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8 results for “Wi-Fi fingerprinting”
Additional TAU datasets for Wi-Fi fingerprinting-based positioning
<p><strong>1. Contents</strong></p> <p>This document describes two datasets collected at Tampere University facilities with samples taken from a Wi-Fi network interface for experiments with indoor positioning based on Wi-Fi fingerprinting.</p> <p>To reference this dataset, please use</p> <p>E.S. Lohan et al. “Additional TAU datasets for Wi-Fi fingerprinting-based positioning” 10.5281/zenodo.3819917</p> <p>Additional reference using these datasets</p> <p><em>Torres-Sospedra, J.; Quezada-Gaibor, D.; Mendoza-Silva, G. M.; Nurmi, J.; Koucheryavy, Y. and Huerta, J. New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting Proceedings of the Tenth International Conference on Localization and GNSS (ICL-GNSS), 2020.</em></p> <p><strong>Dataset format</strong></p> <p>Two independent datasets are provided, they are in different folders, namely “Database_Building01” and “Database_Building02” respectively. Each dataset includes two sets of samples:</p> <ul> <li>radio map – a set of Wi-Fi samples collected at a grid of points (reference points);</li> <li>evaluation – a set of Wi-Fi samples randomly collected in the evaluation area.</li> </ul> <p>Two files are provided for each set that include the rss vectors and the coordinates. For the radio map, the provided files have their names starting with “rm_”; for the evaluation, the evaluation files have their names starting with “eval_”. For instance, for the radio map they are:</p> <ul> <li>rm_crd.csv: holds coordinates (x,y)and floor identifier (z) where the samples were collected;</li> <li>rm_rss.csv: holds the measured RSSI values from each of the Access Points (AP) detected in each sample;</li> </ul> <p>All the file are described in the same format, and all files are CSV – Comma Separated Values plain text (UTF-8).</p> <p><strong>Coordinates:</strong> Each sample is associated to a pair of coordinates in a 2D Euclidean reference system. The origin of the reference system was chosen arbitrarily for convenience. The units are meters. Therefore, distances between points can be easy calculated. Moreover, the floor identifier is included to enable 3D positioning.</p> <p><strong>RSSI values:</strong> The RSSI values provided as read from the Wi-Fi network interface through the Android API. In each sample, a value of +100 was assigned to each AP not detected during a measurement. No information is provided about the MAC addresses of the APs. However, in the files, the same order is used for all samples, meaning that the values in each column are all associated to the same AP.</p> <p>Both datasets are independent and none of the provided files include an identifier for each sample. The values in the two provided files are associated by the line number, meaning that the coordinates and RSSI values in the same line, in each file, refer to the same sample.</p>
Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version)
<p>Database with Wi-Fi samples (RSSI measurements) collected from several Raspberry Pi (RPi) 3B+ devices continuously over 2+ years. The database includes the long-term dataset from the RPi devices (with 7,435,398 Wi-Fi samples), as well as 12 site-survey datasets (with 11,140 Wi-Fi samples) conducted in this period. The site-surveys were also conducted with a RPi 3B+.</p> <p>The measurements obtained from the RPi 3B+ Wi-Fi interface include the list of detected APs, their signal strength (RSSI) and transmission channel. The list has APs from the 2.4GHz and 5GHz bands because it supports IEEE 802.11.b/g/n/ac wireless LAN. </p> <p>These data were collected at a university building, between 19 Feb. 2019 and 25 Mar. 2021.</p> <p>The supporting material includes the Python scripts to parse and analyse the data by generating various plots. It also includes the locations of the monitoring devices and the list of reference points considered in the site-surveys.</p> <p> </p> <p>A detailed description of this dataset and the data collection process can be found here:</p> <p>Silva I, Pendão C, Moreira A. Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems. <em>Sensors</em>. <strong>2022</strong>; 22(22):8585. <a href="https://doi.org/10.3390/s22228585">https://doi.org/10.3390/s22228585</a></p> <p> </p> <p>When using this dataset, please add a citation to the paper above or this citation:</p> <p>Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. <a href="Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6928554">https://doi.org/10.5281/zenodo.6928554</a> </p> <p> </p> <p>The following papers have used this dataset for quantifying radio map degradation and overcoming radio map degradation in Wi-Fi fingerprinting:</p> <ul> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting," <em>2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Lloret de Mar, Spain, 2021, pp. 1-8, doi: 10.1109/IPIN51156.2021.9662558.</li> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Overcoming Radio Map Degradation in Wi-Fi-based Positioning Systems," <em>2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Nuremberg, Germany, 2023, pp. 1-6, doi: 10.1109/IPIN57070.2023.10332545.</li> </ul>
Wi-Fi Fingerprinting dataset with multiple simultaneous interfaces
