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139 results for “iot”
Overview of IoT devices and vulnerable classification at CERN
<p>The figure shows the overview of IoT devices connected to the CERN's GPN and vulnerable classification done after the manual vulnerability assessment carried on by us. </p>
SDR-based IoT Communication Systems: An Application for the DASH7 Alliance Protocol
<p>This repository contains a cabled, indoor (office environment) and an outdoor (suburban) <a href="https://www.dash7-alliance.org/" target="_blank" rel="noopener">DASH7</a> data set. All data sets are formatted as sigmf-data and sigmf-meta files which can be investigated with <a href="https://iqengine.org/" target="_blank" rel="noopener">IQEngine</a>, <a href="https://www.gnuradio.org/" target="_blank" rel="noopener">GNU Radio</a> or <a href="https://www.mathworks.com/products/matlab.html" target="_blank" rel="noopener">MATLAB</a>. The original samples were recorded as complex float 32-bit samples and have been converted to complex signed int 16-bit samples. Below you can find a more extended description of the data sets.</p> <p><strong>ds_indoor.zip:</strong></p> <ul> <li>Wireless indoor data set </li> <li>10 locations</li> <li>60 packets per location, 10 packets per file pair (SigMF)</li> <li>Fc: 866.5 MHz</li> <li>Sample rate: 7.68 MHz</li> <li>Data type: ci16_le</li> <li>Length: 8 seconds</li> <li>Channel class: Lo-Rate</li> <li>Sync word: 0x0B67</li> <li>3 Lo-Rate channel recordings per location <ul> <li>channel 0 (Fc: 863.0125 MHz), </li> <li>channel 93 (Fc: 865.3375 MHz), </li> <li>channel 186 (Fc: 867.6625 MHz)</li> </ul> </li> <li>Payload: 3 bytes [0xAB 0xCD] preceded with a counter byte (0-9)</li> </ul> <p> </p> <p><strong>ds_indoor_cabled.zip:</strong></p> <ul> <li>Cabled indoor data set</li> <li>10 recordings per channel</li> <li>10 packets per file pair (SigMF)</li> <li>Fc: 866.5 MHz</li> <li>Sample rate: 7.68 MHz</li> <li>Data type: ci16_le</li> <li>Length: 4 seconds</li> <li>Channel class: Lo-Rate</li> <li>Sync word: 0x0B67</li> <li>3 Lo-Rate channel recordings per location <ul> <li>channel 0 (Fc: 863.0125 MHz), </li> <li>channel 93 (Fc: 865.3375 MHz), </li> <li>channel 186 (Fc: 867.6625 MHz)</li> </ul> </li> <li>Payload: 3 bytes [0xAB 0xCD] preceded with a counter byte (0-9)</li> </ul> <p> </p> <p><strong>ds_outdoor.zip:</strong></p> <ul> <li>Wireless outdoor data set</li> <li>20 locations</li> <li>60 packets per location, 10 packets per file pair (SigMF)</li> <li>Fc: 866.5 MHz</li> <li>Sample rate: 7.68 MHz</li> <li>Data type: ci16_le</li> <li>Length: 8 seconds</li> <li>Channel class: Lo-Rate</li> <li>Sync word: 0x0B67</li> <li>3 Lo-Rate channel recordings per location <ul> <li>channel 0 (Fc: 863.0125 MHz), </li> <li>channel 93 (Fc: 865.3375 MHz), </li> <li>channel 186 (Fc: 867.6625 MHz)</li> </ul> </li> <li>Payload: 3 bytes [0xAB 0xCD] preceded with a counter byte (0-9)</li> </ul> <p> </p> <p><strong>flowgraphs.zip:</strong></p> <ul> <li>Contains 4 folders <ul> <li><strong>data_recording</strong> <ul> <li>Flowgraphs used to record the I/Q data</li> </ul> </li> <li><strong>data_conversion</strong> <ul> <li>Flowgraphs that convert the data to another data type and optionally decrease the length of a data file</li> </ul> </li> <li><strong>DASH7_TX</strong><br> <ul> <li>DASH7 transmitter flowgraph</li> </ul> </li> <li><strong>DASH7_RX</strong> <ul> <li>DASH7 receiver flowgraph</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>gw_logs.zip:</strong></p> <ul> <li>Contains 3 folders <ul> <li><strong>indoor, outdoor, cabled</strong> <ul> <li>Contains all the DASH7 gateway logs per measured channel.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>locations.zip:</strong></p> <ul> <li><strong>outdoor_locations</strong> <ul> <li>Contains a JSON file with the GPS coordinates of 21 locations and the RX location.</li> <li>Cointains a map with all the outdoor locations</li> </ul> </li> <li><strong>indoor_locations</strong> <ul> <li>Contains a JSON file with the X and Y coordinates of 21 locations and the RX setup location with respect to a reference point.</li> <li>Cointains a map with all the indoor locations</li> </ul> </li> </ul>
