Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version)

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+. 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.   These data were collected at a university building, between 19 Feb. 2019 and 25 Mar. 2021. 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.   A detailed description of this dataset and the data collection process can be found here: Silva I, Pendão C, Moreira A. Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems. Sensors. 2022; 22(22):8585. <a href="https://doi.org/10.3390/s22228585">https://doi.org/10.3390/s22228585</a>   When using this dataset, please add a citation to the paper above or this citation: 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>    The following papers have used this dataset for quantifying radio map degradation and overcoming radio map degradation in Wi-Fi fingerprinting: <ul> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting," 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 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," 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nuremberg, Germany, 2023, pp. 1-6, doi: 10.1109/IPIN57070.2023.10332545.</li> </ul>

opencc-by-4.0May 2019View details →

Kinematically collected reference fingerprint map (RFM) with the high precision tracking system for feature-based indoor positioning

The offline referencing phase, one of the core phases of the fingerprinting-based indoor positioning system (FIPS), is the key stage for deploying the positioning system. The reference fingerprint map (RFM) is acquired for representing the relationship between location-relevant features and the corresponding locations and used for inferring the user’s location at the online stage. The kinematically collecting the RFM using the mobile device with the help of high precision tracking system is contributed to the community for benchmarking comparison of the indoor positioning performance.  The detailed description of the data is cooming soon.

opencc-by-4.0Aug 2024View details →

Evaluating Open Science Practices in Indoor Positioning and Indoor Navigation Research (Supplementary Material: Full Paper Listing and Analysis)

Supplementary material of the paper: Title: "Evaluating Open Science Practices in Indoor Positioning and Indoor Navigation Research" Subtitle: "A Survey of the IPIN's Reference Papers of 2022 and 2023 Editions" The paper is accepted to the "14th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2024, Hong Kong, October 14-17, 2024, IEEE, 2024. An Author's accepted version of the manuscript is available here: <a href="../records/13684170" target="_blank" rel="noopener">https://zenodo.org/records/13684170</a>  If you want to refer to this work, please cite this Zenodo entry as well as the published conference version.   --------------------------------------- This entry contains two files: <ul> <li>"Paper Characterization Spreadsheet.xlsx": The spreadsheet of the full analysis of this work, as described in the paper. It characterizes various features of the analyzed papers and forms the raw data on which the analyses of our work were based.</li> <li>"Main features of the manuscripts analysed in Zenodo Record #12088175.pdf": A document summarizing the main features of the IPIN's Reference Papers of the 2022 and 2023 Editions, that contain some form of open resources (Open Data, Code, or Material).</li> </ul>

opencc-by-4.0Oct 2022View details →

Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"

This dataset was created as suplementary material for research article: Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package). The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office. The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files: Data This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table: <table> <caption>Data Point Coordinates</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col">...</th> </tr> </thead> <tbody> <tr> <th scope="row">A1</th> <td>0.85</td> <td>0.1</td> <td>B1</td> <td>1.85</td> <td>0.1</td> <td>...</td> </tr> <tr> <th scope="row">A2</th> <td>0.85</td> <td>1.1</td> <td>B2</td> <td>1.85</td> <td>1.1</td> <td>...</td> </tr> <tr> <th scope="row">A3</th> <td>0.85</td> <td>2.1</td> <td>B3</td> <td>1.85</td> <td>2.1</td> <td>...</td> </tr> <tr> <th scope="row">...</th> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> <tr> <th scope="row">A11</th> <td>0.85</td> <td>10.1</td> <td>B11</td> <td>1.85</td> <td>10.1</td> <td>...</td> </tr> </tbody> </table> Data_Eval This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected. Processed_Data Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values. The CSV files are in a format: <blockquote> <code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code> </blockquote> Data_Scenarios This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table: <table> <caption>Scenario Descriptions</caption> <thead> <tr> <th scope="col"> Data Name </th> <th scope="col"> Scenario Description </th> </tr> </thead> <tbody> <tr> <td>GPR00</td> <td>Only measured data, 50 samples per reference position</td> </tr> <tr> <td>GPR01</td> <td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td> </tr> <tr> <td>GPR02</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR03</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR04</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR05</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR06</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR07</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR08</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR09</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR10</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR11</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR12</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR13</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> </tbody> </table> The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table: <table> <caption>File Suffix Descriptions</caption> <tbody> <tr> <td> Suffix </td> <td> Suffix Description </td> </tr> <tr> <td>_trncrd</td> <td>Training Labels</td> </tr> <tr> <td>_trnrss</td> <td>Training RSSI Values</td> </tr> <tr> <td>_tstcrd</td> <td>Evaluation Labels</td> </tr> <tr> <td>_tstrss</td> <td>Evaluation RSSI Values</td> </tr> </tbody> </table> These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc. The RSSI data are in format: <blockquote> RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5 </blockquote> The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0) <blockquote> X, Y, 0, 0, 0 </blockquote>

