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435 results for “RSS”
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>
A Dataset of Outdoor RSS Measurements for Localization
<p><strong>Update: </strong>New version includes additional samples taken in November 2022.</p> <p><strong>Dataset Description</strong></p> <p>This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters.</p> <p>The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus.</p> <table> <tbody> <tr> <th>Dataset Description</th> <th>Sample Count</th> <th>Receiver Count</th> </tr> </tbody> <tbody> <tr> <td>No-Tx Samples</td> <td>46</td> <td>10 to 25</td> </tr> <tr> <td>1-Tx Samples</td> <td>4822</td> <td>10 to 25</td> </tr> <tr> <td>2-Tx Samples</td> <td>346</td> <td>11 to 12</td> </tr> </tbody> </table> <p>The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows:</p> <p>\(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \)</p> <table> <tbody> <tr> <th>Measurement Parameters</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>Frequency</td> <td>462.7 MHz</td> </tr> <tr> <td>Radio Gain</td> <td>35 dB</td> </tr> <tr> <td>Receiver Sample Rate</td> <td>2 MHz</td> </tr> <tr> <td>Sample Length</td> <td>N=10,000</td> </tr> <tr> <td>Band-pass Filter</td> <td>6 kHz</td> </tr> <tr> <td>Transmitters</td> <td>0 to 2</td> </tr> <tr> <td>Transmission Power</td> <td>1 W</td> </tr> </tbody> </table> <p>Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device.</p> <p><strong>Usage Instructions</strong></p> <p>Data is provided in <code>.json</code> format, both as one file and as split files.</p> <pre><code>import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f) </code></pre> <p>The <code>json</code> data is a dictionary with the sample timestamp as a key. Within each sample are the following keys:</p> <ul> <li><code>rx_data</code>: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name.</li> <li><code>tx_coords</code>: A list of coordinates for each transmitter. Each entry contains latitude and longitude.</li> <li><code>metadata</code>: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in <code>tx_coords</code></li> </ul> <p><strong>File Separations and Train/Test Splits</strong></p> <p>In the <code>separated_data.zip</code> folder there are several train/test separations of the data.</p> <ul> <li><code>all_data</code> contains all the data in the main JSON file, separated by the number of transmitters.</li> <li><code>stationary</code> consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers.</li> <li><code>train_test_splits</code> contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with <code>unused.json</code>, is equivalent to the file <code>all_data/single_tx.json</code>. <ul> <li>The <code>random</code> split is a random 80/20 split of the data.</li> <li><code>special_test_cases</code> contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus.</li> <li>The <code>grid</code> split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the <code>grid</code> split.</li> <li>The <code>seasonal</code> split contains data separated by the month of collection, in April, July, or November</li> <li>The <code>transportation</code> split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The <code>non-driving.json</code> file contains the union of the walking and cycling data.</li> <li><code>campus.json</code> contains the on-campus data, so is equivalent to the union of each split, not including <code>unused.json</code>.</li> </ul> </li> </ul> <p><strong>Digital Surface Model</strong></p> <p>The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR <a href="https://doi.org/10.5069/G9TH8JNQ">survey</a>. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models.</p> <p>To read the data in python:</p> <pre><code>import rasterio as rio import numpy as np import utm dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T') </code></pre> <p><strong>Dataset Acknowledgement:</strong> This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product.</p> <p><strong>DSM DOI:</strong> <a href="https://doi.org/10.5069/G9TH8JNQ">https://doi.org/10.5069/G9TH8JNQ</a></p>
