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139 results for “iot”
An IoT-Enriched Event Log for Process Mining in Smart Factories
<p><strong>DEPRECATED - current version: </strong><a href="https://figshare.com/articles/dataset/Dataset_An_IoT-Enriched_Event_Log_for_Process_Mining_in_Smart_Factories/20130794">https://figshare.com/articles/dataset/Dataset_An_IoT-Enriched_Event_Log_for_Process_Mining_in_Smart_Factories/20130794</a></p> <p> </p> <p>Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various domains, including Business Process Management (BPM) research. One main research area in BPM is process mining, which can be used to analyze event logs, e.g., for checking the conformance of running processes. However, there are only a few IoT-based event logs available for research purposes. Some of them are artificially generated, and the problem occurs that they do not always completely reflect the actual physical properties of smart environments. In this paper, we present an IoT-enriched XES event log that is generated by a physical smart factory. For this purpose, we created the DataStream XES extension for representing IoT-data in event logs. Finally, we present some preliminary analysis and properties of the log.</p>
Massive IoT for Large-Scale Public Events in the 5GENESIS Surrey Platform
<p>This dataset contains the results of the trials conducted within the context of the main IoT use case of the 5GENESIS Surrey Platform.</p>
Librecast: IoT Software Updates over IPv6 Multicast Archive of all experimental data
<p>The librecast project states that ``Multicast is, by definition, the most efficient way for multiple nodes to communicate''. This experiment is designed to provide evidence of this efficiency by comparing multicast and unicast methods of sending the same data to a large number of nodes, as would for example happen when a software update is released.<br> <br> The data set covers the experimental runs on the Virtual Wall 1 at IMEC as part of the Fed4Fire+ "SME and NGI Cascaded Experiments" https://www.fed4fire.eu/demo-stories/cc/librecasttesting/</p> <p>This directory contains raw experiment results as produced by the "run-experiment" script. File names containing ".test." are experiment runs using code changes which we decided not to keep, and are excluded from processing and summarising. File names containing ".partial." are experiment runs which were interrupted for some reason (usually when some nodes in a testbed stopped responding, and we could not get a complete set). These are also excluded from processing and summarising.</p> <p>Summaries:</p> <p>Results collated by testbed:</p> <table> <tbody> <tr> <th>Testbed</th> <th>Booted</th> <th>Clients</th> <th>Routers</th> <th>Runs</th> </tr> <tr> <td>S1L20B</td> <td>2022-01-19 10:15:08 UTC</td> <td>20</td> <td>0</td> <td>1</td> </tr> <tr> <td>S1L20C</td> <td>2022-01-19 10:15:08 UTC</td> <td>20</td> <td>0</td> <td>30</td> </tr> <tr> <td>S1L40</td> <td>2022-02-16 12:17:41 UTC</td> <td>40</td> <td>0</td> <td>6</td> </tr> <tr> <td>S1L48A</td> <td>2022-02-18 21:27:33 UTC</td> <td>48</td> <td>0</td> <td>6</td> </tr> <tr> <td>S1L49F</td> <td>2022-02-25 19:03:03 UTC</td> <td>49</td> <td>0</td> <td>11</td> </tr> <tr> <td>S1L50</td> <td>2022-02-17 21:02:31 UTC</td> <td>50</td> <td>0</td> <td>2</td> </tr> <tr> <td>S1L51G</td> <td>2022-02-28 19:23:06 UTC</td> <td>51</td> <td>0</td> <td>14</td> </tr> <tr> <td>S1R1L19C</td> <td>2022-01-28 08:41:43 UTC</td> <td>19</td> <td>1</td> <td>1</td> </tr> <tr> <td>S1R1L19D</td> <td>2022-01-28 08:41:43 UTC</td> <td>19</td> <td>1</td> <td>8</td> </tr> <tr> <td>S1R1L20A</td> <td>2022-02-11 17:14:31 UTC</td> <td>20</td> <td>1</td> <td>13</td> </tr> <tr> <td>S1R3L10H</td> <td>2022-03-09 20:46:54 UTC</td> <td>40</td> <td>7</td> <td>4</td> </tr> <tr> <td>S1R3L5B</td> <td>2022-01-24 19:09:44 UTC</td> <td>20</td> <td>7</td> <td>1</td> </tr> <tr> <td>S1R3L5C</td> <td>2022-01-24 19:09:44 UTC</td> <td>20</td> <td>7</td> <td>7</td> </tr> </tbody> </table> <p> </p> <p>Results collated by number of clients:</p> <table> <tbody> <tr> <th>Collection</th> <th>Clients</th> <th>Runs</th> </tr> <tr> <td>Multiple LANs</td> <td>19</td> <td>9</td> </tr> <tr> <td>Two LANs</td> <td>19</td> <td>9</td> </tr> <tr> <td>All