-changing applications in many fields, such as healthcare, security, and entertainment. However, despite one decade of active research on Wi-Fi sensing, most existing work only considers legacy IEEE 802.11n devices, often in particular and strictly-controlled environments. Worse yet, there is a fundamental lack of understanding of the impact on CSI-based sensing of modern Wi-Fi features, such as 160-MHz bandwidth, multiple-input multiple-output (MIMO) transmissions, and increased spectral resolution in IEEE 802.11ax (Wi-Fi 6). This work aims to shed light on the impact of Wi-Fi 6 features on the sensing performance and to create a benchmark for future research on Wi-Fi sensing. To this end, we perform an extensive CSI data collection campaign involving 3 individuals, 3 environments, and 12 activities, using Wi-Fi 6 signals. An anonymized ground truth obtained through video recording accompanies our 80-GB dataset, which contains almost two hours of CSI data from three collectors. We leverage our dataset to dissect the performance of a state-of-the-art sensing framework across different environments and individuals. Our key findings suggest that (i) MIMO transmissions and higher spectral resolution might be more beneficial than larger bandwidth for sensing applications; (ii) there is a pressing need to standardize research on Wi-Fi sensing because the path towards a truly environment-independent framework is still uncertain. To ease the experiments' replicability and address the current lack of Wi-Fi 6 CSI datasets, we release our 80-GB dataset to the community.
1 citations
05 Jul 2021
TL;DR: In this paper, a machine learning methodology based on passive electromagnetic sensing that exploits commodity Wi-Fi signals is proposed, which has been preliminary validated in a real house environment with a classification accuracy of 98%.
Abstract: The study and the design of novel methodologies and techniques for user's activity and gesture recognition is of great interest and a hot topic in human-computer interactions. Hand gesture recognition techniques based on computer-vision have yielded impressive results, but they involve users’ privacy concerns, therefore other sensing approaches are of interest. In this work, a novel machine learning methodology based on passive electromagnetic sensing that exploits commodity Wi-Fi signals is proposed. Such an approach has been preliminary validated in a real house environment with a classification accuracy of 98%.
1 citations
28 Jun 2021
TL;DR: In this article, a distributed indoor positioning system (CLRS) with high robustness is proposed, which uses Wi-Fi signals to divide the space twice based on Angle of Arrival (AoA) and Effective Channel State Information (ECSI).
Abstract: Wi-Fi-based indoor localization gained a lot of attention over recent years due to low cost and open access properties. However, existing schemes might not be applicable in the real environment if their robustness is low. This paper presents CLRS, a novel distributed Indoor Positioning System (IPS) with high robustness which uses Wi-Fi signals to divide the space twice based on Angle of Arrival (AoA) and Effective Channel State Information (ECSI). The proposed scheme trade the redundancy of Access Point (AP) quantity to improve the tolerance of data measurement error. We performed simulations as well as real-world experiments, in which simulation results proved that the theoretical average error is the least when the routers are placed vertically in our localization method while the real-world experiments proved the high accuracy and robustness of CLRS.
1 citations
TL;DR: Wang et al. as mentioned in this paper proposed a novel positioning framework based on multiple transfer learning fusion using Generalized Policy Iteration (GPI), which can not only pay more attention to the information of different dimensions of fingerprints, but also compress redundant information and reduce noise.
Abstract: Indoor localization service is an indispensable part of modern intelligent life, among which Wi-Fi based fingerprint localization system is popular in indoor positioning researches due to its advantages of low cost and widely deployment. However, Wi-Fi based localization system is susceptible to dynamic environment, and fingerprint collection and updating are time-consuming and labor-intensive. To address this problem, we propose a novel positioning framework based on multiple transfer learning fusion using Generalized Policy Iteration (GPI). Firstly, a 1-Dimension Convolutional Autoencoder (1-D CAE) is designed to extract features from one-dimensional fingerprint data; similar to Convolutional Neural Network (CNN), it can not only pay more attention to the information of different dimensions of fingerprints, but also compress redundant information and reduce noise. After that, Domain Adversarial Neural Network (DANN) and Passive Aggressive (PA) algorithm are fused to train localization model based on unlabeled fingerprint of target domain using the theory of GPI in offline stage. Finally, the model is fine-tuned with unlabeled fingerprints and few labeled fingerprints in daily online predictions to improve the performance of the localization system. Various evaluations in five typical scenarios validate the effectiveness of proposed algorithm in dynamic environment, with low tendency, easy recalibration, long-term stabilization high accuracy and so on.
1 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