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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>
Supplementary Materials for "UJI Probes: Dataset of Wi-Fi Probe Requests"
<p>This package contains an anonymized packets of 802.11 probe requests captured throughout March of 2023 at Universitat Jaume I. The packet capture file is 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).<br> <br> The dataset is usable for analyzis of Wi-Fi probe requests, presence detection, occupancy estimation or signal stability analyzis.</p>
WiPerf Vehicular Wi-Fi Performance Monitoring Dataset
<p>WiPerf is a Wi-Fi performance monitoring system that can measure quality-of-service (i.e., throughput) and collect channel state and GPS information from vehicles using Wi-Fi for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) communications.</p> <p>The dataset contains data collected on June 14<sup>th</sup>, 2022, from a real-world vehicular setup. For the setup we mounted two TP-Link Talon AD7200 routers on top of two vehicles, i.e., a 2006 Renault Megane Break and a 2002 Honda Jazz. One of the cars remained static while the other was driven around the stationary node for 10 laps at Rua D. Frei Vicente da Soledade e Castro in Porto,<br> Portugal. We collected 802.11n, 802.11ac, and 802.11ad data simultaneously using the three radio interfaces of the Talon AD7200 routers.</p> <p>The dataset also includes throughput and CPU load data collected using <em>iperf3 </em>and the <em>sar </em>application from the <em>sysstat </em>Linux utility package. These measurements were taken in a lab environment using the same Talon AD7200 routers. </p>
A Dataset for Exploring Wi-Fi Network Diversity in Vehicle-to-Infrastructure Communication
<p><strong>Introduction:</strong></p> <p>This dataset contains space and time-indexed performance data for Wi-Fi communication between a moving vehicle and a set of stationary Access Points (APs). In order to allow comparisons between technologies, 3 different types of Wi-Fi are used in parallel: 800.11n, ac, and ad.</p> <p>For more information, please consult the following article: <a href="https://www.cs.vassar.edu/~rpachecomeireles/research/papers/vnc-2020.pdf"><em>Exploring Wi-Fi Network Diversity for Vehicle-to-Infrastructure Communication</em></a>, Rui Meireles, António Rodrigues, Andrei Stanciu, Ana Aguiar, Peter Steenkiste, in the 2020 IEEE Vehicular Networking Conference (VNC 2020), December 2020, <a href="https://doi.org/10.1109/VNC51378.2020.9318407">doi:10.1109/VNC51378.2020.9318407</a>. Video presentation available <a href="https://youtu.be/IREMIGV4XLc">here</a>.</p> <p><strong>Experiment description:</strong></p> <ul> <li> <p>The AP was placed at the corner of a residential area intersection while the mobile client drove a circuit around it. The mobility pattern is shown in the animated file <code>vehicle-movement.gif</code>.</p> </li> <li> <p>The data is divided into traces, gathered on different dates, using different vehicles to support the AP on the roof, as shown below:</p> </li> </ul> <table> <tbody><tr> <th>trace nr</th> <th>date</th> <th>start time</th> <th>n(n)</th> <th>n(ac)</th> <th>n(ad)</th> <th>AP vehicle</th> <th>n(clients)</th> </tr> </tbody><tbody> <tr> <td>302</td> <td>2019-08-20</td> <td>10:28:45</td> <td>3262</td> <td>2787</td> <td>423</td> <td>2001 Honda Civic sedan</td> <td>1</td> </tr> <tr> <td>303</td> <td>2019-08-20</td> <td>11:26:23</td> <td>3374</td> <td>3027</td> <td>312</td> <td>-</td> <td>2</td> </tr> <tr> <td>304</td> <td>2019-08-20</td> <td>12:39:46</td> <td>1711</td> <td>216</td> <td>14</td> <td>-</td> <td>3 (n & ac) 2 (ad)</td> </tr> <tr> <td>401</td> <td>2019-08-22</td> <td>10:19:24</td> <td>1685</td> <td>1681</td> <td>545</td> <td>2003 Peugeot Partner</td> <td>1</td> </tr> <tr> <td>402</td> <td>2019-08-22</td> <td>10:48:26</td> <td>2859</td> <td>2827</td> <td>764</td> <td>-</td> <td>2</td> </tr> <tr> <td>403</td> <td>2019-08-22</td> <td>11:39:36</td> <td>135</td> <td>135</td> <td>116</td> <td>-</td> <td>2</td> </tr> <tr> <td>404</td> <td>2019-08-22</td> <td>11:42:50</td> <td>114</td> <td>114</td> <td>53</td> <td>-</td> <td>2</td> </tr> <tr> <td>405</td> <td>2019-08-22</td> <td>11:45:07</td> <td>2019</td> <td>2019</td> <td>507</td> <td>-</td> <td>2</td> </tr> </tbody> </table> <ul> <li><strong>APs:</strong> all positioned at coordinates {lat : 41.111879, lon : -8.631146}</li> </ul> <table> <tbody><tr> <th>ap</th> <th>device</th> <th>802.11 type</th> <th>channel</th> <th>cntr. freq (MHz)</th> <th>bw (MHz)</th> </tr> </tbody><tbody> <tr> <td>unifi-003</td> <td>ubiquiti ac lite</td> <td>n</td> <td>6</td> <td>2437</td> <td>20</td> </tr> <tr> <td>unifi-001</td> <td>-</td> <td>ac</td> <td>40</td> <td>5200</td> <td>40</td> </tr> <tr> <td>tp-01</td> <td>tp-link talon ad7200*</td> <td>ad</td> <td>1</td> <td>60480</td> <td>2160</td> </tr> </tbody> </table> <p>*running tp-link's original firmware, not OpenWrt</p> <ul> <li><strong>Main clients:</strong> all positioned in the moving vehicle's roof, a vw golf mk3</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link archer t4uh</td> <td>2</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-03)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <ul> <li><strong>Background clients:</strong> the purpose is to increase channel util.