opencc-by-4.0Jun 2024View details →

WiFi RTT RSS dataset for indoor positioning

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. <h2>Please do use version 2 for better quality.</h2> We provide publicly available datasets of three different indoor scenarios: building floor, office and apartment. The datasets contain both WiFi RSS and RTT signal measures with groud truth coordinates label and LOS condition label. 1.Building Floor 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. 2. Office 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).  3.Apartment 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.    <h2>Collection methodology</h2> The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9. The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers.  Only RTT-enabled APs were included in the dataset. <h2>The features of the datasets</h2> The features of the building floor dataset are as follows: <blockquote> Testbed area:  92 × 15 m2 Grid size: 0.6 × 0.6 m2 Number of AP: 13 Number of reference points: 642 Samples per reference point: 120 Number of all data samples: 77040 Number of training samples: 57960 Number of testing samples: 19080 Signal measure: WiFi RTT, WiFi RSS Collection time interval: 3 days </blockquote> The features of the office dataset are as follows: <blockquote> Testbed area:  4.5 × 5.5 m2 Grid size: 0.455 × 0.455 m2 Number of AP: 3 Reference points: 37 Samples per reference point: 120 Data samples: 4,440 Training samples: 3,240 Testing samples: 1,200 Signal measure: WiFi RTT, WiFi RSS Other information: LOS condition of every AP Collection time: 1 day Notes: A LOS scenario </blockquote> The features of the apartment dataset are as follows: <blockquote> Testbed area:  7.7 × 9.4 m2 Grid size: 0.48 × 0.48 m2 Number of AP: 4 Reference points: 110 Samples per reference point: 120 Data samples: 13,200 Training samples: 9,720 Testing samples: 3,480 Signal measure: WiFi RTT, WiFi RSS Other information: LOS condition of every AP Collection time: 1 day Notes: Contains an AP with NLOS paths for most of the RPs </blockquote> <h2>Dataset explanation</h2> The columns of the dataset are as follows: Column 'X': the X coordinates of the sample. Column 'Y': the Y coordinates of the sample. Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP13 RTT(mm)': the RTT measure from corresponding AP at a reference point. Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP13 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. Please note: <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>Citation request </h2> When using this dataset, please cite the following two items: <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> <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>

opencc-by-4.0Jul 2021View details →

Datasets of Indoor UWB Measurements for Ranging and Positioning in Good and Challenging Scenarios

This is a dataset of ranging and positioning measurements collected from an UWB development board. The Real Time Location System based on UWB is set up in a laboratory. Data were captured in the static laboratory environment with different conditions that affects to the positioning performance. In the lab, scenarios with different propagation conditions between the nodes and different geometries were set up. We consider good, challenging, and intermediate scenarios with: Line of Sight (LOS) and Non-LOS propagation conditions as well as easy and challenging geometries. These datasets may be used, for example, for investing and validating ranging and positioning algorithms in different scenarios. A detailed description is provided in the file README.pdf

opencc-by-4.0May 2022View details →

Indoor Positioning Simulation For Examination And Correction Of Occupancy Limits In Architectural Design

Dataset contains results of the simulation, statistical analysis and images. "Read me" file contains explanations on the content.

openmit-licenseSep 2017View details →

Crowdsourced WiFi database and benchmark software for indoor positioning

This dataset contains two Wi-Fi databases (one for training and one for test/estimation purposes in indoor positioning applications), collected in a crowdsourced mode (i.e., via 21 different devices and different users), together with a benchmarking utility software (in Matlab and Python) to illustrate various algorithms of indoor positioning based solely on WiFi information (MAC addresses and RSS values).  The data was collected in a 4-floor university building in Tampere, Finland,  during Jan-Aug 2017 and it comprises 687 training fingerprints and 3951 test or estimation fingerprints. 13.10.2017: Version 2 uploaded; the revised version contains improved readme files and improved Python SW. The dataset and/or the associated software are to be cited as follows: E.S. Lohan, J. Torres-Sospedra, P. Richter, H. Leppäkoski, J. Huerta, A. Cramariuc, “Crowdsourced WiFi-fingerprinting database and benchmark software for indoor positioning”, Zenodo repository, DOI 10.5281/zenodo.889798

