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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TL;DR: A novel Sybil attack detection based on Channel State Information (CSI) that can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI).
Abstract: With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks.
28 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...We installed CSI tools [46] on these mini-PCs, which can receive the CSI of 30 subcarriers....
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TL;DR: This paper presents ESP32-based Wi-ESP as a CSI gathering tool that can report detailed CSI measurements based on 802.11n standards and proposes it as a tool for DFWS applications.
28 citations
TL;DR: A Wi-Fi-based OAR system called Wi-OAR that enables energy-efficient and user-centric services in smart offices and innovatively presents the fast and robust target component separation (FRTCS) algorithm regarding both time efficiency and high accuracy is proposed.
28 citations
TL;DR: This work presents Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI).
Abstract: A human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.
28 citations
TL;DR: This letter presents design and implementation of a system solution, where light weight wireless devices are used to identify a moving object within underground pipeline for maintenance and inspection, and the experimental results demonstrate greater level of accuracy.
Abstract: This letter presents design and implementation of a system solution, where light weight wireless devices are used to identify a moving object within underground pipeline for maintenance and inspection. The devices such as transceiver operating at S -band are deployed for underground settings. Finer-grained channel information in conjunction with leaky-wave cable (LWC) detects any moving entity. The processing of the measured data over time is analyzed and used for reporting the disturbances. Deploying an LWC as the receiver has benefits in terms of a wider coverage area, covering blind and semiblind zones. The system fully exploits the variances of both amplitude and phase information of channel information as the performance indicators for motion detection. The experimental results demonstrate greater level of accuracy.
28 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...A modified driver is used to collect client side data [16]....
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697 citations
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...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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