WLAN (WiFi) RSS database for fingerprinting positioning
<p>This data set contains two WLAN Received Signal Strengths (RSS) databases suitable for fingerprinting positioning. One database contains training data (Training_rss.csv, Training_coordinates.csv), the radio map, the second database contains test data (Test_rss.csv, Test_coordinates.csv), RSS measurements on a path and the coordinates of that path. The data was collected in a three-floor building at Tampere University of Technology.</p> <p>The files Training_rss.csv and Test_rss.csv represent a matrix, with a reference point per row and an access point per column. The radio map consists of 446 reference points and 489 access points. Empty RSS values are set to 100. The files Training_coordinates.csv and Test_coordinates.csv contain the reference positions, 3D coordinates in a metric local reference frame. The data format allows to use previously published software (https://doi.org/10.5281/zenodo.889797) to analyze the data.<br> <br> The data is postprocessed: The reference positions of each floor are mapped onto a regular grid with 5 meter grid point spacing and the RSS values at each reference position are spatial averages of the RSS values in the resulting cells. The test data is mapped as well, but to a grid of 1 meter grid point spacing, from which only every third value was selected.</p> <p> </p>
BLE RSS dataset for fingerprinting radio map calibration
<p>The dataset contains Bluetooth Low Energy signal strengths measured in a fully furnished flat. The dataset was originally used in the study concerning RSS-fingerprinting based indoor positioning systems. The data were gathered using a hybrid BLE-UWB localization system, which was installed in the apartment and a mobile robotic platform equipped for a LiDAR. The dataset comprises power measurement results and LiDAR scans performed in 4104 points. The scans used for initial environment mapping and power levels registered in two test scenarios are also attached.</p> <p>The set contains both raw and preprocessed measurement data. The Python code for raw data loading is supplied.</p> <p>The detailed dataset description can be found in the <em>dataset_description.pdf</em> file.</p> <p>When using the dataset, please consider citing the original paper, in which the data were used:</p> <p>M. Kolakowski,<strong> “Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning”</strong>, <em>Sensors</em> , vol. <em>21</em>, 6270, Sep. 2021 <a href="https://doi.org/10.3390/s21186270">https://doi.org/10.3390/s21186270</a></p> <p> </p>
Received Signal Srength (RSS) urban measurements from GSM and UMTS networks for cellular-based positioning
<p>GSM (2G) and UMTS (3G) urban measurement data for cellular-based positioning. The data has been collected in Tampere city, Finland with a mobile phone with proprietary software. The data is given in Matlab *mat format and it contains a cell variable called BS_grid* which shows the GPS coordinates (x,y,z) (converted in local coordinates, in meter values) and the collected RSS value (in dB) per transmitter (i.e., BS or Node B). Each cell contains a N x 4 matrix, whose rows are [x y z RSS]. N is the number of measurements points in which the corresponding Base Station were heard. The size of the BS_grid* cells is equal to the number of heard Base Stations in the measured area. </p> <p>Example of research results based on these measurements can be found for example in:</p> <ul> <li>H. Nurminen, J. Talvitie, S. Ali-Loytty, P. Muller, E.S. Lohan, R. Piche, M. Renfors, "Statistical path loss parameter estimation and positioning using RSS measurements", Journal of Global Positioning Systems, vol. 12(1), 2013, ISSN 1446-3156.</li> </ul>
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot.
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot.
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot.
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate.