IoT Forensic Analysis: a Family of Experiments with Amazon Echo Devices (ISP diagrams and Teardown videos)
<p>The two zip files (i.e., ISP Diagrams.zip and Teardown videos.zip) contain the ISP diagrams and teardown videos of the Amazon Echo Show IoT devices used in the experiment that we report in our research paper titled "IoT Forensic Analysis: a Family of Experiments with Amazon Echo Devices."</p>
Research data from the two surveys on IoT implementation for Article "User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe"
<p>The file includes data collected through two online surveys linked to the article "User and Professional Aspects for Sustainable Computing Based on the nternet of Things in Europe" published by journal Sensors in January 2023:</p> <ul> <li>Survey on factors that inlfuence IoT Adoption by non technical users</li> <li>Survey on recommended profile focused on IoT implementation for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician.</li> </ul>
Infection safe workplace IoT sensor measurements
<p>This dataset has been collected and created by "Datu Tehnoloģiju Grupa" and Riga Technical University. This dataset contains data collected from IoT devices scattered in the office workplace. The measurements have been collected in a .csv file, with 150000 records. Project “Platform for the Covid-19 safe work environment” (ID. 1.1.1.1/21/A/011) is founded by European Regional Development Fund specific objective 1.1.1 «Improve research and innovation capacity and the ability of Latvian research institutions to attract external funding, by investing in human capital and infra-structure». The project is co-financed by REACT-EU funding for mitigating the consequences of the pan-demic crisis.</p>
NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies
<p><strong>NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies</strong></p> <p>This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper.</p> <p>Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data.</p> <p><strong>Figure 3</strong></p> <p>Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M.</p> <p>Due to the fact that the duration of <em>Idle Connected</em> in the measurement scripts was 30 seconds and 60 seconds for <em>Idle Not Connected</em>, the D-value and the mean power consumption are divided by 2.</p> <ul> <li>Data – energy_measurements_fig3.csv</li> <li>Code – fig3.R</li> </ul> <p><strong>Figure 4</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP.</p> <p>The delay between the measurements for Figure 4 were all 30 seconds long, but the identified <em>Standby</em> and <em>Idle</em> phases have different lengths. Therefore, the <em>Idle</em> phase values for both access technologies have been normalized and calculated for 20 seconds each.</p> <ul> <li>Data – energy_measurements_fig4.csv</li> <li>Code – fig4.R</li> </ul> <p><strong>Figure 5</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT.</p> <p>In this scenario the delay between the measurements were different again. For <em>MQTT</em> the delay was 150 seconds and for <em>HTTP</em> 30 seconds. Therefore, the data during the <em>Idle</em> and <em>Standby</em> (only for <em>MQTT</em>) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the <em>MQTT</em> <em>Idle</em> phase measurements, the device disconnects. This is not taken into account for the evaluation, which is why these energy values are discarded for this figure.</p> <ul> <li>Data – energy_measurements_fig5.csv</li> <li>Code – fig5.R</li> </ul> <p><strong>Contact</strong></p> <p>For questions or issues with this code, please contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de) or any of the authors of the related publication.</p>
Database of RF fingerprinting on use case IoT devices
