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18 results for “RSSI”
LoRa RSSI vs distance
<p>The data set has been used in CityScan experiment within <a href="https://www.fed4fire.eu/">Fed4Fire+</a> continuous call. RSSI and packet loss between LoRa end device sending data packets and a set of stationary LoRa nodes were studied depending on the distance and other parameters.</p> <p>The data set contains location information transferred over LoRa from a single tag and registered by 10 nodes from <a href="https://doc.lab.cityofthings.eu/wiki/Nodes">CityLab testbed</a>:<a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node2"> Node 2</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node4">Node 4</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node15">Node 15</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node18">Node 18</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node20">Node 20</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node21">Node 21</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node27">Node 27</a>, <a href="https://doc.lab.cityofthings.eu/nodemap/?focus=node33">Node 33</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>.</p> <p>The data is stored in CSV format (with the following columns):<br> <strong>node</strong> - id of the CityLab testbed node<br> (<strong>node_lat</strong>, <strong>node_lon</strong>) - GPS coordinates of the node<br> <strong>tag</strong> - id of the CityScan tag<br> (<strong>tag_lat</strong>, <strong>tag_lon</strong>) - GPS coordinates of the tag<br> <strong>tag_speed</strong> - Speed of the moving tag<br> <strong>dist_m</strong> - Distance between corresponding tag and node (meters)<br> <strong>ts</strong> - timestamp (milliseconds since the UNIX epoch: 1 January 1970)<br> <strong>num</strong> - a sequential number of submitted packet<br> <strong>rssi</strong> - received signal strength indication<br> <strong>received</strong> - Boolean attribute indicating whether or not the data packet was received</p>
A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning</strong>".</p> <p>Preprint:<a href="https://arxiv.org/abs/1908.06851"> https://arxiv.org/abs/1908.06851</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8911792">https://ieeexplore.ieee.org/document/8911792</a></p> <p> </p> <p>The dataset used to create these sets was published in:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The full dataset is available here:</p> <pre><a href="https://doi.org/10.5281/zenodo.1212478">https://doi.org/10.5281/zenodo.1212478</a> </pre> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen; and Weyn, Maarten.</p> <p> </p>
A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN</strong>".</p> <p>Preprint: <a href="https://arxiv.org/abs/1908.05085">https://arxiv.org/abs/1908.05085</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8970177">https://ieeexplore.ieee.org/document/8970177</a></p> <p> </p> <p>The dataset used to create these sets was published in:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The full dataset is available here:</p> <pre><a href="https://doi.org/10.5281/zenodo.1212478">https://doi.org/10.5281/zenodo.1212478</a> </pre> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen and Weyn, Maarten.</p>
BLE RSSI vs distance
<p>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.</p> <p>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></p> <p>The data is stored in CSV format (with the following columns):<br> <strong>node</strong> - id of the CityLab testbed node<br> (<strong>node_lat</strong>, <strong>node_lon</strong>) - GPS coordinates of the node<br> (<strong>tag_lat</strong>, <strong>tag_lon</strong>) - GPS coordinates of the tag<br> <strong>ts</strong> - timestamp (milliseconds since the UNIX epoch: 1 January 1970)<br> <strong>rssi -</strong> received signal strength indication</p>
SAL-Autarkic-Localization-RSSI-BLE-Dataset: SAL-RB-Dataset
<p>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)</p>
BLE RSSI Database for Analyzing Routines of Community-Dwelling Older Adults
<p>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.</p> <p> </p> <p>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," <em>2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)</em>, Valencia, Spain, 2022, pp. 50-55, doi: 10.1109/ICUMT57764.2022.9943410.</p>
RSSI-based mobile robot localization datasets