BLE RSS measurements database and supporting materials
<p>BLE RSS measurements database and supporting materials</p> <p>The database contains over 4,700 fingerprints of measurement from Bluettoth Low Energy (BLE) beacons. The beacons were Accent System's IBKS105, and the RSS were measured in two zone from the Universitat Jaume I, in Spain. One zone is an area among bookshelves that belong to the university library, for which beacons were configured to advertise at the -12 dBm power and the collection was performed using three smartphones: aBQ Aquaris X5 plus, a Samsung Galaxy S6 (SM-G920F) and a Samsung Galaxy A5 2017 (SM-A520F). The other zone is an office space area, for which beacons were configured at -4dBm, -12 dBm, and -20dBm and the collection was performed using the only Samsung Galaxy A5 smartphone. The supporting material includes Matlab® scripts to load and filter the desired data, and provides examples that depict common challenges for BLE RSS-based indoor positioning. The supporting material also includes the beacon deployment and the obstacles local coordinates.</p> <p>Citation request:</p> <p>Mendoza-Silva, G.M.; Matey-Sanz, M.; Torres-Sospedra, J.; Huerta, J. BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning. Data 2019, 4, 12</p> <p>Mendoza-Silva, G.M.; Matey-Sanz, M.; Torres-Sospedra, J.; Huerta, J. "BLE RSS meaurements database and supporting materials". Zenodo repository, DOI 10.5281/zenodo.1618692</p> <p> </p>
A Bluetooth 5.1 Dataset Based on Angle of Arrival and RSS for Indoor Localization
<p><strong>Overview</strong>:<br> The dataset contains measurements of Angle of Arrival (AoA) and Received Signal Strength (RSS)collected from Bluetooth 5.1 tags and a set of 4 anchor nodes deployed in an indoor environment. The data collection campaign has been conducted in a wide-open room of 110 square meters located in a wide open room.<br> The dataset includes four application scenarios covering typical use-cases for indoor localization:</p> <ol> <li>calibration: 4 anchors and 1 tag mounted on a tripod and positioned in 119 different locations;</li> <li>static: 4 anchors and 1 tag held by a person resting in 36 different locations. The tag is locked on a lanyard around the person's neck, we collect data with the person oriented toward North, South, East and West;</li> <li>mobility: 4 anchors and a person holding the tag around the neck a moving along 3 different paths;</li> <li>proximity scenario: 4 anchors and tags held by groups of people. We reproduce proximity with dyads, triplets and of groups of 4 people approach and distancing along the time.</li> </ol> <p>All the scenarios include an accurate Ground Truth (GT) annotation. The dataset is organized in four folders one for each scenario: Calibration, Static, Mobility and Proximity.</p> <p>The dataset enables the study of how AoA varies under stationary or mobility conditions, facilitating the analysis of an anchor's AoA model. In addition, the data collected can be analyzed to create a simulator of AoA values, which is useful for rapid prototyping and evaluation of indoor localization algorithms.</p> <p><strong>How to use the dataset</strong>:<br> Please, read the README.txt file detailing the dataset's format and the data collection camping. In summary, collected data include:<br> - Timestamp<br> - Tag identifier<br> - RSS on the 1<sup>st</sup> antenna's polarization<br> - AoA azimuth<br> - AoA elevation<br> - RSS on the 2<sup>nd</sup> antenna's polarization<br> - Bluetooth channel<br> - Anchor identifier</p> <p><strong>How to cite this dataset</strong>:</p> <p>- DOI number of this datsaset : 10.5281/zenodo.7759557</p> <p>- M. Girolami, F. Furfari, P. Barsocchi and F. Mavilia, "A Bluetooth 5.1 Dataset Based on Angle of Arrival and RSS for Indoor Localization," in <em>IEEE Access</em>, vol. 11, pp. 81763-81776, 2023, doi: 10.1109/ACCESS.2023.3301126.</p>
WiFi RTT RSS dataset for indoor positioning