<p>This document is a dataset of radiofrequency signals. It is composed of 1000 signals coming emitted by 10 different devices. This dataset was developped for benchmarking machine learning methods on an Internet of Things classification task: recognizing which device emitted a signal.</p> <p>This dataset is in an adaptation of the dataset collected by Basak et al. in “Drone classification from RF fingerprints using deep residual nets” (IEEE COMSNETS conference, 2021).</p> <p>Basak et al. collected signals from six commercial drones, three drone radio-controllers and one WiFi router. The conducted the measurements in an anechoic chamber, using a universal software radio peripheral (USRP X310) placed seven meters apart from the devices . The signals were all in the 2.4 GHz ISM band and the whole 100 MHz band was received instantaneously using a receiving sampling rate of 100 MSps (i.e. the system down-converted the signal frequencies to the 0-100 MHz band to sample them correctly).</p> <p>While the original dataset by Basak et al. consisted in spectrograms of 256 frequency bins by 256 time frames, we have converted in into averaged spectra of 256 frequency bins. Furthermore, while Basak et al. have considered several noise levels, here we only consider the lowest noise level available (-60 dBm).</p> <p>The database is stored in an h5 file, a format adapted to databases. Inside the file there are two datasets: the signals (‘Signals’) and the targets (‘Targets’). The targets correspond to the ten different classes of signals: Parrot Disco (0), Q205 (1), Tello (2), MultiTx (3), Nine Eagles (4), Spektrum DX4e (5), Spectrum DX6i (6), Wltoys (7), S500 (8) and Linkys router (9).</p> <p>This dataset corresponds to the Deliverable D6.2 of the RadioSpin EU funded project.</p>
CEP-based Activity Detection Services generated from IoT Data
<p>Data set accompanying the paper "Data-driven Generation of Services for IoT-based Online Activity Detection" submitted to the International Conference on Service-Oriented Computing (ICSOC) 2023.</p> <p>The data set has 6 automatically generated activity detection services (*.siddhi files) for 6 different types of activities executed by 6 different production stations in a smart factory. The *.siddhi files have to be deployed to and activated on an instance of the <a href="http://siddhi.io">Siddhi</a> complex event processing platform.</p> <p>The data set includes the corresponding low-level IoT data for each activity (<strong>activity signature</strong>) that was used to generate the activity detection service (*.txt files). It also includes a visual representation of the activity signature for each type of activity (*.png files). To test the activity detection services, the *.txt files have to be read line by line, each line has to be send as one MQTT message to an MQTT broker and topic that the activity detection service is also listening to (standard: localhost).</p>
A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements
<p>Mobile networks have become highly complex systems. In order to better understand how network features affect performance and suggest additional improvements, it is crucial to examine them from an empirical perspective. In the following, we present a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT) and 5G New Radio (NR) connectivity. We collected our dataset during a period of seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization tasks.</p> <p><br>If you use our dataset in your research, we kindly request that you cite the following paper:</p> <p>K. Kousias <em>et al</em>., "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," in <em>IEEE Communications Magazine</em>, vol. 62, no. 5, pp. 44-49, May 2024, doi: 10.1109/MCOM.011.2200707.</p>
ASSIST-IoT Multimodal Fall Detection Dataset
