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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01 Jun 2017
TL;DR: This paper proposes an accurate corruption estimation approach, AccuEst, which utilizes per-byte SINR (Signal-to-Interferenceand-Noise Ratio) to detect corruption, and proposes an adaptive pilot instrumentation scheme to strike a good balance between accuracy and overhead.
Abstract: Cross-Technology Interference affects the operation of low-power ZigBee networks, especially under severe WiFi interference. Accurate corruption estimation is very important to improve the resilience of ZigBee transmissions. However, there are many limitations in existing approaches such as low accuracy, high overhead, and requiring hardware modification. In this paper, we propose an accurate corruption estimation approach, AccuEst, which utilizes per-byte SINR (Signal-to-Interferenceand-Noise Ratio) to detect corruption. We combine the use of pilot symbols with per-byte SINR to improve corruption detection accuracy, especially in highly noisy environments (i.e., noise and interference are at the same level). In addition, we design an adaptive pilot instrumentation scheme to strike a good balance between accuracy and overhead. We implement AccuEst on the TinyOS 2.1.1/TelosB platform and evaluate its performance through extensive experiments. Results show that AccuEst improves corruption detection accuracy by 78.6% on average compared with state-of-the-art approach (i.e., CARE) in highly noisy environments. In addition, AccuEst reduces pilot overhead by 53.7% on average compared to the traditional pilot-based approach. We implement AccuEst in a coding-based transmission protocol, and results show that with AccuEst, the packet delivery ratio is improved by 20.3% on average.
13 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...Different from SoNIC, TIIM [19] skips the classification step and directly builds a decision tree classifier to learn under which interference patterns a particular mitigation scheme empirically achieves the best performance....
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..., 5300 NIC[40]), our approach can be easily applied....
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...We believe that for any platform that can enable high-resolution RSSI sampling (e.g. Micaz[39]) or provide fine-grained Channel State Information (CSI) of the channel where the bits are transmitted on (e.g., 5300 NIC[40]), our approach can be easily applied....
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...SoNIC [20] utilizes the key insight that different interferers disrupt individual 802.15.4 packets in different ways that can be detected by sensor nodes....
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TL;DR: In this paper , the authors investigated the dependence of velocity estimation accuracy on target locations and headings in WiFi sensing systems and derived a closed-form solution to optimize velocity estimation performance.
Abstract: Enabling pervasive WiFi devices with non-contact sensing capability is an important topic in the field of integrated sensing and communication. Doppler effect has been widely exploited to estimate targets’ velocity from wireless signals. However, the separation of signal sources and receivers complicates the relationship between Doppler frequency shift (DFS) and target velocity in WiFi-based non-contact sensing systems. In contrast to existing works that rely on either approximated relations or coarse-grained information such as whether a target is moving toward or away from WiFi transceivers, this paper investigates rigorously the dependency of velocity estimation accuracy on target locations and headings in WiFi sensing systems. The theoretical insights allow us to derive a closed-form solution and understand the fundamental limitation of velocity estimation. To optimize velocity estimation performance, we devise a receiving device selection scheme that dynamically chooses the optimal set of receivers among multiple available WiFi devices. A prototype real-time target tracking system has been implemented using commodity WiFi devices. Extensive experimental results show that the proposed system outperforms state-of-the-art approaches in velocity estimation and tracking, and is able to achieve $9.38cm/s$ , 13.42°, $31.08cm$ median errors in speed, heading and location estimation amongst experiments conducted in three indoor environments with three device placements and eight human subjects over 15 trajectories.
13 citations
13 Sep 2020
TL;DR: To deal with various walking trajectories and speeds, GaitID first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals.
Abstract: Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectory and sufficient training data. In this paper, we present GaitID, a Wi-Fi based human identification system, to overcome above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitID first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitID reduces the training efforts in new deployment scenarios by transfer learning. Extensive experiments have been conducted on the implementation and the outcomes are satisfying. To the best of our knowledge, GaitID is the first gait-based identification approach without any restriction on walking trajectory and speed.
13 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...GaitID is implemented on one Wi-Fi sender and six Wi-Fi receivers, each of which is equipped with Intel 5300 wireless NIC and Linux CSI Tool [1]....
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04 Nov 2020
TL;DR: The Polite WiFi behavior is discovered for the first time and it is found that all existing WiFi devices respond to fake packets transmitted to them, which creates many threats as well as opportunities.
Abstract: WiFi networks employ authentication and encryption mechanisms to protect the network from being accessed by unauthorized devices. Therefore, WiFi communication should be possible only between devices inside the same network. However, we have found that all existing WiFi devices send back acknowledgments (ACK) to even fake packets received from WiFi devices outside of their network. We call this behavior Polite WiFi since WiFi devices respond to all packets even those coming from strangers! In this paper, we discover the Polite WiFi behavior for the first time. We also examine this behavior on over 5,000 WiFi devices from 186 vendors. We find that all existing WiFi devices respond to fake packets transmitted to them. We believe this behavior creates many threats as well as opportunities. For example, one can couple this behavior with WiFi sensing and localization techniques to create a new class of security threats. In particular, by continuously sending fake frames to a target device, and measuring the properties of the ACK signal, one can extract sensitive personal information. Similarly, an attacker may use this behavior to quickly drain the battery of WiFi devices by continuously sending fake packets to them. Despite these threats, we also believe that the Polite WiFi behavior can open up new opportunities to WiFi sensing applications by making them more practical and easier to deploy.
13 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...3We use ESP32 instead of the more commonly used CSI tool [14] and Intel 5300 WiFi card because it enables us to measure the CSI for legacy 802....
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Posted Content•
TL;DR: A semi-supervised, generative adversarial network (GAN)-based device-free fingerprinting indoor localization system that uses a small amount of labeled data and a large amount of unlabeled data, thus considerably reducing the expensive data labeling effort.
Abstract: Device-free wireless indoor localization is a key enabling technology for the Internet of Things (IoT). Fingerprint-based indoor localization techniques are a commonly used solution. This paper proposes a semi-supervised, generative adversarial network (GAN)-based device-free fingerprinting indoor localization system. The proposed system uses a small amount of labeled data and a large amount of unlabeled data (i.e., semi-supervised), thus considerably reducing the expensive data labeling effort. Experimental results show that, as compared to the state-of-the-art supervised scheme, the proposed semi-supervised system achieves comparable performance with equal, sufficient amount of labeled data, and significantly superior performance with equal, highly limited amount of labeled data. Besides, the proposed semi-supervised system retains its performance over a broad range of the amount of labeled data. The interactions between the generator, discriminator, and classifier models of the proposed GAN-based system are visually examined and discussed. A mathematical description of the proposed system is also presented.
13 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...CSI samples [17] were collected at the fixed-location receiver (using the tool [18]) when a subject person stood at each location without any tracking device attached....
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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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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...
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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