testbeds</td> <td>19</td> <td>9</td> </tr> <tr> <td>Single LAN</td> <td>20</td> <td>31</td> </tr> <tr> <td>Multiple LANs</td> <td>20</td> <td>21</td> </tr> <tr> <td>Two LANs</td> <td>20</td> <td>13</td> </tr> <tr> <td>Multiple LANs</td> <td>20</td> <td>8</td> </tr> <tr> <td>All testbeds</td> <td>20</td> <td>52</td> </tr> <tr> <td>Single LAN</td> <td>40</td> <td>6</td> </tr> <tr> <td>Multiple LANs</td> <td>40</td> <td>4</td> </tr> <tr> <td>Multiple LANs</td> <td>40</td> <td>4</td> </tr> <tr> <td>All testbeds</td> <td>40</td> <td>10</td> </tr> <tr> <td>Single LAN</td> <td>48</td> <td>6</td> </tr> <tr> <td>All testbeds</td> <td>48</td> <td>6</td> </tr> <tr> <td>Single LAN</td> <td>49</td> <td>11</td> </tr> <tr> <td>All testbeds</td> <td>49</td> <td>11</td> </tr> <tr> <td>Single LAN</td> <td>50</td> <td>2</td> </tr> <tr> <td>All testbeds</td> <td>50</td> <td>2</td> </tr> <tr> <td>Single LAN</td> <td>51</td> <td>14</td> </tr> <tr> <td>All testbeds</td> <td>51</td> <td>14</td> </tr> </tbody> </table> <p> </p> <p>All results together:</p> <table> <tbody> <tr> <th>File size</th> <th>Runs</th> </tr> <tr> <td>32</td> <td>104</td> </tr> <tr> <td>128</td> <td>104</td> </tr> <tr> <td>512</td> <td>104</td> </tr> <tr> <td>2048</td> <td>104</td> </tr> <tr> <td>All</td> <td>416</td> </tr> </tbody> </table> <p>All results together, immediate, size 2048:</p> <table> <tbody> <tr> <th>Update</th> <th>Runs</th> </tr> <tr> <td>multicast</td> <td>104</td> </tr> <tr> <td>scp</td> <td>104</td> </tr> <tr> <td>tcp</td> <td>104</td> </tr> <tr> <td>udp</td> <td>104</td> </tr> </tbody> </table> <p>Router results for selected multicast runs and routers</p> <table> <tbody> <tr> <th>Testbed</th> <th>Run</th> </tr> <tr> <td>S1R3L10H</td> <td>20220309222056</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310074238</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310172944</td> </tr> <tr> <td>S1R3L10H</td> <td>20220311033431</td> </tr> <tr> <td>S1R3L5B</td> <td>20220128192622</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129114604</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129215606</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130060831</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130144549</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130224025</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131063956</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131164414</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310001954</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310093547</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310193037</td> </tr> <tr> <td>S1R3L10H</td> <td>20220311052100</td> </tr> <tr> <td>S1R3L5B</td> <td>20220128210828</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129132315</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129234328</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130075126</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130162044</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131002021</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131083908</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131181549</td> </tr> </tbody> </table> <p> </p> <p> </p>
VICINITY IoT Use Case Data Sets
<p>This repository contains Internet-of-Things (IoT) use case data sets from the VICINITY project. The use cases are described under https://vicinity2020.eu, where also the contributing companies are described in more detail. </p> <p>The use cases include: </p> <ul> <li>eHealth use case data by GNOMON/CERTH (anonymized)</li> <li>Smart parking, building control use case data by HITS, TINYM</li> <li>Energy management data set by ENERC</li> <li>Several other, smaller use cases with less data from open call winners. </li> </ul> <p>The data sets have also been the basis for the publications available for download under the same URL; the folder AAU gives additionally the data from AAU's publications. </p> <p> </p>
Orthophoto & DEM (MNE) issues d'images drone, UAV, Iot, Madagascar - 20230502 - 02_1