<p>Multimodal dataset for fall detection. Includes acceleration data collected from a tag and two smartwatches, and location reported by the tag. More details about the data collection procedure can be found in <code>notes.md</code>.</p> <p><strong>Contents</strong></p> <p>The repository contains:</p> <ul> <li><code>data/location_data.csv</code> and <code>data/full_acceleration</code> – preprocessed acceleration and location data from 10 participants and mannequin simulated falls with target variable identified</li> <li><code>data/subsampled_acceleration_data.csv</code> – subsampled acceleration dataset used for training the AI model</li> <li><code>notes.md</code> – description of activities performed and notes from data collection</li> <li><code>videos</code> – reference videos for performed activities</li> </ul> <p><strong>Authors</strong></p> <ul> <li><a href="https://orcid.org/0000-0002-2543-9461">Piotr Sowiński</a> – research methodology, data collection and processing</li> <li><a href="https://orcid.org/0000-0003-3217-1050">Monika Kobus</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0003-4295-3005">Anna Dąbrowska</a> – research methodology, methodological supervision</li> <li><a href="https://orcid.org/0000-0003-1524-7877">Kajetan Rachwał</a> – data collection</li> <li><a href="https://orcid.org/0000-0002-7109-891X">Karolina Bogacka</a> – research methodology</li> <li><a href="https://orcid.org/0000-0002-9572-2705">Krzysztof Baszczyński</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0002-3080-0303">Anastasiya Danilenka</a> – research methodology, data collection and processing</li> </ul> <p><strong>Acknowledgements</strong></p> <p>This work is part of the <a href="https://assist-iot.eu/">ASSIST-IoT project</a> that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258.</p> <p>The <a href="https://www.ciop.pl/en">Central Institute for Labour Protection – National Research Institute</a> provided facilities and equipment for data collection.</p> <p><strong>License</strong></p> <p>The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
IoT device identification - Multi user data (No fading)
<p>Artificial multi user observations without added fading generated as described in the associated paper Section III.</p> <p>Frequency: 863-870 MHz (center 866,5 MHz)</p> <p>Sample Frequency: 10 MSPS</p> <p>Date of measurement: 15 November 2018</p> <p>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</p>
IoT device identification - Other room
<p>Other room measurements of the 868 MHz ISM band. The transmitter device is placed in an adjacent room to the receiver. Both transmitter and receiver are on the same floor.</p> <p>Frequency: 863-870 MHz (center 866,5 MHz)</p> <p>Sample Frequency: 10 MSPS</p> <p>Date of measurement: 15 November 2018</p> <p>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</p>
IoT device identification - Upstairs
<p>Upstairs measurements of the 868 MHz ISM band. The transmitter devices are placed in a room a floor above the measurement setup.</p> <p>Frequency: 863-870 MHz (center 866,5 MHz)</p> <p>Sample Frequency: 10 MSPS</p> <p>Date of measurement: 15 November 2018</p> <p>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</p>
IoT device identification dataset
<p>This is the main record in the IoT device measurements connected to the paper "Identification of IoT Devices using Experimental Radio Spectrum Dataset and Deep Learning".</p> <p>This is a connecting dataset that references all the actual data. The records are split because of the large size of the dataset. The records containing the data are found at the following DOIs:</p> <ul> <li>Raw data <ul> <li>Other room: <a href="https://doi.org/10.5281/zenodo.3646427">10.5281/zenodo.3646427</a></li> <li>Upstairs: <a href="https://doi.org/10.5281/zenodo.3641580">10.5281/zenodo.3641580</a></li> <li>Same room: <a href="https://10.5281/zenodo.3638163">10.5281/zenodo.3638163</a></li> <li>Background measurement: <a href="https://doi.org/10.5281/zenodo.3638139">10.5281/zenodo.3638139</a></li> </ul> </li> <li>Multi user data (No fading): <a href="https://doi.org/10.5281/zenodo.3754210">10.5281/zenodo.3754210</a></li> <li>Multi user data: <a href="https://doi.org/10.5281/zenodo.3753003">10.5281/zenodo.3753003</a></li> <li>Cut and dimensionality reduced measurements: <a href="https://doi.org/10.5281/zenodo.3752981">10.5281/zenodo.3752981</a></li> </ul> <p> </p> <p>Common parameters for all the measurements:</p> <ul> <li>Frequency: 863-870 MHz (center 866,5 MHz)</li> <li>Sample Frequency: 10 MSPS</li> <li>Date of measurement: 15 November 2018</li> <li>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</li> </ul>
