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8 results for “Channel State Information”
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</p>
WiFiCam Dataset | Through-Wall Imaging based on WiFi Channel State Information
<p><strong>Through-Wall Imaging based on WiFi Channel State Information</strong></p> <p>This repository contains the <strong>WiFiCam dataset</strong> for through-wall imaging based on WiFi channel state information proposed in [1]. The corresponding source code repository is located at: <a href="https://github.com/StrohmayerJ/wificam">https://github.com/StrohmayerJ/wificam</a></p> <p>The demo video (demo.mp4) showcases the through-wall imaging capabilities of our approach. </p> <p> </p> <p><strong>Dataset Structure</strong></p> <p>/wificam</p> <p>├── j3</p> <p> └── 320 <-- 320x240 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 92108.png <-- 320x240 RGB image</p> <p> └── 92112.png</p> <p> └── ...</p> <p> └── 640 <-- 640x480 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 154.png <-- 640x480 RGB image</p> <p> └── 155.png</p> <p> └── ...</p> <p>├── statistics320.csv <-- per-channel means and standard deviations for 320x240 images</p> <p>├── statistics640.csv <-- per-channel means and standard deviations for 640x480 images</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] J. Strohmayer, R. Sterzinger, C. Stippel and M. Kampel, "Through-Wall Imaging Based On WiFi Channel State Information," <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 4000-4006, doi: 10.1109/ICIP51287.2024.10647775.</p> <p>BibTeX:</p> <pre>@INPROCEEDINGS{10647775, author={Strohmayer, Julian and Sterzinger, Rafael and Stippel, Christian and Kampel, Martin}, booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, title={Through-Wall Imaging Based On WiFi Channel State Information}, year={2024}, volume={}, number={}, pages={4000-4006}, doi={10.1109/ICIP51287.2024.10647775}}</pre>
Simulated Channel state information for In-factory Subnetworks
<p><span>In the simulated factory environment, a multitude of strategically deployed short-range cells forms the backbone of robotic systems, production modules, conveyors, and other industrial machinery. These cells, designated as In-factory subnetworks (InF-S), are comprised of an access point (AP) serving one or more devices within the subnetwork. </span></p>
Channel State Information (CSI) analysis for predictive maintenance using Convolutional Neural Network (CNN)
<p>Dataset manual:</p> <p>This dataset contains CSI amplitude values for rotating motors in an office environment. Details of the experiments may be found in the corresponding paper published in the DATA'19 workshop, SenSys (<a href="https://doi.org/10.1145/3359427.3361917">https://doi.org/10.1145/3359427.3361917</a>). </p> <p>Folder structure:<br> The folders for servo motor and stepper motor contains separate folders for network reconnection conditions (w_recc: with reconnections, wo_recc: without recconnections) and load conditions (w_load: with load and wo_load: without load). The data is stores as Matlab files with .mat extentions. </p> <p>File structure:<br> In each file name, the digits after the '_' at the end of the file name correspond to the speed of the motor. In case of stepper motor these numbers could be directly interpreted as rpm. Ex: table_inj_with_load_5_0.mat corresponds to stationary motor (0 rpm) and table_inj_with_load_5_250.mat corresponds to motor rotating with 250 rpm speed. In the case of servo motor these numbers should be mapped with the following table in order to get the speeds.</p> <p>0: 0 rpm<br> 50: 14.45 rpm<br> 100: 8.02 rpm<br> 150: 5.38 rpm<br> 200: 4.05 rpm<br> 250: 3.26 rpm<br> 300: 2.67 rpm<br> Ex: table_inj_with_load_50.mat corresponds to motor running with 14.45 rpm.</p> <p>Each file has 3 columns, each corresponding to CSI value, labels (speed/last digits in the file name) and the data sample number (not in sequence as a result of packer loss) respectively. CSI values are typically a matrix of size 3000*180 (3000 CSI samples for 3sec data @1kHz sampling rate and 180 channels for 6 antenna pairs @ 30 subcarrier data per antenna).</p>
Evaluation results from "Next2You: Robust Copresence Detection Based on Channel State Information"
<p>This deposit contains the evaluation results for the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. See the <a href="https://doi.org/10.5281/zenodo.5105815">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Raw data from "Next2You: Robust Copresence Detection Based on Channel State Information"
<p>This deposit contains the raw channel state information (CSI) data from the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. See the <a href="https://doi.org/10.5281/zenodo.5105815">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Index of Supplementary Files from "Next2You: Robust Copresence Detection Based on Channel State Information"