"Ce jeu de données présente les résultats des traitements photogrammétriques d'images de drone DJI Mavic 2 Pro UAV acquises sur le site de Iot, Madagascar à la date suivante : 20230502. <br>Les vols ont été réalisés en partenariat avec l'IH.SM dans le but de créer des modèles numériques d'élévations pour cartographier l'écosystème marin. <br> <br><br>Le paramétrage du logiciel OpenDroneMap est partagé pour permettre la reproductibilité ou l'amélioration des traitements proposés:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>Le dépôt est composé des éléments suivants:</b> <br> - 00_: Planche d'aperçu des images <br> - DCIM.zip: Images brutes issues du drone <br> - GPS.zip: Geopackage contenant l'emprise du survol ainsi que la géolocalisation des images accompagnées de leurs miniatures dans la table d'attribut en base64 <br> - METADATA.zip: Métadonnées au format ISO19115, Rapports avec miniatures des images de drone (dossier tb) et statistiques de vols. <br> - PROCESSED_DATA.zip: Orthophoto, DEM, nuages de points, ... <br> <br><b>Arborescence d'origine:</b> <br>│ └─ 20230502_MDG-iot_UAV-02_1 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│---------------- └─ tb <br>│-------- └─ PROCESSED_DATA <br> <br><b>Informations de survol:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -89.90 <br> <br>- Survey informations: <br> No Images: 303 <br> Median height: 256 meters <br> Survey area: 141.79 hectares <br> Survey from: 2023:05:02 16:10:31 to: 2023:05:02 16:50:41 <br>"
Orthophoto & DEM (MNE) issues d'images drone, UAV, Iot, Madagascar - 20230503 - 02_1
"Ce jeu de données présente les résultats des traitements photogrammétriques d'images de drone DJI Mavic 2 Pro UAV acquises sur le site de Iot, Madagascar à la date suivante : 20230503. <br>Les vols ont été réalisés en partenariat avec l'IH.SM dans le but de créer des modèles numériques d'élévations pour cartographier l'écosystème marin. <br> <br><br>Le paramétrage du logiciel OpenDroneMap est partagé pour permettre la reproductibilité ou l'amélioration des traitements proposés:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>Le dépôt est composé des éléments suivants:</b> <br> - 00_: Planche d'aperçu des images <br> - DCIM.zip: Images brutes issues du drone <br> - GPS.zip: Geopackage contenant l'emprise du survol ainsi que la géolocalisation des images accompagnées de leurs miniatures dans la table d'attribut en base64 <br> - METADATA.zip: Métadonnées au format ISO19115, Rapports avec miniatures des images de drone (dossier tb) et statistiques de vols. <br> - PROCESSED_DATA.zip: Orthophoto, DEM, nuages de points, ... <br> <br><b>Arborescence d'origine:</b> <br>│ └─ 20230503_MDG-iot_UAV-02_1 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│---------------- └─ tb <br>│-------- └─ PROCESSED_DATA <br> <br><b>Informations de survol:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -90.00 <br> <br>- Survey informations: <br> No Images: 299 <br> Median height: 148 meters <br> Survey area: 132.44 hectares <br> Survey from: 2023:05:03 07:06:46 to: 2023:05:03 07:48:00 <br>"
Lightweight Self-adaptive Cloud-IoT Monitoring across Fed4FIRE+ Testbeds (LiSCIo)
<p>Monitoring will be crucial to properly orchestrate next-gen services. Indeed, monitoring’s output can be exploited to choose where to deploy application services for the first time and to decide when and where to migrate them in case their QoS and contextual requirements cannot be satisfied by the current deployment and infrastructure state. However, only a few works have focused so far on the design and prototyping of monitoring tools for next-gen Cloud-IoT computing platforms.</p> <p>In this context, <a href="https://github.com/di-unipi-socc/FogMon">FogMon</a>, described in (Brogi et al., 2019) and (Forti et al., 2021), is an open-source C++ distributed monitoring service targeting heterogeneous infrastructures along the Cloud-IoT continuum, e.g. Fog computing. FogMon monitors hardware and virtualised resources at different Cloud-IoT computing nodes, end-to-end network QoS between such nodes, as well as available IoT devices. Besides, it features a self-organising peer-to-peer overlay topology with self-restructuring mechanisms and differential monitoring updates, which feature scalability, fault-tolerance, and low communication overhead.