</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> <th>position</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w2</td> <td>fixed, ~2m away from AP</td> </tr> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w2</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-04)</td> <td>-</td> <td>macbook</td> <td>stopped vehicle's roof</td> </tr> </tbody> </table> <ul> <li><strong>Monitor nodes:</strong> all positioned in the moving vehicle's roof</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link talon ad7200 (tp-02)</td> <td>8</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-02)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <p>Dataset structure:</p> <p>All the packet captures are already digested and ready to use in the file <code>wifi-exp-log-summary.csv</code>. An explanation of the fields below:</p> <ul> <li><strong>systime</strong> : system time (1 Hz resolution) that this row refers to. All node clocks were synchronized through NTP.</li> <li><strong>traceNr</strong> : nr. of the trace the row belongs to.</li> <li><strong>lon</strong> : longitude (in degrees) reported by the receiver's GPS at <code>systime</code></li> <li><strong>lat</strong> : latitude reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverAlt</strong> : altitude (in meters) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverX</strong> : x coordinate of the receiver's position when space is discretized as a Cartesian plane and the sender is set to be the origin of the coordinate system. The x axis corresponds to east-west (positive values are east, negative values are west). Unit is meters.</li> <li><strong>receiverY</strong> : y coordinate of the receiver's position when space is discretized as a Cartesian plane</li> <li><strong>receiverDist</strong> : distance (in meters) of receiver to ap(s)</li> <li><strong>receiverSpeed</strong> : speed (in m/s) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverId</strong> : system-specific id for the client (in the vehicle)</li> <li><strong>senderId</strong> : system-specific id for the ap serving the client (side of the road)</li> <li><strong>isIperfOn</strong> : 1 if row's <code>systime</code> corresponds to a period where our UDP packet consumer application is known to have been running on the receiver side.</li> <li><strong>isInLap</strong> : 1 if this row's systime has been marked as being part of a time period where clients were doing laps around the APs, 0 otherwise.</li> <li><strong>rssiMean</strong> : the mean of the RSSI (Received Signal Strength Indicator) values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>snrMean</strong> : SNR (signal to noise ratio) retrieved from 802.11ad sectore sweep frames.</li> <li><strong>channelFreq</strong> : center frequency of the WiFi channel used, in MHz.</li> <li><strong>channelBw</strong> : bandwidth of the WiFi channel used, in MHz.</li> <li><strong>channelUtil</strong> : percentage of time the wireless medium was sensed to be busy during the 1-second period systime period the row refers to. <strong>In traces 40x, the 802.11n and ac routers didn't log channel busy time, and as such we had to approximate channel util. based on x,y coordinates and nr. of active clients.</strong></li> <li><strong>wifiType</strong> : 802.11 type (e.g., n, ac or ad).</li> <li><strong>nrClients</strong> : nr. of parallel clients operating in <code>wifiType</code> mode, on the same channel and bandwidth as <code>receiverId</code>.</li> <li><strong>dataRateMedian</strong> : the median of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>dataRateMean</strong> : the mean of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>nBytesReceived</strong> : total number of bytes received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>tghptConsumer</strong> : throughput reported by the UDP packet consumer application, during the 1-second period systime period the row refers to.</li> <li><strong>nRetries</strong> : nr. of WLAN-level re-transmissions on 1 second period</li> <li><strong>meanBeaconRssi</strong> : mean RSSI measured from beacons in 1 second period. nan values are filled with -100 dBm.</li> <li><strong>meanInterBeaconTime</strong> : mean interval between consecutive beacons, within 1 second period. nan values are filled with 1 sec.</li> <li><strong>nBeacons</strong> : total nr. of beacons received by client within 1 second period.</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>