Dataset for: Trust Assessment in 32 KiB of RAM: Multi-application Trust-based Task Offloading for Resource-constrained IoT Nodes
<p>The dataset used to generate graphs for: Matthew Bradbury, Arshad Jhumka and Tim Watson. Trust Assessment in 32 KiB of RAM: Multi-application Trust-based Task Offloading for Resource-constrained IoT Nodes. Proceedings of the Symposium on Applied Computing, ACM, 2021, 1-10.</p> <p>Also includes instructions for experiment setup.</p> <p>Scripts from https://github.com/MBradbury/iot-trust-task-alloc are required to analyse and graph these results.</p>
Securing the IoT through Moving Target Defense (short film)
<p>Short film by Renzo E. Navas, presented in the Festival de sciences en cour[t]s 2019 (scientific vulgarisation)</p> <p><a href="https://www.youtube.com/channel/UCUY4_X-KClXAxmdbg_VCfDA">Festival Sciences en Courts (Youtube)</a> -- <a href="http://sciences-en-courts.fr/">http://sciences-en-courts.fr/</a></p> <p>** Mention spéciale du jury **</p> <p>Teaser:</p> <p>Une lampe... connecté? OUI. Parce que l'Internet des Objets (IdO) est arrivé. Mais l'IdO n'est pas limité à la maison. Il est partout. L'IdO ouvre la possibilité à nouvelles façons de faire interagir le monde physique et numérique. Malheureusement, les cyber-attaques ne sont pas exclus de ces nouvelles interactions. Je travaille pour faire la vie des cyber-attaquants plus difficile, et par conséquence cela des nos objets connectés (... et la notre) plus tranquille. Mon travail est basé sur le paradigme "Moving Target Defense" (MTD) [En français: Défense du Cible en Mouvement], qui propose faire que certaines propriétés de nos systèmes soient en changement perpétuel. Avec le MTD, la réussite des attaquants -assuré auparavant- sera en échec. Par : Renzo Navas</p>
Measurement data of the industrial IoT scenario
<p>In these measurements considered in this dataset, 15 measurement points are deployed in the scenario. They are sorted in two groups. Among line 1, all measurement points are LoS scenarios. Among line 2, measurement points 1, 2, 3, 4, 5,and 7 are LoS scenarios, and measurement points 6, 8, 9 are NLoS scenarios. Due to the limitation of the cable length, measurement data of line 2 locations 1-6 is collected at mmwave band. The heights of the Tx antenna and the Rx antenna are 2.05 m and 1.45 m, respectively. The bandwidth of the intermediate frequency filter of the adopted VNA is 2 kHz. Both the Tx and the Rx antennas are omnidirectional antennas. 200 snapshots are collect at each Rx location. In terms of the 3-4 GHz data, the bandwidth is 1 GHz. The number of frequency points swept in each snapshot is 501. In the aspect of 38-39 GHz and 39-40 GHz data, the center frequencies are 38.5 GHz and 39.5 GHz with 1 GHz bandwidth, respectively. The number of frequency points swept in each snapshot is also 501.</p>
Dataset: Computational resources in the development of e-health IoT applications: A systematic mapping study
<p>Files related to systematic mapping titled Computational resources in the development of e-health IoT applications: A systematic mapping study</p>
Optimal IoT Sensor Deployment in the WUI: A Comparative Analysis of Strategies
<p>Included here are individual burn maps used for evaluating algorithm results in the paper: Optimal IoT Sensor Deployment in the WUI: A Comparative Analysis of Strategies. This paper was accepted for presentation at IEEE HONET 2024, the 21st IEEE International Conference on Smart Communities. (For financial/travel reasons the paper has since been withdrawn)</p> <p>Also included are maps of fuel load and elevation (geotifs) and the daily weather (in .csv format) for the region of interest used in the burn probability simulator Burn-P3+ to generate the individual burn maps.