</p> <p>The LiSCIo project aimed at assessing FogMon over increasing infrastructures from 20 to 40 Cloud and Edge nodes, spanning two testbeds within the Fed4Fire+ federated infrastructure. Particularly, LiSCIo implemented a new version of the service, i.e. <a href="https://github.com/di-unipi-socc/FogMon-LiSCIo/tree/2.0">FogMon 2.0</a>, which was thoroughly fixed and tuned over a large number of experiments carried on Fed4Fire+ facilities. Throughout the project, data have been collected on all the measurements performed by FogMon 1.x and by FogMon 2.0 (viz. node hardware, IoT, latency, bandwidth) to assess their footprint on hardware resources and bandwidth in all settings, and the relative error on its estimates of latency and bandwidth against ground-truth configurations, enforced via GRE tunnels.</p>
Leaflet Engage IoT Societal Engagements with the Internet of Things
<p>Leaflet of the project Engage IoT, funded by the Fundação para a Ciência e a Tecnologia (EXPL/SOC-SOC/1375/2021), in the shape of a fortune teller.</p> <p>Website of the project: https://engageiot.wordpress.com/</p>
Dataset of IoT-Based Energy and Environmental Parameters in a Smart Building Infrastructure
<p>This dataset includes detailed measurements from IoT sensors deployed throughout the M5 building, capturing energy consumption from various devices like coffee machines, microwaves, etc., as well as environmental data such as temperature, humidity, and occupancy in key areas like the Interdisciplinary lab, kitchen, and mailroom.</p> <p>This release aims to provide researchers and practitioners with comprehensive data to facilitate research on energy efficiency and environmental monitoring within smart building infrastructures. The data are structured to support various types of analysis, from operational efficiency assessments to environmental impact studies.</p> <p>For detailed information on the dataset's structure and usage, please refer to the README.md file included in this repository.</p>
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
Investigating secondary students' stance on IoT driven educational activities - Dataset
<p>This data set supports the research and the results that are presented in Glaroudis, D., Iossifides, A., Spyropoulou, N., Zaharakis, I. D., “Investigating Secondary Students' Stance on IoT Driven Educational Activities”. In Kameas A, and Stathis K. (Eds) Ambient Intelligence, LNCS 11249, 2018, pp. 188-203. Springer Nature Switzerland AG. DOI: https://doi.org/10.1007/978-3-030-03062-9_15.</p>
Context-Aware Dataset: STS - South Tyrol Suggests IoT Mobile App Data
<p><strong>STS dataset </strong>was collected by a context-aware recommender system mobile app named as<strong> <a href="https://play.google.com/store/apps/details?id=it.unibz.sts.android&hl=en">"South Tyrol Suggests"</a></strong>. The app provides <strong>context-aware recommendations</strong> for attractions, events, public services, restaurants, and much more based on the rating preferences and personality factors of users.</p> <p><strong>Contextual</strong> <strong>variables</strong> includes </p> <ul> <li><strong>distance:</strong> far away, near by</li> <li><strong>time available:</strong> half day, one day, more than one day</li> <li><strong>temperature:</strong> burning, hot, warm, cool, cold, freezing</li> <li><strong>crowdedness:</strong> crowded, not crowded, empty</li> <li><strong>knowledge of surroundings:</strong> new to area, returning visitor, citizen of the area</li> <li><strong>season:</strong> spring, summer, autumn, winter</li> <li><strong>budget:</strong> budget traveler, price for quality, high spender</li> <li><strong>daytime:</strong> morning, noon, afternoon, evening, night</li> <li><strong>weather:</strong> clear sky, sunny, cloudy, rainy, thunderstorm, snowing</li> <li><strong>companion:</strong> alone, with friends/colleagues, with family, with girlfriend/boyfriend, with children</li> <li><strong>mood:</strong> happy, sad, active, lazy weekday: weekday, weekend</li> <li><strong>travel goal:</strong> visiting friends, business, religion, health care, social event, education, scenic/landscape, hedonistic/fun, activity/sport</li> <li><strong>means of transport:</strong> no transportation means, a bicycle, a car, public transport</li> </ul> <p>More details can be found here:</p> <p><em>Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. <a href="https://www.researchgate.net/profile/Mehdi_Elahi2/publication/283502363_Techniques_for_cold-starting_context-aware_mobile_recommender_systems_for_tourism/links/56ccaa7608ae059e37507cc0.pdf">"<strong>Techniques for cold-starting context-aware mobile recommender systems for tourism</strong>."</a> Intelligenza Artificiale 8, no. 2 (2014): 129-143.</em></p>