UTMInDualSymFi: A Dataset of Dual-band Wi-Fi RSSI Data in Symmetric Indoor Environments
<p>The UTMInDualSymFi database contains a comprehensive source of dual-band Wi-Fi RSSI data.</p> <p>In total, Wi-Fi RSSI data for fingerprinting positioning were collected from 4 residential buildings <br> within Universiti Teknologi Malaysia (UTM) campus:</p> <p> - building 1 = F04 (raw training data + test data + radio maps)<br> - building 2 = CX1 (raw training data + test data + radio maps)<br> - building 3 = F03 (test data only)<br> - building 4 = CY2 (test data only)</p> <p>Buildings 1,3 are similar in structure, symmetric in layout and locations of access points.<br> Buildings 2,4 are similar in structure, symmetric in layout and locations of access points.</p> <p>Each building is multi-floor, with multiple wings at each floor. Wings are labeled as A,B,C.</p> <p>The floor(s)-wing(s) of each building at which data were collected are listed:</p> <p> Building Floor-wing<br> -------- ----------<br> F04 3-A<br> F04 3-B<br> F04 3-C<br> F04 4-A<br> F04 4-B<br> F04 4-C<br> CX1 3-A<br> CX1 3-B<br> CX1 5-A<br> CX1 5-B<br> F03 4-A<br> F03 4-B<br> F03 4-C </p> <p>Data were collected on two laptop devices. Devices are distinguished by wireless network adapters:</p> <p> - Device 1 = Intel<br> - Device 2 = Qualcomm</p> <p>RSSI values range between -50 dBm and -100 dBm<br> RSSI of (+)100 dBm signifies non detection of a certain access point/source</p> <p>Further details are provided in 'README.txt' (included in the data package).</p> <p>Detailed, data elaboration and benchmark performance analysis are reported in the related data descriptor publication: </p> <p>Abdullah, A.; Haris, M.; Aziz, O.A.; Rashid, R.A.; Abdullah, A.S. UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments. <em>Data</em> <strong>2023</strong>, <em>8</em>, 14. https://doi.org/10.3390/data8010014</p> <p><br> </p>
DATASET: IQ SAMPLES OF LTE, 5G NR, WI-FI, ITS-G5, AND C-V2X PC5
<div> <div> <div> <h1>Dataset: IQ samples of LTE, 5G NR, WiFi, ITS-G5, and C-V2X PC5</h1> <p>The dataset comprises IQ samples captured from ITSG-5, C-V2X PC5, WiFi, LTE, 5G NR and Noise. In each dataset cluster, 7500 examples are collected from each considered technology. The dataset collected was used for technology recognition and traffic charchterization model. More details on the structure and performance of the model can be found <a href="https://www.sciencedirect.com/science/article/abs/pii/S2214209622001103">here</a>.</p> <p> </p> </div> </div> </div> <div> <div> <div> <h1>Description of Dataset Collection</h1> <h2>LTE</h2> <p>For LTE dataset collection, <a href="https://www.srslte.com/" target="_blank" rel="nofollow noopener">srsRAN</a>, an open-source SDR platform, is used. This LTE SDR implementation is used to collect samples for the LTE dataset. The indoor testbed setup used to collect our dataset consists of one eNB host PC and one UE host PC. Each host PC is connected to a USRP X310 board, which is used as the RF front end. We use the latest srsRAN version 21.04, which is installed on each host PC. For the LTE dataset collection, the FDD mode with a 10 MHz bandwidth and a 5.9 GHz center frequency is used for the down-link traffic. For the LTE dataset, the traffic load was varied between 5 and 50 Mbps, and the MCS used was varied by manually configuring from MCS index 1 to 28 .</p> <h2>5G NR</h2> <p>The <a href="https://openairinterface.org/" target="_blank" rel="nofollow noopener">OpenAirInterface</a> SDR solution is used for 5G NR dataset collection. OpenAirInterface is an open source SDR platform that provides a 3GPP compliant implementation of eNB, UE, and EPC. The OpenAirInterface SDR solution also includes a 5G non-stand alone (NSA) mode which supports 5G networks by using existing 4G infrastructure. This SDR-based 5G network setup is used to collect the IQ samples for the 5G NR dataset. For the 5G NR dataset collection, a 1:1 static TDD configuration is used for up-link and down-link traffic in NSA mode. Numerology 1 is used at 10 MHz bandwidth and center frequency of 5.9 GHz. For the 5G NR dataset, the MCS used was varied by manually configuring different values ranging from MCS index 1 to 28, and the traffic load was varied between 5 and 50 Mbps.