<p>This record serves as an index to the other dataset releases that are part of the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. </p> <p>We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist:</p> <ol> <li> <p><strong>Raw data</strong>: <a href="https://doi.org/10.5281/zenodo.5592335">This dataset</a> contains raw channel state information (CSI) data in terms of CSI magnitude and phase values. The data is collected in the following environments: <em>office</em>, <em>urban apartment</em>, <em>rural house</em>, <em>moving</em> and <em>parked</em> cars, as well as additional setups in these environments such as <em>heterogeneous</em> devices (i.e., Nexus 6P and Raspberry Pi), different <em>frame types</em> to extract CSI data from (i.e., beacon), and varying transmission <em>power</em> of devices sending frames from which CSI is extracted. The data collection in additional setups was performed in the office environment.</p> <p>These raw CSI data can be used to repeat our own experiment or to develop new context-based copresence detection schemes. To collect the raw CSI data, we used the following <a href="https://github.com/seemoo-lab/next2you/tree/main/Data-collection/Nexus%205%20and%206P">Android app</a> and <a href="https://github.com/seemoo-lab/next2you/tree/main/Data-collection/Raspberry%20Pi">scripts on the Raspberry Pi 3 Model B+</a> (both include <a href="https://github.com/seemoo-lab/nexmon_csi">Nexmon</a> patches to enable CSI extraction).</p> </li> <li> <p><strong>Results Data</strong>: <a href="https://doi.org/10.5281/zenodo.5592823">The results dataset</a> contains the evaluation results (e.g., computed error rates and AUCs for different cases, Right for the Right Reasons trained models, etc.). The codebase to generate these results can be found in the <a href="https://github.com/seemoo-lab/next2you/tree/main/Scheme">source code repository</a>.</p> </li> </ol>
Spatially-diverse High-dimensional Channel State Information (CSI) based Dataset for Human Activity Recognition (HAR)
<p><strong>Introduction</strong></p> <p>The data collection for this novel channel state information (CSI)-based human activity recognition (HAR) dataset was conducted in a rigorously controlled laboratory environment. The space, measuring 5 meters x 8 meters x 3 meters, served as a well-defined testing ground. Four strategically positioned ESP32 devices formed transceiver pairs in a diagonal network, functioning as both transmitters and receivers, which are separated by a distance of 1.5 meters. The transmitters, powered by external power banks for consistent operation, were mounted on tripods at a height of 1.5 meters on the north and east corners. Their corresponding receivers, connected to laptops via USB for real-time CSI data acquisition, were positioned on the south and west corners.</p> <p>This dataset captures a wider range of activities (including subtle movements) and accounts for variations in body type and environmental conditions. It achieves this by collecting CSI data in a controlled environment using multiple transmitter-receiver pairs positioned at different orientations. This setup captures CSI information across 166 subcarriers used in Wi-Fi Wi-Fi IEEE 802.11n on channel 11, providing a richer and more nuanced view of human movement compared to traditional datasets. This data is expected to lead to the development of more robust and generalizable HAR models with higher accuracy and real-world applicability.</p> <p><strong>Description of Dataset</strong></p> <p>The dataset is housed within a directory named "SHD-HAR-Dataset-main" in the repository. This directory is further divided into "raw" and "amplitude" subdirectories. Data in "raw" and "amplitude" directories is further categorized based on participant orientation relative to the transceivers ("front/side"). It's important to note that the samples are synchronized between the "front" and "side" folders. This is because both devices collected data simultaneously for a particular activity. To summarize, the file "activityX.csv" in the "front" folder was collected at the same time as the corresponding "activityX.csv" file in the "side" folder, where "X" represents a unique identifier for each sample. </p> <p>The "raw" directory stores the unprocessed CSI data for each activity sample. These samples are stored as individual CSV files ("activityX.csv") containing 300-450 packets of CSI data obtained within 5 seconds. Each packet encompasses 25 distinct data fields, resulting in a total of 300-450 x 25 data entries per activity stored within the ".csv" file. </p> <p>The "amplitude" directory contains the processed signal amplitude data for 166 subcarriers. This directory mirrors the structure of the "raw" directory, with subdirectories for "front" and "side" orientations and "activityX.csv" files for each sample. However, the data within these files is transformed into a 300-450 x 166 matrix, representing the extracted signal amplitudes from the original CSI data. This format significantly reduces dimensionality while preserving key information for HAR analysis.</p>
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