<p>Experimental datasets used for an MSc project, titled "Outdoor Localization System for Mobile Robots Based on Radio-Frequency Signal Strength", for performing trajectory recovery of a mobile ground robot by data fusion of odometry, gyroscope and Received Signal Strength Indicator (RSSI), through Extended and Augmented-Extended Kalman Filter algorithms.</p> <p><br> "experiment_1" contains the robot's data in "rosbags" (i.e. ROS' compressed robot data), recorded in a parking lot with ground-truth from an RTK GPS. The measurements recorded were wheel odometry, IMU accelerations/angular velocities, RSSI from three receiver-transmitter pairs, regular GPS position and RTK GPS position.</p> <p>"experiment_2" contains the robot's data in "rosbags", recorded in the parking lot and in a second environment, a garden with tall trees and a nearby building. Both environments were recorded for later comparison, to see how the localization solution performed in a GPS-denied environment. All measurements were the same as in experiment 1, except for the exclusion of the common and RTK GPS.</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>
RSSI Traces from the WSN-Testbed at the I4, Friedrich-Alexander University Erlangen-Nuremberg
<p>This is a one week RSSI-trace from an office-based WSN-Testbed at the I4, Friedrich-Alexander University Erlangen-Nuremberg. 9 Tmote Sky node were used to sample the RSSI at approximately 8192 Hz.</p> <p>The data was collected using base_rssi2.c, which is part of the code provided with https://doi.org/10.5281/zenodo.582277. It can be decoded using the code in /tools/scala in the the same repo.</p> <p>The trace was taken in the week of 2015-05-06.</p>
Measurement Results of Single-Bit Sigma-Delta Modulator and Simulation Data for DFT Based RSSI Calculation
<p>Measurement of single-bit output data stream of an low-IF receiver with sigma-delta ADC. Inphase and Quadraturephase signals can be found in raw_rx_*.csv files. Simulation results of DFT based RSSI calculation are found in sim_RSSI_*.csv. Measurement results of the RSSI dynamic range are located in meas_RSSI_dynamicRange.csv.</p>
Wi-Fi (CSI and RSSI) data of six basic knife activities for cooking (chopping, cubing, French cutting, julienning, mincing, and slicing)
<p>To gather the dataset, we asked two participants to perform six basic knife activities. The layout of the system experiment is provided in Fig. 4. As it illustrates, we put the receiver on the right side and the ESP32 transceiver on the left side of the performing area. The performing area is a cutting board (30 x 46 cm) in this experiment. Each participant performs each activity five times in the performing area. The data is recorded using a customized version of ESP32-CSI-tool [38] on the laptop that helps us to record and save each data in a separate file. After recording all 60 data entries, we used Python code to extract the clean data from all generated text by the tool. The clean data is stored in a database and creates the dataset.</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>
A dataset for RSSI based outdoor localization using LoRaWAN in a harbor as a harsh and industrial environment
<p>Enabling precise device localization is a critical requirement for the future of industry. Leveraging signal features for location determination has emerged as a leading approach and good alternative for Global Navigation Satellite Systems (GNSS) because of their limitations (low accuracy for indoor environments, expensive chips, and high energy consumption). On this basis, to provide localization for IoT in an industry with a harsh environment, the adopted wireless networks should have a long range coverage area. LoRaWAN is one of the most common communication networks that can provide large coverage with low power consumption and low implementation cost. Between various signal features that can be used for localization, Received Signal Strength (RSS) received more attention because of their low-cost deployment. But, RSS is highly dependent and sensitive to environmental changes, such as temperature, humidity, and background noise. This sensitivity becomes more intensive in an industrial environment with a harsh and dynamic environment. In order to evaluate the environmental effects on RSS in the harsh and highly dynamic industry, we present a comprehensive repository of LoRaWAN Received Signal Strength Indicator (RSSI) measurements, collected in a harbor as a testbed featuring three LoRaWAN gateways and one mobile end node. During the data collecting process, the mobile device obtains its location via a GPS and transmits it as the LoRaWAN message. In addition, to provide more insight of the effect of dynamic environment on the RSSI, two end nodes are implemented in fixed locations. These end nodes transmit messages with fixed time intervals including their unique id. The collected dataset includes RSSI and SNR measurements recorded by multiple gateways for each transmitted packet by fixed or mobile end nodes, and timestamp. This dataset enables the development and evaluation of RSSI-based localization and allows researchers to explore the challenges and opportunities associated with localization in dynamic IoT deployments.</p>
SNR and RSSI from an urban LoRa Network