</p> <h2>Wi-Fi</h2> <p>For the WiFi dataset collection, the <a href="https://github.com/open-sdr/openwifi" target="_blank" rel="nofollow noopener">openwifi</a> SDR solution is used. openwifi is an open-source full-stack IEEE 802.11 a/g/n SDR implementation based on the Xilinx Zynq System on Chip that includes a Field Programmable Gate Array (FPGA) and an ARM processor. For our dataset, we used an IEEE802.11n access point and a client connected to it. WiFi traffic generated cover a wide range of traffic loads, i.e., 10–200 packets per second (pps), with packet sizes ranging from 500 to 1500 bytes. The MCS used was varied by manually configuring the MCS index value between 0 and 7.</p> <h2>ITS-G5 and C-V2X PC5</h2> <p>The <a href="https://www.marquez-barja.com/images/papers/vehicuularcomms21-openaccess-version.pdf" target="_blank" rel="nofollow noopener">CAMINO</a> framework was used for the ITS-G5 and C-V2X PC5 dataset collection. CAMINO is a core framework for managing the V2X communication technologies, including ITS-G5, C-V2X PC5 and C-V2X Uu (5G/4G). The CAMINO framework is used to dynamically generate standardized C-ITS service packets, including Cooperative Awareness (CA), Decentralized Environmental Notification (DEN), and Infrastructure to Vehicle Information (IVI) message packets. The CAMINO software is implemented on the infrastructure deployed as part of the <a href="https://ieeexplore.ieee.org/document/9621229" target="_blank" rel="nofollow noopener">Belgian Smart Highway testbed</a>. The Smart Highway is a testbed deployed by IMEC on the E313 highway, near Antwerp, Flanders. The Smart Highway testbed consists of eight RSUs and 2 OBUs. Each RSU and OBU includes a general purpose CPU running the CAMINO software, and Cohda MK5 and MK6c modules, which are COTS ITS-G5 and C-V2X modules respectively. In our dataset collection, RSU4 is used as a transmitter, and a USRP N310 connected to RSU3 is used to capture and store the samples. To represent a wide range of traffic characteristics, different packet sizes of 300B for CA, 300B and 600B for DEN, and 600B for IVI packets were used at inter-packet intervals of 20, 50, 100, and 200 seconds. The MCS used were varied by manually changing the MCS index value in the configuration files of the Cohda devices. The MCS index of ITS-G5 varied from 0 to 7. For the C-V2X PC5, the MCS is varied between 0 and 20.</p> </div> </div> </div> <div> </div>
Vehicle-to-Infrastructure IEEE 802.11ad Wi-Fi dataset
<p><strong>1. Introduction</strong></p> <p>This dataset contains space and time-indexed data collected in a Vehicle-To-Infrastructure (V2I) communication scenario, where a moving vehicle downloaded data from a stationary Access Point (AP) using IEEE 802.11ad Wi-Fi.</p> <p>The dataset is comprised of both throughput data and detailed frame information captured with <code>tcpdump</code>. It can be used to study 802.11ad's behavior in vehicular environments, in particular in what pertains to antenna sector selection.</p> <p>This dataset is associated with the following article, which we recommend consulting for more information: <a href="https://www.cs.vassar.edu/~rpachecomeireles/research/papers/comcom-2022-preprint.pdf"><em>Geolocation-based Sector Selection for Vehicle-to-Infrastructure 802.11ad Communication</em></a>, Mateus Mattos, António Rodrigues, Rui Meireles, Ana Aguiar, in the Elsevier Journal of Computer Communications, Volume 193, ISSN 0140-3664, 2022, <a href="https://doi.org/10.1016/j.comcom.2022.07.005">doi:10.1016/j.comcom.2022.07.005</a>.</p> <p><strong>2. Experimental setup</strong></p> <p>The AP was placed at the corner of a residential-area intersection while a mobile client vehicle drove around it, downloading data from the AP.</p> <p>Commercial Off-The-Shelf (COTS) TP-Link Talon AD7200 were used for both the stationary AP and mobile client. A third AD7200 configured in promiscuous mode was placed next to the mobile client, in order to capture the control frames being exchanged.</p> <p><strong>2.1 Experimental nodes</strong></p> <table> <tbody><tr> <th>MAC address</th> <th>Role</th> <th>Position</th> <th>Orientation</th> </tr> </tbody><tbody> <tr> <td><code>70:4f:57:72:b2:52</code></td> <td>AP</td> <td>Static, latitude: 41.111879, longitude: -8.631146, mounted of top of a parked vehicle</td> <td>Perpendicular to road</td> </tr> <tr> <td><code>50:c7:bf:97:8a:ac</code></td> <td>Client</td> <td>Mobile, mounted on roof of client vehicle</td> <td>Towards front of vehicle</td> </tr> <tr> <td><code>50:c7:bf:3c:53:1c</code></td> <td>Monitor</td> <td>Mobile, mounted on roof of client vehicle</td> <td>Towards front of vehicle</td> </tr> </tbody> </table> <p><strong>3. Trace description</strong></p> <p>The data is divided into traces. Each trace represents an uninterrupted period of data collection. The experiments were ran twice, once in 2020, and again in 2021. Environmental conditions, such as weather and topography, were consistent between the two experiment sets.