<p>This is the first batch of WiFi RSS RTT datasets with LOS conditions we published. Please see <code>https://doi.org/10.5281/zenodo.11558792</code> for the second batch.</p> <h2><strong>Please do use version 2 for better quality.</strong></h2> <p>We provide publicly available datasets of three different indoor scenarios: building floor, office and apartment. The datasets contain both <strong>WiFi RSS and RTT signal measures</strong> with <strong>groud truth coordinates</strong> label and <strong>LOS condition</strong> label.</p> <p>1.Building Floor</p> <p>This is a detailed WiFi RTT and RSS dataset of a whole floor of a university building, of moare than 92 x 15 square metres. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures recorded in 642 reference points for 3 days and is well separated so that training points and testing points will not overlap.</p> <p>2. Office</p> <p>Office scenario is of more than 4.5 x 5.5 square metres. 3 APs are set to cover the whole space. At least two LOS AP could be seen at any reference point (RP). </p> <p>3.Apartment</p> <p>Apartment scenario is of more than 7.7 x 9.4 square metres.Four APs were leveraged to generate WiFi signal measures for this testbed. Note that AP 1 in the apartment dataset was positioned so that it could had an NLOS path to most of the testbed. </p> <p> </p> <h2><strong>Collection methodology</strong></h2> <p>The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.</p> <p>The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers. </p> <p>Only RTT-enabled APs were included in the dataset.</p> <h2><strong>The features of the datasets</strong></h2> <p><strong>The features of the building floor dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 92 × 15 m2</p> <p>Grid size: 0.6 × 0.6 m2</p> <p>Number of AP: 13</p> <p>Number of reference points: 642</p> <p>Samples per reference point: 120</p> <p>Number of all data samples: 77040</p> <p>Number of training samples: 57960</p> <p>Number of testing samples: 19080</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Collection time interval: 3 days</p> </blockquote> <p><strong>The features of the office dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 4.5 × 5.5 m2</p> <p>Grid size: 0.455 × 0.455 m2</p> <p>Number of AP: 3</p> <p>Reference points: 37</p> <p>Samples per reference point: 120</p> <p>Data samples: 4,440</p> <p>Training samples: 3,240</p> <p>Testing samples: 1,200</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Other information: LOS condition of every AP</p> <p>Collection time: 1 day</p> <p>Notes: A LOS scenario</p> </blockquote> <p><strong>The features of the apartment dataset are as follows:</strong></p> <blockquote> <p>Testbed area: 7.7 × 9.4 m2</p> <p>Grid size: 0.48 × 0.48 m2</p> <p>Number of AP: 4</p> <p>Reference points: 110</p> <p>Samples per reference point: 120</p> <p>Data samples: 13,200</p> <p>Training samples: 9,720</p> <p>Testing samples: 3,480</p> <p>Signal measure: WiFi RTT, WiFi RSS</p> <p>Other information: LOS condition of every AP</p> <p>Collection time: 1 day</p> <p>Notes: Contains an AP with NLOS paths for most of the RPs</p> </blockquote> <h2><strong>Dataset explanation</strong></h2> <p>The columns of the dataset are as follows:</p> <p>Column 'X': the X coordinates of the sample.</p> <p>Column 'Y': the Y coordinates of the sample.</p> <p>Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP13 RTT(mm)': the RTT measure from corresponding AP at a reference point.</p> <p>Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP13 RSS(dBm)': the RSS measure from corresponding AP at a reference point.</p> <p>Column 'LOS APs': indicating which AP has a LOS to this reference point.</p> <p>Please note:</p> <ul> <li>The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.</li> <li>The RTT value 100,000 mm indicates that no signal is received from the specific AP.</li> </ul> <h2><strong>Citation request<br></strong></h2> <p>When using this dataset, please cite the following two items:<br><br><code>Feng, X., Nguyen, K. A., & Luo, Z. (2024). WiFi RTT RSS dataset for indoor positioning [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11558192" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11558192</a></code><br><br><code>@article{feng2023wifi, title={WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments}, author={Feng, Xu and Nguyen, Khuong an and Luo, Zhiyuan}, journal={Journal of Location Based Services}, volume={17}, number={4}, pages={307--339}, year={2023}, publisher={Taylor \& Francis} }</code></p>
RSS Feed Efficiency Study Dataset
