Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

GSPEED - BLE-based gait speed dataset

Bluetooth Low Energy (BLE) database to evaluate the gait speed of individuals in an in-home environment. The database is composed of several BLE RSSI measurements from different wearable devices and different BLE beacons, corresponding to 382 walks performed by 13 actors. Each row represents an RSSI measurement. The structure of the dataset is as follows: <ul> <li> <pre>mac: The MAC address of the detected beacon. rssi: The RSSI value obtained for the beacon. device: A four-character descriptor for the smartwatch that performed the scan. timestamp: The time stamp at which the scan was received. user: The id of the user that was performing the experiment. direction: A number (0 or 1) indicating the direction of the walk. walk_id: A number that identifies each walk. speed: The actual speed of the user, in $m/s$.</pre> </li> </ul>

opencc-by-4.0Oct 2019View details →

BLE RSSI vs distance

The data set has been used in SmartCPS experiment within <a href="https://www.fed4fire.eu">Fed4Fire+</a> continuous call for studying RSSI (of BLE devices sending advertisements) depending on the distance to the scanning nodes. The data set contains location information transferred over Bluetooth Low Energy from a single BLE tag and registered by 5 nodes from <a href="https://doc.lab.cityofthings.eu/wiki/Nodes">CityLab testbed</a>: <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node9">Node 9</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node33">Node 33</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node34">Node 34</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node35">Node 35</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node36">Node 36</a> The data is stored in CSV format (with the following columns): node - id of the CityLab testbed node (node_lat, node_lon) - GPS coordinates of the node (tag_lat, tag_lon) - GPS coordinates of the tag ts - timestamp (milliseconds since the UNIX epoch: 1 January 1970) rssi - received signal strength indication

opencc-by-4.0Jul 2021View details →

Open-Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection"

This archive contains three folders which are supplementary material for the paper accepted for publishing in IEEE Sensors Journal. Contents: <ul> <li> The folder `open-access-data/upb/` contains the measurements acquired at UPB. The subfolders are named as `upb_ble_*`, where an asterisk masks the directory number. Whenever UPB is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/tau/` contains the measurements acquired at TAU. The subfolders are named as `tau_ble_*`, where an asterisk masks the directory number. Whenever TAU is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/wifi-on-off/` contains a sample code to read the files and plot the data from Fig. 14 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.py` and Fig. 15 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.ipynb`.</li> </ul> Results based on the data have been presented in the paper: Flueratoru, L., Shubina, V., Niculescu, D., Lohan, E.S. (2021). On the High Fluctuations of Received Signal Strength Measurements with BLE Signals for Contact Tracing and Proximity Detection, IEEE Sensors, Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation To cite these data sets please use the following: Laura Flueratoru, Viktoriia Shubina, Dragoș Niculescu, & Elena Simona Lohan. (2021). Open Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4643668

opencc-by-4.0Sep 2021View details →

BLE RSS dataset for fingerprinting radio map calibration

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. The set contains both raw and preprocessed measurement data. The Python code for raw data loading is supplied. The detailed dataset description can be found in the dataset_description.pdf file. When using the dataset, please consider citing the original paper, in which the data were used: M. Kolakowski, “Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning”, Sensors , vol. 21, 6270, Sep. 2021 <a href="https://doi.org/10.3390/s21186270">https://doi.org/10.3390/s21186270</a>

opencc-by-4.0Oct 2020View details →

SAL-Autarkic-Localization-RSSI-BLE-Dataset: SAL-RB-Dataset

The SAL-Autarkic-Localization-RSSI-BLE-Dataset (SAL-RB-Dataset) is a set of more than 20 000 labeled Received Signal Strength Indicator (RSSI) measurements for indoor localization with Bluetooth Low Energy (BLE)

opencc-by-4.0May 2022View details →

BLE RSSI Database for Analyzing Routines of Community-Dwelling Older Adults

The database includes the RSSI information emitted by BLE beacons installed in an elderly care home and collected by smartwatches worn by volunteers. Raw data and processed data are provided. Also, files in the Python programming language are included to facilitate data manipulation.   S. Lluva Plaza, R. Montoliu Colás, A. Jiménez Martín, J.M. Villadangos Carrizo, E. Sansano Sansano and J.J. García Domínguez, "BLE RSSI Database for Analyzing Routines of Community-Dwelling Older Adults," 2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Valencia, Spain, 2022, pp. 50-55, doi: 10.1109/ICUMT57764.2022.9943410.