Data set: Industrial IoT-driven remote path planning
<p>This compressed file contains data from three different experiments during the IIoT-REPLAN experimentation phase (Industrial IoT-drive remote path planning). IIoT-REPLAN was funded by an open call from the H2020 Fed4FIRE+ project.</p> <p>Contents:</p> <p>A. astar.csv<br> This file contains the timestamp of each movement of the Robot and the uncertainty (d) at each specific time that Switch 1 was checked. The first two columns refer to seconds while the third one is a scalar value. The total duration of the experiment is 62.33 sec and the setup of this experiment is the real time application of the Astar Algorithm with the localization being based only on the sensor of the Robot</p> <p><br> B. Dijkstra.csv <br> In this experiment the full functionality of the switching system proposed in this work is highlighted. </p> <p>C. Cloud.csv<br> In this experiment the localization algorithm and the path planning algorithm are always executed on the cloud.</p> <p>In both B,C experiments the values of each column are explained inside the Dijkstra.csv </p> <p>Also, two pictures of singlie vision-based self localization are included. </p> <p>A more detailed exposition on all of the above can be found at <br> github link : https://github.com/maravger/alphabot-ppl</p>
Indoor Wireless Deterministic Anycast Transmissions Data from the FIT IoT-Lab testbed
<p>This dataset contains the raw openwsn results generated by indoor experiments.</p> <p>The data was collected on the <a href="https://www.iot-lab.info">FIT IoT-Lab</a> platform, using the m3 motes with a AT86RF231 radio chip, on the Grenoble's site.</p> <p>We rely on the following workflow:</p> <ul> <li>a modified version of openwsn that implements anycast transmissions at the link layer (CCA branch, <a href="https://github.com/ftheoleyre/openwsn-fw/releases/tag/duocast-mswim21">https://github.com/ftheoleyre/openwsn-fw/releases/tag/duocast-mswim21</a>). The firmware is implemented in C, and is executed by the m3 motes;</li> <li>a modified version of openvisualizer (<a href="https://github.com/ftheoleyre/openvisualizer/releases/tag/mswim21">https://github.com/ftheoleyre/openvisualizer/releases/tag/mswim21</a>)</li> <li>a tool to process the dataset and compute the metrics: end-to-end reliability, number of transmissions, CCA events, etc. (<a href="https://github.com/ftheoleyre/openwsn-data/releases/tag/mswim21-duocast">https://github.com/ftheoleyre/openwsn-data/releases/tag/mswim21-duocast</a>)</li> </ul> <p> </p> <p> </p> <p> </p>
Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices
<p>This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices"</p> <p>With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.</p> <p>For more detail about this dataset, please refer to the paper listed above.</p>
ASSIST-IOT Open Call Project RAZOR DATASETS (INSIGHIO)
<p>Example datasets for road anomaly detection produced in the context of RAZOR Open Call ASSIST-IoT Project, carried out by INSIGHIO.</p>
EU-IoTs CSA's SRIA meta-analysis database
<p>This database compiles the collection of research topics identified throughout various Strategic Research and Innovation Agendas published by Industry Associations relevant to the IoT ecosystem between 2020 and 2023. The present data along with the authors' classification was used to produce the STRATEGIC TOPICS AND THEMES RELATED TO THE NGIOT section within Deliverable 2.6 NGIoT Roadmap and Policy Recommendations of CSA Project: EU-IoT - The European IoT HUb - Growing a sustainable and comprehensive ecosystem for Next Generation Internet of Things.