<p> This dataset was collected within the context of a research project aiming to develop an indoor positioning system for autonomous industrial vehicles. This positioning system is based on fusing data from Wi-Fi sensors, magnetic encoders, and an IMU, using a particle filter. Wi-Fi data is first processed using Wi-Fi fingerprinting.</p> <p> One of the unique characteristics of this system is that it uses Wi-Fi fingerprints collected simultaneously from multiple, synchronised, Wi-Fi interfaces. Some results about the benefits of using multiple Wi-Fi interfaces are described in:</p> <p> Moreira, A., Silva, I., Meneses, F., Nicolau, M. J., Pendao, C., & Torres-Sospedra, J. (2017, September). Multiple simultaneous Wi-Fi measurements in fingerprinting indoor positioning. In 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-8). IEEE. http://dx.doi.org/10.1109/IPIN.2017.8115914</p> <p> These data were collected at a university building that resembles an industrial floor plant, with a total area of around 1000 m2. The data were collected in July 2017.</p> <p> The data collection setup was based on a Raspberry Pi 3 Model B with its internal Wi-Fi interface, and four additional USB Wi-Fi interfaces (Edimax EW-7811un).</p> <p> </p>
Wi-Fi RSSI fingerprint dataset from two malls with validation routes in a shop-level for indoor positioning
<p><strong>Th</strong>e dataset is composed of the RSSI fingerprinting calibration of two malls employing four different smartphones with the random walking survey method. Moreover, the dataset includes validation routes performed with ten smartphones from various brands and labeled with the code of the shops where they were registered. For evaluation purposes, together with the samples, information related to the shopping center like the Euclidean distance between shops is provided. The files are described as follows:</p> <ul> <li> <p>building.csv’: two rows with the id of mall 1 and mall 2.</p> </li> <li> <p>‘floor.csv’: there is a row for each of the floors of the two malls. Three floors for mall 1 and two floors for mall 2 </p> </li> <li> <p>‘zones.csv’: for each zone is indicated the id of the mall and the id floor. There are 69 zones from mall 1 and 124 for mall 2 </p> </li> <li> <p>‘training.csv’: in this file are registered the ids and the description of the five different calibrations performed (one per smartphone) in each of the malls. </p> </li> <li> <p>‘training_sample.csv’: each row of the file has the RSSI in dBm recorded during the off-line calibration phase together with the timestamp, channel, and MAC of the AP.</p> </li> <li> <p>‘route.csv’: the id’s and the description of each of the validation routes are recorded in this file. </p> </li> <li> <p>‘route_sample.csv’: with a similar structure than the training_sample.csv, the RSSI, timestamp, channel, and MAC acquired during the validation routes are stored in this file.</p> </li> <li> <p>‘euclidea_distance.csv”: to evaluate the error of the different algorithms, this file recorded the euclidean distance from the center of a shop to each other in meters.</p> </li> </ul> <p>The dataset is to be cited as follows:<br> J. A. López-Pastor, A.J. Ruiz-Ruiz, A.J. García-Sánchez, J.L. Gómez-Tornero. Wi-Fi RSSI fingerprint dataset from two malls with validation routes in a shop-level for indoor positioning. Zenodo repository. DOI: 10.5281/zenodo.3698238</p>
Long-Term Wi-Fi fingerprinting dataset and supporting material
<p>WiFi measurements database for UJI's library and supporting material.</p> <p>The measurements were collected by one person using mainly one Android smartphone during 25 months at two floor of the library building from Universitat Jaume I, in Spain. It contains 103,584 WiFi fingerprints, which are organized into datasets. Each dataset is the result of a collection campaign.</p> <p>The supporting material includes Matlab® scripts to load and filter the desired data, and provides examples on possible studies that the database may enable. The supporting material also includes the bookshelves local coordinates.</p> <p>Citation request:</p> <p>Mendoza-Silva, G.M.; Richter, P.; Torres-Sospedra, J.; Lohan, E.S.; Huerta, J. Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning. <em>Data</em> <strong>2018</strong>, <em>3</em>, 3.</p> <p>G.M. Mendoza-Silva, P. Richter, J. Torres-Sospedra, E.S. Lohan, J. Huerta, "Long-Term Wi-Fi fingerprinting dataset and supporting material", Zenodo repository, DOI 10.5281/zenodo.1066041.</p> <p> </p>
The DSI dataset for Wi-Fi fingerprinting using mobile devices
<p>The provided data were collected at the first floor of building 11 of the University of Minho, Portugal, Azurém Campus (Department of Information Systems – DSI), between 2 and 3 of May 2016.</p> <p>Data was collected by a single person, using a Nvidia Shield tablet running Android 6.0.</p> <p> </p>
Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts
<p>A detailed description of our dataset can be found here: <br> Nor Hisham, A.N.; Ng, Y.H.; Tan, C.K.; Chieng David, Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts. Data 2022, to appear.</p> <p>Please cite the paper when using the dataset.</p>
Datasets for Indoor Positioning with Single-AP Wi-Fi Fingerprinting
<p>Datasets for Indoor Positioning with Single-AP Wi-Fi Fingerprinting.</p> <p> </p>
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