</p> <p>This paper investigates optimized IoT sensor deployment strategies within the Wildland-Urban Interface (WUI), a key component of smart communities. It presents a comparative analysis of a novel dynamic grid approach against traditional random and greedy algorithms. By employing the Burn-P3+ simulator, detailed burn probability maps are generated for two Canadian geographically distinct areas: the Halifax Regional Municipality and Kelowna. Our analysis reveals that the dynamic grid method significantly enhances fire detection capabilities by strategically distributing IoT sensors in alignment with calculated burn probability. This approach shows a marked improvement of as much as 35% in burn detection over traditional deployment methods. The paper highlights the advantages of structured IoT sensor placement in supporting smart communities, offering more efficient and effective wildfire management strategies in the WUI through real-time data available to both fire mitigation teams and AI.</p> <p>Partial code for the sensor deployment algorithms discussed in the above mentioned paper is <a href="https://github.com/richardjpurcell/sensor-deployment-algorithms">available on GitHub</a>.</p>
Outdoor NB-IoT and 5G coverage and channel information data in urban environments
<p>This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).</p> <p>Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: <a href="https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements">https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements</a> </p> <p>The dataset includes a metadata file providing the following information for each campaign: </p> <ul> <li>date of collection;</li> <li>start time and end time of collection;</li> <li>length;</li> <li>type (walking/driving).</li> </ul> <p>Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.</p> <p>The dataset contains the following data for NB-IoT:</p> <ul> <li>Raw data for each campaign, stored in two .csv files. For a generic campaign <X>, the files are: <ul> <li>NB-IoT_coverage_C<X>.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);</li> <li> NB-IoT_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <NPCI, Operator ID, eNodeB-ID> unique combination detected at the coordinates of the data point.</li> <li>Estimated positions of eNodeBs, stored in a csv file for each city;</li> <li>A matlab script and a function to extract and generate processed data from the raw data for each city.</li> </ul> <p>The dataset contains the following data for 5G:</p> <ul> <li>Raw data for each campaign, stored in two .xslx files. For a generic campaign <X>, the files are: <ul> <li>5G_coverage_C<X>.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);</li> <li> 5G_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <PCI, Beamforming Index, Operator ID> unique combination detected at the coordinates of the data point.</li> <li>A matlab script and a supporting function to extract and generate processed data from the raw data.</li> </ul> <p>In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:</p> <ul> <li>A campaign-by-campaign linear interpolation (both NB-IoT and 5G);</li> <li>A bidimensional interpolation on all campaigns combined (NB-IoT only).</li> </ul> <p>A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in:</p> <p>L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. <span>DOI: </span><a href="https://doi.org/10.3390/s23094266" target="_blank" rel="noopener"><span>10.3390/s23094266</span></a>.</p> <p>The second interpolation approach is instead introduced and described in:</p> <p>L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.</p> <p>Positioning using the 5G data was furthermore in investigated in: </p> <p>K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," <span>IEEE Communications Magazine, Volume 62, Issue 5, pp</span><span>. 44-49, May</span><span> 202</span><span>4</span><span>. DOI: </span><a href="https://doi.org/10.1109/MCOM.011.2200707" target="_blank" rel="noopener"><span>10.1109/MCOM.011.2200707</span></a><span>.</span></p> <p><span>G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: <a href="https://doi.org/10.1109/OJCOMS.2024.3499370" target="_blank" rel="noopener"><span>10.1109/OJCOMS.2024.3499370</span></a>.</span></p> <p>Please refer to the above publications when using and citing the dataset. </p>
ScienceDex guides
Understand access before you commit
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.