openother-openMay 2022View details →

InSecTT BLE Channel Sniff Dataset

This dataset provides Bluetooth Low Energy (BLE) measurements for a single BLE channel, observed with a Software Defined Radio (SDR) in close proximity to several BLE devices. Since BLE uses channel hopping, not every message is detected if only a single channel is observed. In general, we do not know the access address, the connection interval, or the hopping pattern of the connections [1]. This measurement set provides the raw bitstream of the received BLE messages, as well as the already extracted measurements of the BLE connections including the header and measured timestamp. For each measurement set, we provide metadata with information about the BLE devices that are part of the setup. In BLE there are currently two different channel hop algorithms used [1]. The first one is the Channel Selection Algorithm 1 (CSA1), which was released with the first BLE specification, and the second is the Channel Selection Algorithm 2 (CSA2) which was implemented with the BLE version 5.0. This dataset provides measurements with BLE devices using both algorithms. The intention of this dataset, is to show that even though channel hopping is used, it is possible to reconstruct certain communication parameters and predict future channel access of the BLE connections by only passively listening to the channel. This dataset is a work from <a href="https://silicon-austria-labs.com/">Silicon Austria Labs GmbH</a> (SAL) and the <a href="https://www.jku.at/en/institute-for-communications-engineering-and-rf-systems/">Institute for Communications Engineering and RF-Systems</a> (NTHFS) of the Johannes Kepler University (JKU) in Linz for the <a href="https://www.insectt.eu/">InSecTT project</a>.

opencc-by-4.0Aug 2024View details →

Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning

The dataset titled "Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning" was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms.

openmit-licenseDec 2018View details →

BLE RSS measurements database and supporting materials

BLE RSS measurements database and supporting materials 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. Citation request: 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 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

zenodo36/100

Outdoor fingerprint localization with BLE beacons

IntroductionThe data set contains received signal strength (RSS) measurements made with Bluetooth Low Energy (BLE) technology, which can be used for outdoor fingerprint-based localization applications, as presented in an article "<a href="https://ieeexplore.ieee.org/document/9900607">LOG-a-TEC Testbed Outdoor Localization Usign BLE Beacons</a>".Measurement setupThe measurements were created with WL1837MOD radio connected to a <a href="https://log-a-tec.eu/hw-lgtc.html">in-house embedded device</a>.  The data set was collected with 40 nodes of the <a href="https://log-a-tec.eu">LOG-a-TEC testbed</a> positioned at the campus of the Jožef Stefan Institute, Ljubljana. The experimentation area is composed of 5 x 26 positions separated by 1.2 m covering 150 square meters. On each position a mobile phone was broadcasting BLE advertising beacons with power of -2 dBm in interval of 100 ms. Surrounding testbed nodes were collecting the beacons for approximately a minute for each position.Data setMeasurements are stored in JSON format where each object contains rss measurement (in dBm) with corresponding timestamp (in seconds). The folder contains two JSON files:<ul><li>spring_data.json - measurements made in May 2022,</li><li>winter_data.json - smaller measurements made in December 2021. This data set contains only the measurements from the middle row of the campus park.</li></ul>

opencc-by-4.0Dec 2022View details →

zenodo36/100

BLE Ray-Tracing Simulation Dataset

BLE ray-tracing propagation data generated via Altair Feko WinProp software in a simulation environment of dimensions 14m x 7m. Four horizontal facing APs are placed in the corners of the room at 2.5m height and at 45 degrees azimuth rotation pointing towards the center of the room. The elevation angle of all APs is 45 degrees pointing downwards. Three transmitting frequencies are simulated i.e., 2402, 2426 and 2480 MHz, corresponding to the three advertising BLE channels (numbered 37, 38 and 39, respectively) and two antenna polarizations (omni-directional), i.e., horizontal and vertical. The tag is positioned at a fixed height of 1.5 m (z-dimension) and 2450 signal samples are collected per  room configuration, evenly distributed across the room. Data was collected for 9 different room configurations with varying furniture, furniture material and anchor points positions and orientation as shown below.    <table> <tbody> <tr> <td> <table> <thead> <tr> <th>Room Setup</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>testbench_01</td> <td>No line-of-sight blocking furniture</td> </tr> <tr> <td>testbench_01_furniture_low</td> <td>One line-of-sight blocking furniture</td> </tr> <tr> <td>testbench_01_furniture_mid</td> <td>Three line-of-sight blocking furniture</td> </tr> <tr> <td>testbench_01_furniture_high</td> <td>Six line-of-sight blocking furniture</td> </tr> <tr> <td>testbench_01_furniture_low_concrete</td> <td>Same as low but with concrete furniture</td> </tr> <tr> <td>testbench_01_furniture_mid_concrete</td> <td>Same as mid but with concrete furniture</td> </tr> <tr> <td>testbench_01_furniture_high_concrete</td> <td>Same as high but with concrete furniture</td> </tr> <tr> <td>testbench_01_rotated_anchors</td> <td>Same as testbench_01 but the anchors have been rotated clockwise by 5 degrees</td> </tr> <tr> <td>testbench_01_translated_anchors</td> <td>Same as testbench_01 but the anchors have been translated by 10cm</td> </tr> </tbody> </table> </td> </tr> </tbody> </table> For each room setup, data is split into 12 json files - 6 for the tag signal data collected from the 4 anchor points and 6 for the properties of each anchor point. The 6 different files correspond to the different channel-polarization combinations as indicated by their names.  Each entry of the anchor json files contains the following information: <ul> <li>anchor: anchor's index</li> <li>x_anchor, y_anchor, z_anchor: anchor point's coordinates</li> <li>az_anchor: anchor's horizontal rotation</li> <li>el_anchor: anchor's vertical rotation</li> <li>reference_power: received signal strength (RSS) reference value in dB</li> </ul> Each entry of the tag json files contains the following information: <ul> <li>anchor: anchor's index</li> <li>point: point's index</li> <li>x_tag, y_tag, z_tag: tag point's coordinates</li> <li>los: point is in line of sight of anchor {0=false,1=true}</li> <li>relative power: RSS value in dB</li> <li>pdda_input_real: in-phase components of the anchor's antennas' measurements</li> <li>pdda_input_image: quadrature-phase components of the anchor's antennas' measurements</li> <li>pdda_phi: pdda prediction for azimuth angle</li> <li>pdda_theta: pdda prediction for elevation angle</li> <li>pdda_out_az: pdda's spatial power spectrum for azimuth angle</li> <li>pdda_out_el: pdda's spatial power spectrum for elevation angle</li> <li>true_phi: actual azimuth angle</li> <li>true_theta: actual azimuth angle</li> </ul>