</p> <p><strong>3.1 2020 traces</strong></p> <table> <tbody><tr> <th>Trace #</th> <th>Start timestamp</th> <th>End timestamp</th> <th>Mobility pattern</th> </tr> </tbody><tbody> <tr> <td>235</td> <td>1593946456</td> <td>1593946593</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed</td> </tr> <tr> <td>237</td> <td>1593946793</td> <td>1593946908</td> <td>Vehicle moving westwards from AP and back, straight line, low speed</td> </tr> <tr> <td>238</td> <td>1593946938</td> <td>1593947076</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed</td> </tr> <tr> <td>240</td> <td>1593947181</td> <td>1593947332</td> <td>Vehicle moving westwards from AP and back, straight line, low speed</td> </tr> <tr> <td>241</td> <td>1593947360</td> <td>1593947499</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed</td> </tr> <tr> <td>242</td> <td>1593947566</td> <td>1593947700</td> <td>Vehicle moving westwards from AP and back, straight line, low speed</td> </tr> <tr> <td>243</td> <td>1593947759</td> <td>1593947915</td> <td>Vehicle moving southwards from AP and back, straight line, low speed</td> </tr> <tr> <td>244</td> <td>1593947971</td> <td>1593948111</td> <td>Vehicle moving southwards from AP and back, straight line, low speed</td> </tr> <tr> <td>245</td> <td>1593948210</td> <td>1593948348</td> <td>Vehicle moving southwards from AP and back, straight line, low speed</td> </tr> <tr> <td>246</td> <td>1593948433</td> <td>1593948569</td> <td>Vehicle moving northwards from AP and back, straight line, low speed</td> </tr> <tr> <td>247</td> <td>1593948631</td> <td>1593948795</td> <td>Vehicle moving northwards from AP and back, straight line, low speed</td> </tr> <tr> <td>248</td> <td>1593948904</td> <td>1593949053</td> <td>Vehicle moving northwards from AP and back, straight line, low speed</td> </tr> <tr> <td>249</td> <td>1593949307</td> <td>1593950016</td> <td>Vehicle driving circuit around the intersection, medium speed (see file <code>driving-circuit.gif</code>)</td> </tr> <tr> <td>250</td> <td>1593950073</td> <td>1593950643</td> <td>Vehicle driving circuit around the intersection, medium speed (see file <code>driving-circuit.gif</code>)</td> </tr> <tr> <td>251</td> <td>1593950682</td> <td>1593951240</td> <td>Vehicle driving circuit around the intersection, medium speed (see file <code>driving-circuit.gif</code>)</td> </tr> </tbody> </table> <p><strong>3.2 2021 traces</strong></p> <table> <tbody><tr> <th>Trace #</th> <th>Start timestamp</th> <th>End timestamp</th> <th>Mobility pattern</th> </tr> </tbody><tbody> <tr> <td>201</td> <td>1632244398</td> <td>1632244548</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>202</td> <td>1632244563</td> <td>1632244663</td> <td>Vehicle moving westwards from AP and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>203</td> <td>1632244674</td> <td>1632244799</td> <td>Vehicle moving southwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>204</td> <td>1632244812</td> <td>1632244915</td> <td>Vehicle moving northwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>206</td> <td>1632245138</td> <td>1632245346</td> <td>Vehicle moving eastwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>207</td> <td>1632245355</td> <td>1632245463</td> <td>Vehicle moving southwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>208</td> <td>1632245472</td> <td>1632245581</td> <td>Vehicle moving northwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>209</td> <td>1632245592</td> <td>1632245790</td> <td>Vehicle moving southwards from AP and back, northwards from AP and back, then westwards and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>210</td> <td>1632245798</td> <td>1632245987</td> <td>Vehicle moving eastwards from AP and back, straight line, low speed (see file <code>driving-patterns-by-trace-2021.pdf</code>)</td> </tr> <tr> <td>302</td> <td>1632335672</td> <td>1632336273</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>303</td> <td>1632336286</td> <td>1632336870</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>401</td> <td>1634983869</td> <td>1634984363</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>402</td> <td>1634984410</td> <td>1634984881</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>403</td> <td>1634984908</td> <td>1634985562</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>404</td> <td>1634985666</td> <td>1634986835</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>405</td> <td>1634986980</td> <td>1634988303</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>406</td> <td>1634988352</td> <td>1634989470</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> <tr> <td>407</td> <td>1634989490</td> <td>1634990434</td> <td>Vehicle driving circuit around the intersection (see file <code>driving-circuit.gif</code>), medium speed</td> </tr> </tbody> </table> <p><strong>4. Data description</strong></p> <p><strong>4.1 File structure</strong></p> <p>The data from the 2020 and 2021 sets of experiments can be found in subfolders <code>2020</code> and <code>2021</code>, respectively.