opencc-by-4.0Jun 2024View details →

WiFi RSS & RTT dataset with different LOS conditions for indoor positioning

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.   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 WiFi RSS and RTT signal measures with groud truth coordinates label and LOS condition label. <ol> <li> 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).  </li> <li> 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).  </li> <li> 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).  </li> </ol> <div> <h2>Collection methodology</h2> The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9. The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers.   Only RTT-enabled APs were included in the dataset. <h2>The features of the dataset</h2> </div> The features of the lecture theatre dataset are as follows: <div> <blockquote> <pre>Testbed area: 15 × 14.5 m2 Grid size: 0.6 × 0.6 m2 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> The features of the corricor dataset are as follows: <div> <blockquote> <pre>Testbed area: 35 × 6 m2 Grid size: 0.6 × 0.6 m2 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> The features of the office dataset are as follows: <div> <blockquote> <pre>Testbed area: 18 × 5.5 m2 Grid size: 0.6 × 0.6 m2 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>Dataset explanation</h2> </div> The columns of the dataset are as follows: <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> Please note: <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>Citation request</h2> When using this dataset, please cite the following three items: <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>   <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> <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>

opencc-by-nc-sa-4.0Jul 2018View details →

Associated raw data to the PhD thesis: Design and evaluation of a camera-based indoor positioning system for forklift trucks

opencc-by-nc-sa-4.0Aug 2018View details →

Associated raw data to the publication: An accurate and efficient camera-based indoor positioning approach for intralogistic environments (MHCL 2015)

This is a test data set for marker-based augmented reality algorithms used to locate ground conveyors in an industrial environment. It was recorded in the testing area of the chair fml at TUM to develop and evaluate algorithms for locating forklift trucks in the publication "An accurate and efficient camera-based indoor positioning approach for intralogistic environments" at MHCL 2015 conference (see https://mediatum.ub.tum.de/1286589 and http://www.fml.mw.tum.de/fml/images/Publikationen/MHCL_2015_jung_submitted.pdf). Originally these files were recorded and used as uncompressed 8-bit grayscale bitmaps. The images were losslessly compressed to png files in order to reduce the test set file size (by approx. factor 3.5)

openOct 2023View details →

Dataset for Vehicle Indoor Positioning in Industrial Environments with Wi-Fi, inertial, and odometry data

Dataset collected in an indoor industrial environment using a mobile unit (manually pushed trolley) that resembles an industrial vehicle equipped with several sensors, namely, Wi-Fi, wheel encoder (displacement), and Inertial Measurement Unit (IMU). Sensors were connected to a Raspberry Pi (RPi 3B +), which collected the data from the sensors. Ground truth information was obtained with video camera pointed towards the floor, registering the times when the trolley passed by reference tags. List of sensors: <ul> <li>4x Wi-Fi interfaces: Edimax EW7811-Un</li> <li>2x IMUs: Adafruit BNO055</li> <li>1x Absolute Encoder: US Digital A2 (attached to a wheel with a diameter of 125 mm)</li> </ul> This dataset includes: <ul> <li>1x Wi-Fi radio map that can be used for Wi-Fi fingerprinting.</li> <li>6x Trajectories: including sensor data + ground truth.</li> <li>APs Information: list of APs in the building, including their position and transmission channel.</li> <li>Floor plan: image of the building's floor plan with obstacles and non-navigable areas.</li> <li>Python package provided for: <ul> <li>parsing the dataset into a data structure (Pandas dataframes).</li> <li>performing statistical analysis on the data (number of samples, time difference between consecutive samples, etc.).</li> <li>computing Dead Reckoning trajectory from a provided initial position.</li> <li>computing Wi-Fi fingerprinting position estimates.</li> <li>determining positioning error in Dead Reckoning and Wi-Fi fingerprinting.</li> <li>generating plots including the floor plan of the building, dead reckoning trajectories, and CDFs.</li> </ul> </li> </ul>   When using this dataset, please cite its data description paper: Silva , I.; Pendão, C.; Torres-Sospedra, J.; Moreira, A. Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data. Data 2023, 8, 157. <a href="https://doi.org/10.3390/data8100157" target="_blank" rel="noopener">https://doi.org/10.3390/data8100157</a>

opencc-by-4.0Mar 2020View details →

Wi-Fi RSSI fingerprint dataset from two malls with validation routes in a shop-level for indoor positioning