opencc-by-4.0Feb 2022View details →

zenodo36/100

WatchBLoc: A smartwatch IMU and ambient BLE dataset for room-level localisation

WatchBLoc dataset This dataset consists of BLE RSSIs (emitted from identical BLE beacons) and IMU recordings (3-axial acceleration, 3-axial gyroscope, 3-axial magnetometer) recorded by a Sony Smartwatch 3. All participants wore the smartwatch in their right hand, which was the dominant hand in all participants. The data were recorded across two environments: a real-home and a demo-home. The demo-home consists of 6 rooms: big office, small office, kitchen, bathroom, meeting room, lab room. The real-home consists of 6 rooms: kitchen, living room, bedroom, bathroom, office, and loo. However, the living room and kitchen lie in the same open-plan space, and the loo lies within the office (i.e., it is an ensuite space). These can thus be accounted as: <ul> <li>the aforementioned 6 rooms</li> <li>5 rooms, namely: open-plan kitchen/living room, bedroom, bathroom, office, loo</li> <li>5 rooms, namely: kitchen, living room, bedroom, bathroom, ensuite</li> <li>4 rooms, namely: open-plan kitchen/living room, bedroom, bathroom, ensuite.</li> </ul> BLE beacons were installed in each room of the above environments. A total of three BLE configurations were considered for each room; one beacon was placed in the centre of each room (denoted as "centre" in the dataset), one by the entrance of each room (denoted "doors") and one at a location in each room chosen such that the pairwise distances between the beacons from all rooms are maximised (denoted as "far"). The smartwatch was recording IMU at 100 Hz and BLE RSSIs at 0.2 Hz and the ground truth location which the participants had to report, by tapping the appropriate room label on the watch's screen every time they were entering a new room. Each participant performed the experiment for approximately 1 hour continuously; with the sensor recording application active and the user instructed on how to record the ground truth location, the participants moved around the environment, performing activities that are commonly encountered in each room in their own style. Not everyone performed the exact same activities, and the ground truth activity labels were not recorded. A total of 11 participants, noted as user1 to user11 performed the experiment across the two environments, yielding a total of 20 recordings, noted as rec1 to rec20 in the dataset. user1 performed the experiment in both environments; three times in the demo-home (rec1,5,8) and once in the real-home (rec13,15,17,19). user11 performed the experiment only in the real-home (rec14,16,18,20) while the rest of the users performed the experiment only in the demo home, yielding one recording each. Note that recording rec13,15,17,19 were recorded simultaneously, but account for the four different assumptions on what constitutes a room in the "real home", as described before. The same holds for recordings rec14,16,18,20. The data are organised per recording and user id. Note that some users have more than one corresponding recording.

opencc-by-4.0Aug 2022View details →

dryad36/100

S-BLE: A participatory BLE sensory data set recorded from real-world bus travel events

Open the record for dataset details and reuse information.

publicAug 2023View details →

opencc-by-4.0Jul 2020View details →

Multi-slot BLE raw database for accurate positioning in mixed indoor/outdoor environments

This database presents the measurements of RSSI over BLE signals for indoor positioning applications. The experiments were done in the Physics and Mathematics building of the University of Extremadura. In the collection procedure, three different deployments were used with three smartphones for each one.  Supporting material to load and work with the database is also provided with Matlab scripts. Further information about the dataset can be found in Multi-slot BLE raw database for accurate positioning in mixed indoor/outdoor environments, Data, 2020.

opencc-by-4.0Oct 2021View details →

CoNEXT21: Mind the Gap: Multi-hop IPv6 over BLE in the IoT - Experiment Result Data

This dataset contains the all raw experiment data that was used in our paper "Mind the Gap: Multi-hop IPv6 over BLE in the IoT" published at CoNEXT21.

opencc-by-4.0Jun 2024View details →

BLE RSSI Dataset for Indoor localization

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2022View details →

Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts

A detailed description of our dataset can be found here:  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. Please cite the paper when using the dataset.