<p>Work on improving overall RSS bandwidth, CPU and thus carbon efficiency,<br>especially between aggregators / catalogues (iTunes, Amazon Music) and<br>static backend servers.</p> <p>See: https://www.earth.org.uk/RSS-efficiency.html</p> <p>Note the data and code archive:<br> * Manifest dataCodeArchive.manifest.txt<br> * Archive dataCodeArchive.zip</p> <p>See the statsHouse sonification project:<br> * https://github.com/DamonHD/statsHouse</p> <p>This data (and code) is made available under<br>a CC0 licence (Creative Commons Zero v1.0 Universal).</p>
WiFi RSS & RTT dataset with different LOS conditions for indoor positioning
<p>This is the second batch of WiFi RSS RTT datasets with LOS conditions we published. Please see <code><a href="https://doi.org/10.5281/zenodo.11558192" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11558192</a></code> for the first release.</p> <p> </p> <p>We provide three real-world datasets for indoor positioning model selection purpose. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures and is well separated so that training points and testing points will not overlap. Please find the datasets in the 'data' folder. The datasets contain both <strong>WiFi RSS and RTT signal measures</strong> with <strong>groud truth coordinates</strong> label and <strong>LOS condition label</strong>.</p> <ol> <li> <p>Lecture theatre: This is a entirely LOS scenario with 5 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP). </p> </li> <li> <p>Corridor: This is a entirely NLOS scenario with 4 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP). </p> </li> <li> <p>Office: This is a mixed LOS-NLOS scenario with 5 APs. At least one AP was NLOS for each RP. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP). </p> </li> </ol> <div> <h2><strong>Collection methodology</strong></h2> <p>The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.</p> <p>The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers. </p> <p>Only RTT-enabled APs were included in the dataset.</p> <h2><strong>The features of the dataset</strong></h2> </div> <p>The features of the lecture theatre dataset are as follows:</p> <div> <blockquote> <pre>Testbed area: 15 × 14.5 m2 Grid size: 0.6 × 0.6 m2<br>Number of AP: 5 Number of reference points: 120 Samples per reference point: 60 Number of all data samples: 7,200 Number of training samples: 5,400 Number of testing samples: 1,800 Signal measure: WiFi RTT, WiFi RSS Note: Entirely LOS </pre> </blockquote> <div> </div> </div> <p>The features of the corricor dataset are as follows:</p> <div> <blockquote> <pre>Testbed area: 35 × 6 m2 Grid size: 0.6 × 0.6 m2<br>Number of AP: 4 Number of reference points: 114 Samples per reference point: 60 Number of all data samples: 6,840 Number of training samples: 5,130 Number of testing samples: 1,710 Signal measure: WiFi RTT, WiFi RSS Note: Miexed LOS-NLOS. At least one AP was NLOS for each RP. </pre> </blockquote> <div> </div> </div> <p>The features of the office dataset are as follows:</p> <div> <blockquote> <pre>Testbed area: 18 × 5.5 m2 Grid size: 0.6 × 0.6 m2<br>Number of AP: 5 Number of reference points: 108 Samples per reference point: 60 Number of all data samples: 6,480 Number of training samples: 4,860 Number of testing samples: 1,620 Signal measure: WiFi RTT, WiFi RSS Note: Entirely NLOS </pre> </blockquote> <div> </div> </div> <div> <h2><strong>Dataset explanation</strong></h2> </div> <p>The columns of the dataset are as follows:</p> <div> <pre>Column 'X': the X coordinates of the sample. Column 'Y': the Y coordinates of the sample. Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP5 RTT(mm)': the RTT measure from corresponding AP at a reference point. Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP5 RSS(dBm)': the RSS measure from corresponding AP at a reference point. Column 'LOS APs': indicating which AP has a LOS to this reference point. </pre> </div> <p>Please note:</p> <ul> <li> <p>The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.</p> </li> <li> <p>The RTT value 100,000 mm indicates that no signal is received from the specific AP.