<p>The dataset contains various SNR and RSSI reading from a LoRa Network in the city of Portland, Maine. The dataset consists in a single sheet file, where each row corresponds to a specific LoRa node. Note that: i) the first row corresponds to the LoRa Gateway; ii) each row contains information over the node ID, its coordinates, the packet frequency, bandwidth, , Transmission Power, Spreading Factor, Coding Rate, SNR and RSSI.</p>
ESP32C3 WiFi FTM RSSI Indoor Localization
<div> <div> <div> <h2><strong>Wi-Fi FTM RSSI Localization dataset</strong></h2> <p>Wi-Fi Fine Time Measurement for positioning / Indoor Localization in <strong>3 different locations</strong> and using <strong>8 different APs</strong> <br> <br>Custom APs using <strong>ESP32C3</strong> and Raw FTM is measured in nanoseconds <br> <br>Data is only measured at the Router Side <br> <br>Data is not measured at client side <br> <br>Has 4 datasets inside the zip folder with over <strong>100,000 data points</strong> <br> <br>Contains processed Wi-Fi FTM packets from various routers in: <br>1. University of Victoria, Engineering Office Wing (EOW) 3rd Floor <br>2. University of Victoria, Engineering Office Wing (EOW) 5th Floor <br>3. University of Victoria, Engineering and Computer Science (ECS) 1st Floor <br> <br>Each folder contains a training dataset and a testing dataset that is independent in time and space <br> <br>Router Time is synchronized using chrony</p> </div> </div> </div> <div> <div>Instructions: </div> <div> <div> <h2><strong>Dataset is in CSV format</strong></h2> <p>Relative Time (seconds) | X Position (meters) | Y Position (meters) | Feature 1 | Feature 2 | Feature 3 ..... <br> <br>Time resets at every new position and position accuracy is a few centimeters using LIDAR and RGBD camera <br> <br>Map is in ROS2 PGM format that can read by ROS2 programs <br> <br>Data for the paper <br> <br>Wi-Fi and Bluetooth Contact Tracing Without User Intervention</p> <p><br><a href="https://ieeexplore.ieee.org/document/9866766" rel="nofollow">https://ieeexplore.ieee.org/document/9866766</a></p> <p>Please Cite As</p> <pre><code>@article{yuen2022wi, title={Wi-Fi and Bluetooth contact tracing without user intervention}, author={Yuen, Brosnan and Bie, Yifeng and Cairns, Duncan and Harper, Geoffrey and Xu, Jason and Chang, Charles and Dong, Xiaodai and Lu, Tao}, journal={IEEE Access}, volume={10}, pages={91027--91044}, year={2022}, publisher={IEEE} }</code></pre> </div> </div> </div>
BLE RSSI Dataset for Indoor localization
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WiFi and Bluetooth RSSI SQI Indoor Localization
<h2><strong>Wi-Fi BLE RSSI SQI Localization dataset</strong></h2> <p>Wi-Fi BLE RSSI for positioning / Indoor Localization in <strong>4 different locations</strong> and using <strong>18 different APs</strong></p> <p>Data is only measured at the <strong>Router Side</strong></p> <p>Data is not measured at client side</p> <p>Has <strong>12 datasets</strong> inside the zip folder with over <strong>1,000,000 data points</strong></p> <p>Contains processed Wi-Fi and BLE packets from various routers in: <br>1. University of Victoria, Engineering Office Wing (EOW) <br>2. University of Victoria, Engineering Lab Wing (ELW) <br>3. University of Victoria, Engineering and Computer Science (ECS) <br> <br>Each folder contains a training dataset and a testing dataset that is independent in time and space</p> <p>Router Time is synchronized using chrony</p> <p> </p> <h2><strong>Dataset is in CSV format</strong></h2> <p>Relative Time (seconds) | X Position (meters) | Y Position (meters) | Feature 1 | Feature 2 | Feature 3 ..... <br> <br>Time resets at every new position and position accuracy is a few centimeters using LIDAR and RGBD camera <br> <br> <br>Map is in ROS2 PGM format that can read by ROS2 programs <br> <br> <br>Data for the paper <br> <br>Wi-Fi and Bluetooth Contact Tracing Without User Intervention</p> <p><br><a href="https://ieeexplore.ieee.org/document/9866766" rel="nofollow">https://ieeexplore.ieee.org/document/9866766</a></p> <p> </p> <p>Please Cite As</p> <blockquote> <pre>@article{yuen2022wi, title={Wi-Fi and Bluetooth contact tracing without user intervention}, author={Yuen, Brosnan and Bie, Yifeng and Cairns, Duncan and Harper, Geoffrey and Xu, Jason and Chang, Charles and Dong, Xiaodai and Lu, Tao}, journal={IEEE Access}, volume={10}, pages={91027--91044}, year={2022}, publisher={IEEE} }</pre> </blockquote>
5G RSSI Dataset for Estimating Number of Drones in Scene
<p>This dataset provides high-grade Received Signal Strength Indicator (RSSI) data collected from a set of experiments meant to estimate the number of drones present in a closed indoor space. The experiments are conducted varying the number of drones from one to seven, where all the variations in RSSI signal data are captured using a 5G transceiver setup established using Ettus E312 software-defined radio. There are seven files in the database, with a minimum of about 270 million samples.</p>
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