</p> <p>Each subfolder constains the following:</p> <ul> <li><code>gps.csv</code>: client vehicle mobility trace (individual NMEA sentences);</li> <li><code>gps-merged.csv</code>: client vehicle mobility trace (summarized);</li> <li><code>thrghpt.csv</code>: application throughput data;</li> <li><code>wifi.csv</code>: summarized 802.11ad frame data;</li> <li><code>pcap/</code>: contains raw <code>.pcap</code> files used to generate <code>wifi.csv</code>, separated by trace number;</li> <li><code>configs/</code>: includes JSON file with fields and filters used by <code>tshark</code> for the generation of <code>wifi.csv</code>.</li> </ul> <p><strong>4.2 GPS data: <code>gps.csv</code></strong> and <code>gps-merged.csv</code></p> <p>GPS data was captured by a high-accuracy GPS device: Trimble Pro Series 6H <a href="http://www.windenvironmental.com/Data-Sheets/Trimble-Pro%20Series-DS.pdf">[link]</a>, and consists of a combination of fields provided by multiple NMEA <code>GP*</code> sentence codes, namely: <code>GPRMC</code>, <code>GPGGA</code>, <code>GPGLL</code>, and <code>GNGSA</code> <a href="http://aprs.gids.nl/nmea/">[link]</a>.</p> <p>Column description</p> <p><code>gps.csv</code> is a table with the following columns:</p> <table> <tbody><tr> <th>Column</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td><code>timestamp</code></td> <td>UNIX system timestamp at which GPS sentence was recorded, in seconds</td> </tr> <tr> <td><code>lat</code></td> <td>Latitude, in decimal degrees</td> </tr> <tr> <td><code>lon</code></td> <td>Longitude, in decimal degrees</td> </tr> <tr> <td><code>alt</code></td> <td>Altitude, in meters</td> </tr> <tr> <td><code>speed</code></td> <td>Ground speed, in knots</td> </tr> <tr> <td><code>HDOP</code></td> <td>Horizontal dilution of precision</td> </tr> <tr> <td><code>PDOP</code></td> <td>Position dilution of precision</td> </tr> <tr> <td><code>VDOP</code></td> <td>Vertical dilution of precision</td> </tr> <tr> <td><code>heading</code></td> <td>Direction of movement as provided by GPS device, in clockwise degrees from north</td> </tr> <tr> <td><code>identifier</code></td> <td>NMEA sentence code, e.g., <code>GPRMC</code></td> </tr> <tr> <td><code>gpstime</code></td> <td>Timestamp as provided by GPS device, in seconds</td> </tr> </tbody> </table> <p><strong>Note:</strong> Because each GPS sentence only contains a subset of the listed columns, any missing values are set to -1.0.</p> <p><code>gps-merged.csv</code> is a table containing all mobility information aggregated by GPS timestamp, for ease of use. It contains the following columns:</p> <table> <tbody><tr> <th>Column</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td><code>gpstime</code></td> <td>Timestamp as provided by GPS device, in seconds</td> </tr> <tr> <td><code>timestamp</code></td> <td>Average UNIX system timestamp at which the GPS sentences from which this row was created were recorded, in seconds</td> </tr> <tr> <td><code>lat</code></td> <td>Latitude, in decimal degrees</td> </tr> <tr> <td><code>lon</code></td> <td>Longitude, in decimal degrees</td> </tr> <tr> <td><code>alt</code></td> <td>Altitude, in meters</td> </tr> <tr> <td><code>speed</code></td> <td>Ground speed, in knots</td> </tr> <tr> <td><code>HDOP</code></td> <td>Horizontal dilution of precision</td> </tr> <tr> <td><code>PDOP</code></td> <td>Position dilution of precision</td> </tr> <tr> <td><code>VDOP</code></td> <td>Vertical dilution of precision</td> </tr> <tr> <td><code>heading</code></td> <td>Direction of movement as provided by GPS device, in clockwise degrees from north</td> </tr> </tbody> </table> <p><strong>4.3 Application layer throughput : <code>thrghpt.csv</code></strong></p> <p>Data was sent from a custom sender application running on the AP, at the maximum possible rate. A custom receiver application on the client vehicle consumes the data. A time-indexed log of the amount of data sent and received was recorded.