The 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: <ul> <li> building.csv’: two rows with the id of mall 1 and mall 2. </li> <li> ‘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  </li> <li> ‘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  </li> <li> ‘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.  </li> <li> ‘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. </li> <li> ‘route.csv’: the id’s and the description of each of the validation routes are recorded in this file.  </li> <li> ‘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. </li> <li> ‘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. </li> </ul> The dataset is to be cited as follows: 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

opencc-by-4.0Aug 2020View details →

Supplementary evaluation files for the paper: Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

This package contains evaluation supplementary files for the paper: Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones by Miroslav Opiela and Franti&scaron;ek Galčík. Contents:  <ul> <li>ground_truth - real positions of checkpoints for given input files</li> <li>input - sensor measurements recordings with initial positions (also after floor transitions) and checkpoint labels </li> <li>maps - processed map models containing positions of points and connections (e.g., walls) in custom coordinate system. Reference to GNSS and map rotation is inducted in maps-meta.xml</li> <li>output - data processed by the localization system. JSON containing the applied method, its configuration, and all estimated positions. Errors for every folder are summarized in the csv file</li> <li>visualization - trajectories visualized for selected output files</li> <li>readme.txt - describes data formats used for particular files in this dataset and summarizes output files</li> </ul> Venues Data are recorded in three buildings: <ul> <li>codename: SA1, SA1_rotated - recorded by the author in the faculty building (Park Angelinum 9, 04001, Ko&scaron;ice, Slovakia) using Lenovo tablet</li> <li>codename: AtlantisR0, AtlantisR-1, AtlantisR+1, AtlantisR+2 - the shopping mall Atlantis Le Centre (Boulevard Salvador Allende, 44800 Saint-Herblain, France). Dataset is from IPIN 2018 competition and loc_20180922_160206 is recorded by the author using Xiaomi Mi 5.</li> <li>codename: CNR_0, CNR_1, CNR_2 - the research institute building CNR (Via Giuseppe Moruzzi, 56127 Pisa, Italy). Dataset is from IPIN 2019 competition. </li> </ul> Used datasets A subset of input data is derivated from available logfiles provided by organizers of IPIN 2018 and IPIN 2019 competitions: <ul> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.;  Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. <a href="http://dx.doi.org/10.5281/zenodo.3606765">http://dx.doi.org/10.5281/zenodo.3606765</a>    </li> </ul> Funding The work was partially supported by the Slovak Grant Agency of the Ministry of Education and Academy of Science of the Slovak Republic under grant no. 1/0056/18 and by the Slovak Research and Development Agency under the contract no. APVV-15-0091. Contact For any further questions, please contact: Miroslav Opiela, miroslav.opiela@upjs.sk Institute of Computer Science, Faculty of Science, P. J. &Scaron;afárik University (UPJS), Ko&scaron;ice, Slovakia

opencc-zeroNov 2014View details →

Indoor Positioning Database

Database containing measurements from wireless networks.

opencc-by-4.0May 2022View details →

Indoor Positioning Simulation For Examination And Correction Of Occupancy Limits In Architectural Design

Dataset that contains the images of scenarios used for the analysis and the analysis itself.

opencc-by-4.0Jun 2018View details →

Indoor Positioning - de Blasio et al - dataset paper IEEE Access (2018)

This zip file contains the raw BLE data in .xlsx format obtained in the tests detailed in the following article (attached pdf): Gabriel de Blasio, Alexis Quesada-Arencibia, Carmelo R. García, José Carlos Rodríguez-Rodríguez, Roberto Moreno-Díaz jr. A Protocol-Channel-Based Indoor Positioning Performance Study for Bluetooth Low Energy, IEEE Access vol. 6, pp. 33440-33450 (2018) DOI: 10.1109/ACCESS.2018.2837497

opencc-by-4.0May 2019View details →

Indoor Positioning - de Blasio et al - dataset paper Sensors (2019)

This zip file contains the raw BLE data in .xlsx format obtained in the tests detailed in the following article (attached pdf): de Blasio, Gabriele Salvatore, Rodríguez-Rodríguez, José C., Garcia, Carmelo R., Quesada-Arencibia, Alexis. Beacon-related parameters of bluetooth low energy: development of a semi-automatic system to study their impact on indoor positioning systems, Sensors vol. 19(4), (2019) DOI: 10.3390/s19143087