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.
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01 Nov 2020
TL;DR: A data interaction framework is developed and publicly released, capable of interpreting, processing and visualising data from a range of CSI-capable hardware, and training a Deep Convolutional LSTM model to classify the activities.
Abstract: Ambient, non-intrusive approaches to smart home health monitoring, while limited in capability, are preferred by residents. More intrusive methods of sensing, such as video and wearables, can offer richer data but at the cost of lower resident uptake, in part due to privacy concerns. A radio frequency-based approach to sensing, Channel State Information (CSI), can make use of low cost off-the-shelf WiFi hardware. We have implemented an activity recognition system on the Raspberry Pi 4, one of the world's most popular embedded boards. We have implemented an classification system using the Pi to demonstrate its capability for activity recognition. This involves performing data collection, interpretation and windowing, before supplying the data to a classification model. In this paper, the capabilities of the Raspberry Pi 4 at performing activity recognition on CSI data are investigated. We have developed and publicly released a data interaction framework, capable of interpreting, processing and visualising data from a range of CSI-capable hardware. Furthermore, CSI data captured for these experiments during various activity performances have also been made publicly available. We then train a Deep Convolutional LSTM model to classify the activities. Our experiments, performed in a small apartment, achieve 92% average accuracy on 11 activity classes.
21 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...11n CSI Tool was released in 2011 [7], CSI research has primarily been performed using the Intel IWL5300 wireless card....
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...11n CSI Tool was released in 2011 [7], it became the most accessible way to extract CSI using a standard hardware configuration with an Intel IWL5300....
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TL;DR: A novel bivariate kernel regression scheme is proposed to precisely infer the target’s location by exploiting both CSI amplitude and phase information, and results from extensive indoor experiments validate the superior localization performance of the proposed system over previous approaches.
Abstract: With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches.
21 citations
17 Sep 2021
TL;DR: In this paper, an indoor navigation system based on a combination of wall-mounted wireless sensors, a mobile health application (mHealth app), and WiFi/Bluetooth beacons is presented.
Abstract: A handheld device (such as a smartphone/wearable) can be used for tracking and delivering navigation within a building using a wireless interface (such as WiFi or Bluetooth Low Energy), in situations when a traditional navigation system (such as a global positioning system) is unable to function effectively. In this paper, we present an indoor navigation system based on a combination of wall-mounted wireless sensors, a mobile health application (mHealth app), and WiFi/Bluetooth beacons. Such a system can be used to track and trace people with neurological disorders, such as Alzheimer’s disease (AD) patients, throughout the hospital complex. The Contact tracing is accomplished by using Bluetooth low-energy beacons to detect and monitor the possibilities of those who have been exposed to communicable diseases such as COVID-19. The communication flow between the mHealth app and the cloud-based framework is explained elaborately in the paper. The system provides a real-time remote monitoring system for primary medical care in cases where relatives of Alzheimer’s patients and doctors are having complications that may demand medical care or hospitalization. The proposed indoor navigation system has been found to be useful in assisting patients with Alzheimer’s disease (AD) while in the hospital building.
21 citations
TL;DR: This paper designs a novel wireless system using commercial off-the-shelf RFID readers and tags to provide a general and effective means of measuring bodily oscillation rates, such as the hand tremor rate of a patient with Parkinson’s disease.
Abstract: Traditional systems for monitoring and diagnosing patients’ health conditions often require either dedicated medical devices or complicated system deployment, which incurs high cost. The networking research community has recently taken a different technical approach of building health-monitoring systems at relatively low cost based on wireless signals. However, the radio frequency signals carry various types of noise and have time-varying properties that often defy the existing methods in more demanding conditions with other body movements, which makes it difficult to model and analyze the signals mathematically. In this paper, we design a novel wireless system using commercial off-the-shelf RFID readers and tags to provide a general and effective means of measuring bodily oscillation rates, such as the hand tremor rate of a patient with Parkinson’s disease. Our system includes a series of noise-removal steps, targeting at noise from different sources. More importantly, it introduces two sliding window-based methods to deal with time-varying signal properties from channel dynamics and irregular body movement. The proposed system can measure bodily oscillation rates of multiple persons simultaneously. Extensive experiments show that our system can produce accurate measurement results with errors less than 0.4 oscillations per second when it is applied to monitor hand tremor, even when the individuals are moving.
21 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Since UniBreathe and CSI are only able to concurrently measure up to two persons, we only evaluate the two-person case and the result is shown in Fig....
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...It extracts the fine-grained channel state information (CSI) [20] using WiFi devices to track the breathing rate and the heart-beat rate of a person in bed....
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...We compare the performance of our system with UbiBreathe [6], CSI [34], and TagBreathe [18], which are designed specifically for this task....
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...Some systems use Doppler radars [34], ultrawideband (UWB) radars [35], [36], or FMCWs radars [5]; others rely on measurement of RSS [3], [6], CSI [4], [7], or phase values [18], [21]....
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...For example, when the person breathes at a rate of 18 b/min, the mean measurement errors of our system, TagBreathe, CSI, and UbiBreathe are 0.19, 0.2, 0.2, and 0.25 b/min, respectively....
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TL;DR: HAR-SAnet is a novel RF-based HAR framework that adopts an original signal adapted convolutional neural network architecture that substantially outperforms the state-of-the-art algorithms and systems.
Abstract: Human Activity Recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting RF signals arise as a promising alternative for HAR. Most of the latest device-free approaches require training a large deep neural network model in either time or frequency domain, entailing extensive storage to contain the model and intensive computations to infer activities. Consequently, even with some major advances on device-free HAR, current device-free approaches are still far from practical in real-world scenarios where the computation and storage resources possessed by, for example, edge devices, are limited. Therefore, we introduce HAR-SAnet which is a novel RF-based HAR framework. It adopts an original signal adapted convolutional neural network architecture: instead of feeding the handcraft features of RF signals into a classifier, HAR-SAnet fuses them adaptively from both time and frequency domains to design an end-to-end neural network model. We apply point-wise grouped convolution and depth-wise separable convolutions to confine the model scale and to speed up the inference execution time. The experiment results show that the recognition accuracy of HAR-SAnet outperforms state-of-the-art algorithms and systems.
21 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...To better understand the limitation of Wi-Fi in terms of sensing resolution, we use the Intel 5300 Wi-Fi card [31] to collect a few data samples of activities at a 400Hz sampling rate....
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...11n Wi-Fi cards (Intel 5300 [31] and Atheros 9390 network interface cards (NICs) [57])....
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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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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.
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