</p> <p>Column description</p> <p><code>thrghpt.csv</code> is a table with the following columns:</p> <table> <tbody><tr> <th>column</th> <th>description</th> </tr> </tbody><tbody> <tr> <td><code>timestamp</code></td> <td>UNIX system timestamp the throughput record pertains to</td> </tr> <tr> <td><code>pckt_cntr</code></td> <td>Number of packets received within the current record</td> </tr> <tr> <td><code>byte_cntr</code></td> <td>Number of bytes received within the current record</td> </tr> <tr> <td><code>elapsed_time</code></td> <td>Time elapsed since previous throughput record</td> </tr> <tr> <td><code>thrghpt</code></td> <td>Throughput for the current record, in in Megabit per second (Mbps)</td> </tr> <tr> <td><code>inter_arrival_avg </code></td> <td>Average inter-packet arrival time during the recording period, in seconds</td> </tr> <tr> <td><code>diff_local_avg </code></td> <td>Average delta between the timestamp recorded in the packet's payload (set by the sender) and the local timestamp in the receiver, in microseconds</td> </tr> <tr> <td><code>trace_nr</code></td> <td>Trace number the throughput data is associated with</td> </tr> </tbody> </table> <p><strong>4.4 802.11ad frame data: <code>wifi.csv</code></strong></p> <p>The <code>wifi.csv</code> file contains 802.11ad frame data, captured with <code>tcpdump</code>, on a Talon AD7200 router configured in promiscuous mode and colocated with the mobile client device.</p> <p>Data collection and processing</p> <p>The following <code>tcpdump</code> command was used to collect raw data frames:</p> <pre><code>tcpdump -B 100000 -s96 -i <ad-monitor-interface> -y IEEE802_11_RADIO -w <pcap-file> & </code></pre> <p>In order to create <code>wifi.csv</code>, the raw data frames were processed using <code>tshark</code> in order to filter out unnecessary information. More specifically, we ran the following command:</p> <pre><code>tshark -r <input-pcap> -2 -T fields <fields> -Y "<filter>" -E header=y -E separator=, -E quote=d -E occurrence=f </code></pre> <p>The raw input <code>.pcap</code> files for each trace are provided in the <code>pcap/</code> folder. The parameters <code><filter></code> and <code><fields></code> represent the filtering conditions and what fields we want to extract from each frame, respectively. The actual values used are provided in the <code>configs/tshark.json</code> file.</p> <p>Frames were filtered based on a single field: the WLAN frame type and subtype, or <code>wlan.fc.type_subtype</code>. Only the following types of frames were kept:</p> <table> <tbody><tr> <th>Frame type/subtype value</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>0x0000</td> <td>Association request</td> </tr> <tr> <td>0x0001</td> <td>Association response</td> </tr> <tr> <td>0x0002</td> <td>Re-association request</td> </tr> <tr> <td>0x0003</td> <td>Re-association response</td> </tr> <tr> <td>0x000a</td> <td>Disassociation</td> </tr> <tr> <td>0x000b</td> <td>Authentication</td> </tr> <tr> <td>0x000c</td> <td>De-authentication</td> </tr> <tr> <td>0x0019</td> <td>Block ACKs</td> </tr> <tr> <td>0x001d</td> <td>Clear-to-send</td> </tr> <tr> <td>0x0028</td> <td>QoS data</td> </tr> <tr> <td>0x0030</td> <td>DMG beacon</td> </tr> <tr> <td>0x0164</td> <td>Grant</td> </tr> <tr> <td>0x0167</td> <td>Grant ACK</td> </tr> <tr> <td>0x0168</td> <td>SLS</td> </tr> <tr> <td>0x0169</td> <td>SLS feedback</td> </tr> <tr> <td>0x016a</td> <td>SLS feedback ACK</td> </tr> </tbody> </table> <p>Column description</p> <p><code>wifi.csv</code> is a table with the following columns:</p> <table> <tbody><tr> <th>Column</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td><code>frame.time_epoch</code></td> <td>UNIX timestamp of frame capture, in seconds (with microsecond resolution)</td> </tr> <tr> <td><code>frame.number</code></td> <td>Ordinal number attributed to captured frame</td> </tr> <tr> <td><code>frame.len</code></td> <td>Frame length, in bytes</td> </tr> <tr> <td><code>ip.src</code></td> <td>Source IP address</td> </tr> <tr> <td><code>ip.dst</code></td> <td>Destination IP address</td> </tr> <tr> <td><code>ip.flags</code></td> <td>IP flags</td> </tr> <tr> <td><code>ip.frag_offset</code></td> <td>IP fragmentation offset</td> </tr> <tr> <td><code>ip.hdr_len</code></td> <td>IP header length</td> </tr> <tr> <td><code>ip.id</code></td> <td>IP identification field</td> </tr> <tr> <td><code>ip.proto</code></td> <td>IP protocol field</td> </tr> <tr> <td><code>ip.reassembled_in</code></td> <td>Frame number in which IP packet is reassembled</td> </tr> <tr> <td><code>radiotap.channel.flags.2ghz</code></td> <td>1 if channel frequency is in 2.4 GHz range, 0 otherwise</td> </tr> <tr> <td><code>radiotap.channel.flags.5ghz</code></td> <td>1 if channel frequency is in 5 GHz range, 0 otherwise</td> </tr> <tr> <td><code>radiotap.channel.freq</code></td> <td>Channel frequency (60480 Hz in this case)</td> </tr> <tr> <td><code>radiotap.length</code></td> <td>IEEE 