</p> </li> </ul> <p> </p> <h2><strong>Citation request</strong></h2> <p>When using this dataset, please cite the following three items:</p> <p><code>Feng, X., Nguyen, K. A., & Zhiyuan, L. (2024). WiFi RSS & RTT dataset with different LOS conditions for indoor positioning [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11558792</code></p> <p> </p> <pre><code>@article{feng2024wifi, title={A WiFi RSS-RTT indoor positioning system using dynamic model switching algorithm}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, journal={IEEE Journal of Indoor and Seamless Positioning and Navigation}, year={2024}, publisher={IEEE} }</code><br><br><code>@inproceedings{feng2023dynamic, title={A dynamic model switching algorithm for WiFi fingerprinting indoor positioning}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, booktitle={2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, pages={1--6}, year={2023}, organization={IEEE} }</code></pre> <p> </p>
RSS database
<ul> <li>The database consists of : <ul> <li>1 second and 3 second</li> <li>10 males and 10 females</li> <li>text-dependent singing and normal reading speech</li> </ul> </li> </ul>
WiFi RSS measurements in Tampere University multi-building campus, 2017
<p>This package contains two Wi-Fi RSSI Datasets, namely TIE1 and SAH1.</p> <ul> <li>TIE1 was collected in the Tietotalo building from Tampere University during the period of August-December 2017 and it is composed of four files: <ul> <li>TIE1_training_rss.csv - Radio map or reference dataset as a matrix with 10633 reference samples (rows) and 613 APs (columns) </li> <li>TIE1_training_coordinates.csv - Ground truth location of the 10633 reference samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> <li>TIE1_test_rss.csv - Operational fingerprints as a matrix with 50 evaluation samples (rows) and 613 APs (columns) </li> <li>TIE1_test_coordinates.csv - Ground truth location of the 50 evaluation samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> </ul> </li> <li>SAH1 was collected in the Sahkotalo building from Tampere University during the period of October-December 2017 and it is composed of four files: <ul> <li>SAH1_training_rss.csv - Radio map or reference dataset as a matrix with 9291 reference samples (rows) and 775 APs (columns) </li> <li>SAH1_training_coordinates.csv - Ground truth location of the 9291 reference samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> <li>SAH1_test_rss.csv - Operational fingerprints as a matrix with 156 evaluation samples (rows) and 775 APs (columns) </li> <li>SAH1_test_coordinates.csv - Ground truth location of the 156 evaluation samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> </ul> </li> </ul> <p>Additional considerations:</p> <ul> <li>All the RSSI values are stored are they were registered with a regular smartphone. In order to generate the fingerprint vectors, the non-detected APs where filled with the default bogus value +100dBm. i.e., the value +100dBm in the training and test rss files indicates that the AP was not detected. Generally, this default bogus value has been replaced with a low RSSI value in experiments, such as -100dBm, -110dBm, -150dBm or -200dBm.</li> <li>The coordinates X,Y,Z are expressed in meters in a local coordinate system. Additionally, the sequential floor identifier is also provided.</li> </ul> <p>Please cite the source at Zenodo when using any of these two datasets:</p> <blockquote> <p>Lohan, E.S; Torres-Sospedra, J.; Gonzalez, A.; "WiFi RSS measurements in Tampere University multi-building campus, 2017", Zenodo, 2021.<br> https://zenodo.org/record/5174851<br> https://www.doi.org/10.5281/zenodo.5174851</p> </blockquote>
Input data files for RSS-NET analysis of IBD GWAS summary statistics and NK cell regulatory network
<p>Details of these data files are provided in https://suwonglab.github.io/rss-net/ibd2015_nkcell.</p> <p>Contact:<code> xiangzhu[at]psu.edu </code></p>
Atmospheric Absorption Tables for AMSU-A Channels 4 through 9, RSS oxygen model
Open the record for dataset details and reuse information.
RSS Wiki Example 2 Data Files
<p>Data files associated with RSS Wiki Example 2: https://stephenslab.github.io/rss/example_2.html</p>
RSS Wiki Example 1 Data Files
<p>Data files associated with RSS Wiki Example 1: https://stephenslab.github.io/rss/example_1.html</p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.