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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TL;DR: This article recognizes multiple human activities in an Internet-of-Things (IoT) network using differential channel state information (CSI) of the available wireless fidelity (Wi-Fi) signals using long short-term memory (LSTM) model for automatic feature extraction and classification of human activities from the differential CSI.
Abstract: In this article, we recognize multiple human activities in an Internet-of-Things (IoT) network using differential channel state information (CSI) of the available wireless fidelity (Wi-Fi) signals. Different human activities in the Wi-Fi environment lead to multipath fading, resulting in a change of CSI for each activity. This CSI is sensed by smart IoT devices, such as smartphones, tablets, and laptops for activity recognition. The use of differential CSI mitigates the offset and background noise. Another advantage of the proposed method is that it eliminates the requirement of traditional wearable activity recognition sensors, such as gyroscope, pedometers, and accelerometers. A long short-term memory (LSTM) model is used for automatic feature extraction and classification of human activities from the differential CSI. Training the LSTM model with the phase of differential denoised CSI significantly improves the classification accuracy. The results show a good tradeoff between model complexity and classification accuracy, thereby ensuring better performance as compared to the previous state-of-the-art methods.
26 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...11n MIMO radio, using a custom modified firmware and open-source Linux wireless drivers [5]....
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01 Nov 2016
TL;DR: Harmony is proposed, a human activity recognition and monitoring middleware which can utilize the coarse-grained received signal strength (RSS) measurements from the radios of IoT devices and can achieve similar accuracy as fine- grained WiFi channel state information (CSI) measurement-based approaches.
Abstract: The emerging smart health and smart home applications require pervasive and non-intrusive human activity recognition and monitoring. Traditional technologies (e.g., using cameras or accelerometers and gyroscopes) may introduce privacy issues or require people to wear sensors. To address these issues, recent approaches exploit fine-grained wireless signals for activity recognition. However, these approaches require devices that are costly or need to provide unique wireless features (e.g., Doppler shifts or phase information). With the increasingly available Internet of Things (IoT) devices, in this paper, we propose Harmony, a human activity recognition and monitoring middleware which can utilize the coarse-grained (but pervasively available) received signal strength (RSS) measurements from the radios of IoT devices. We implement a complete evaluation platform (from data collection to data analysis) of the middleware on top of low cost ZigBee compliant MICAz nodes and a laptop. We also conducted extensive experiments. Our results show that our design can achieve similar accuracy as fine-grained WiFi channel state information (CSI) measurement-based approaches. Specifically, our overall human activities recognition accuracy is up to 74% and 90% for RSS readings from a single pair and 3 pairs of IoT devices, respectively.
26 citations
Cites background from "Tool release: gathering 802.11n tra..."
...…rates (e.g., 1,000 samples for ZigBee, while 2,500 samples for WiFi); (iii) RSS values are extremely coarse-grained (e.g., 8 bits for a MICAz node) when compared with the fine-grained channel state information (e.g., Intel 5300 [11] has 3 antennas and each antenna has 30 groups of subcarriers....
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TL;DR: SDR-Fi is presented, the first reported Wi-Fi software-defined radio (SDR) receiver for indoor positioning using CSI measurements as features for deep learning (DL) classification and two state-of-the-art DL classification methods outperforming traditional RSS-based methods for low AP scenarios.
Abstract: Wi-Fi fingerprinting-based indoor localization has received increased attention due to its proven accuracy and global availability. The common received-signal-strength-based (RSS) fingerprinting presents performance degradation due to well-known signal fluctuations, but more recently, the more stable channel state information (CSI) has gained popularity. In this paper, we present SDR-Fi, the first reported Wi-Fi software-defined radio (SDR) receiver for indoor positioning using CSI measurements as features for deep learning (DL) classification. The CSI measurements are obtained from a fast-prototyping LabVIEW-based 802.11n SDR receiver platform. SDR-Fi measures CSI data passively from pilot beacon frames from a single access point (AP) at almost 10 Hz rate. A feed-forward neural network and a 1D convolutional neural network are examined to estimate location accuracy in representative testing scenarios for an indoor cluttered laboratory area, and an adjacent, covered outdoor area. The proposed DL classification methods leverage CSI-based fingerprinting for low AP scenarios, as opposed to traditional RSS-based systems, which require many APs for reliable positioning. Demonstration results are threefold: (a) A fast-prototyping SDR platform that passively extracts CSI measurements from Wi-Fi beacon frames, providing a genuine possibility for vendor network cards to provide such measurements, (b) two state-of-the-art DL classification methods outperforming traditional RSS-based methods for low AP scenarios, (c) a testing methodology for performance evaluation of the proposed indoor positioning system.
26 citations
Cites methods from "Tool release: gathering 802.11n tra..."
..., Intel 5300 [31], or Atheros AR9580 [32]....
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...Recently, modified firmware on the Intel 5300 NIC called CSI Tool, has been made available to access CSI measurements [31]....
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TL;DR: A smart attendance system that extracts distinguishable phase characteristics of individuals to enable recognition of various targets and the design is robust against differences in the clothing worn and time of day, which further verifies the successful performance of the system.
26 citations
28 Nov 2017
TL;DR: The design and implementation of AWL is presented, an accurate indoor localization system that enables a single WiFi AP to achieve decimeter-level accuracy with only one channel hopping, and the widely known "bad" spatial aliasing is utilized to improve the AoA estimation accuracy.
Abstract: Owing to great potential in smart home and human-computer interactive applications, WiFi indoor localization has attracted extensive attentions in the past several years. The state-of-the-art systems have successfully achieved decimeter-level accuracies. However, the high accuracy is acquired at the cost of dense access point (AP) deployment, employing large size of frequency bandwidths or special-purpose radar signals which are not compatible with existing WiFi protocol, limiting their practical deployments. This paper presents the design and implementation of AWL, an accurate indoor localization system that enables a single WiFi AP to achieve decimeter-level accuracy with only one channel hopping. The key enabler of the system is we novelly employ channel hopping to create virtual antennas, without the need of adding more antennas or physically move the antennas' positions for a larger antenna array. We successfully utilize the widely known "bad" spatial aliasing to improve the AoA estimation accuracy. A novel multipath suppression scheme is also proposed to combat the severe multipath issue indoors. We build a prototype of AWL on WARP software-defined radio platform. Comprehensive experiments manifest that AWL achieves a median localization accuracy of 38 cm in a rich multipath indoor environment with only a single AP equipped with 6 antennas. In a small scale area, AWL is able to accurately track a moving device's trajectory, enabling applications such as writing/drawing in the air.
26 citations
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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.
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