802.11 radiotap capture header length</td> </tr> <tr> <td><code>radiotap.mcs.index</code></td> <td>Modulation Coding Scheme index</td> </tr> <tr> <td><code>udp.srcport</code></td> <td>UDP source port</td> </tr> <tr> <td><code>udp.dstport</code></td> <td>UDP destination port</td> </tr> <tr> <td><code>wlan.ba.bm</code></td> <td>Block ACK bitmap</td> </tr> <tr> <td><code>wlan.bf</code></td> <td>Full beamforming field of WLAN frame</td> </tr> <tr> <td><code>wlan.bf.isInit</code></td> <td>Whether or not frame is SLS initiator</td> </tr> <tr> <td><code>wlan.bf.isResp</code></td> <td>Whether or not frame is SLS responder</td> </tr> <tr> <td><code>wlan.bf.num_dmg_ants</code></td> <td>Number of DMG antennas</td> </tr> <tr> <td><code>wlan.bf.num_sectors</code></td> <td>Number of SLS sectors</td> </tr> <tr> <td><code>wlan.bf.train</code></td> <td>Whether or not frame is part of SLS training</td> </tr> <tr> <td><code>wlan.fc.retry</code></td> <td>Whether WLAN frame is re-transmitted</td> </tr> <tr> <td><code>wlan.fc.type_subtype</code></td> <td>WLAN frame type and subtype</td> </tr> <tr> <td><code>wlan.fixed.ssc.sequence</code></td> <td>WLAN starting sequence number</td> </tr> <tr> <td><code>wlan.fixed.timestamp</code></td> <td>WLAN timestamp</td> </tr> <tr> <td><code>wlan.frag</code></td> <td>WLAN fragment number</td> </tr> <tr> <td><code>wlan.ta</code></td> <td>WLAN transmitter MAC address</td> </tr> <tr> <td><code>wlan.ra</code></td> <td>WLAN receiver MAC address</td> </tr> <tr> <td><code>wlan.seq</code></td> <td>WLAN frame sequence number</td> </tr> <tr> <td><code>wlan.ssw</code></td> <td>Full Sector-level Sweep (SLS) field of WLAN frame</td> </tr> <tr> <td><code>wlan.ssw.cdown</code></td> <td>SLS countdown (CDOWN) number</td> </tr> <tr> <td><code>wlan.ssw.direction</code></td> <td>SLS direction (0: frame sent by SLS initiator, 1: by SLS responder)</td> </tr> <tr> <td><code>wlan.ssw.sector_id</code></td> <td>ID of sector used for SLS frame</td> </tr> <tr> <td><code>wlan.sswf</code></td> <td>Full SLS feedback field of WLAN frame</td> </tr> <tr> <td><code>wlan.sswf.sector_select</code></td> <td>SLS Feedback Sector Select</td> </tr> <tr> <td><code>wlan.sswf.snr_report</code></td> <td>SLS Feedback SNR Report</td> </tr> <tr> <td><code>wlan_radio.11n.mcs_index</code></td> <td>WLAN MCS index</td> </tr> <tr> <td><code>wlan_radio.channel</code></td> <td>WLAN channel</td> </tr> <tr> <td><code>wlan_radio.data_rate</code></td> <td>WLAN data rate</td> </tr> <tr> <td><code>wlan_radio.duration</code></td> <td>WLAN frame duration</td> </tr> <tr> <td><code>wlan_radio.frequency</code></td> <td>WLAN channel frequency</td> </tr> <tr> <td><code>wlan_radio.noise_dbm</code></td> <td>WLAN noise level, in dBm</td> </tr> <tr> <td><code>wlan_radio.phy</code></td> <td>WLAN PHY type</td> </tr> <tr> <td><code>wlan_radio.preamble</code></td> <td>WLAN preamble</td> </tr> <tr> <td><code>wlan_radio.signal_dbm</code></td> <td>WLAN signal strength, in dBm</td> </tr> <tr> <td><code>wlan_radio.timestamp</code></td> <td>WLAN TSF timestamp</td> </tr> <tr> <td><code>data.text</code></td> <td>Data enclosed in WLAN data frame</td> </tr> <tr> <td><code>trace_nr</code></td> <td>Number of the trace the frame is associated with</td> </tr> </tbody> </table>
Dataset for Vehicle Indoor Positioning in Industrial Environments with Wi-Fi, inertial, and odometry data
<p>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).</p> <p>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.</p> <p>List of sensors:</p> <ul> <li>4x <strong>Wi-Fi interfaces</strong>: Edimax EW7811-Un</li> <li>2x <strong>IMUs</strong>: Adafruit BNO055</li> <li>1x <strong>Absolute Encoder</strong>: US Digital A2 (attached to a wheel with a diameter of 125 mm)</li> </ul> <p>This dataset includes:</p> <ul> <li>1x <strong>Wi-Fi radio map</strong> that can be used for Wi-Fi fingerprinting.</li> <li>6x <strong>Trajectories</strong>: including sensor data + ground truth.</li> <li><strong>APs Information</strong>: list of APs in the building, including their position and transmission channel.</li> <li><strong>Floor plan:</strong> image of the building's floor plan with obstacles and non-navigable areas.</li> <li><strong>Python package</strong> 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> <p> </p> <p>When using this dataset, please cite its data description paper:</p> <p>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. <em>Data</em> <strong>2023</strong>, <em>8</em>, 157. <a href="https://doi.org/10.3390/data8100157" target="_blank" rel="noopener">https://doi.org/10.3390/data8100157</a> </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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.