</p> <p>In performing the meta-analysis the following actions were taken to realise the comparison and data collection across the SRIAs and roadmaps:</p> <ul> <li>Within the scope of the identified target communities for the EU-IoT project and NGIoT Initiative7, the latest publications, roadmaps and SRIAs were revised and reviewed. Selected SRIAs to be included met the following criteria: <ul> <li>Relevance to the scope of the NGIoT and latterly the Cloud Edge IoT Continuum. o Levelofdetailandstructuredrepresentation.</li> <li>Specificity and action ability of thetopic sprovided.</li> </ul> </li> <li>From the selected agendas, individual topics were abstracted and categorised under the following fields to provide a comparable analysis and assessment: <ul> <li>Type <ul> <li>Priority area: considered to be topics of strategic importance, encompassing multiple technologies and applications. E.g., Constraint- based planning and decision making in complex natural environments.</li> <li>Application: specific implementations of technologies either within a given context or addressing a defined goal. E.g., Data streaming in constraint environments.</li> <li>Technology: a variety of different technical, electronic, or physical systems, assets, devices or algorithms. E.g., Self-configuring and adaptive sensor nodes.</li> </ul> </li> <li>Theme: definition of the common priority theme taking a bottom-up approach and aligned with the NGIoT technologies.</li> <li>Position within the EU-IoT framework as described in the previous section: <ul> <li>Layer: Tech, Market, Policy & Standards, Skills, All.</li> <li>Context: Human Interface, Far Edge, Near Edge, Infrastructure, Data Spaces, All.</li> </ul> </li> </ul> </li> <li>Finally, the analysis identified the key trends and themes across the contributing communities and NGIoT framework.</li> </ul> <p>In total 645 topics were abstracted, categorised and analysed across two cycles. The resulting database is provided as a public output for further analysis and reuse by the community and construction of future trend mapping. Within this paper, the latest versions of identified agendas were included in the analysis totalling 590 topics.</p>
Week-long continuous noise levels measured by an IoT-based device - EcoDecibel, in Sanzhi District, Taiwan, Aug 12 2021 - Aug 18 2021.
Noise pollution is a growing concern in urban and rural environments, impacting public health and quality of life. We conducted a study to check the validation of an IoT based, low cost, noise sensor - EcoDecibel as compared to traditional Class 1 and Class 2 noise meters in indoor as well as outdoor environments. Data was collected continuously over a seven-day period at various sites, including arterial roadways, county highways, and environmental areas. The primary objective was to evaluate the performance of the EcoDecibel device in comparison to the gold standard noise meters which was conducted in different settings in indoor and outdoor environments and then the EcoDecibel device was set up in field to analyse the real world noise measurements.Despite its lower cost, the EcoDecibel device demonstrated a high degree of accuracy with an R2 value of 0.9 when compared to the standard devices. This dataset provides valuable insights into noise pollution levels across different environments and highlights the potential of low-cost noise measurement devices for widespread monitoring and research. The metadata accompanying this dataset includes detailed information on the experimental setup, data collection methods, and analysis procedures. The dataset is openly available for further research and validation, promoting transparency and reproducibility in environmental noise studies.
Brainport, Platooning, baseline platoon formation without IoT
<p><strong>Scenario description</strong>:</p> <p>Platoon formation and platooning, from Helmond to Eindhoven and back to the Automotive Campus.<br> - Starting in urban area with speed limits of 15 and 30 km/h.<br> - Driving East on the Europaweg with speed limits of 50 and 70 km/h. This includes 3 crossings with traffic lights.<br> - Driving on the the N270, along the Automotive Campus. One crossing with traffic lights, just before the A270.<br> - Driving on the A270 (speed limit 100 km/h). Interrupted by one traffic light.<br> - U-turn at the fly-over or at the end of the A270, to return the same way to the Automotive Campus.</p> <p><strong>Session description</strong>:</p> <p>Baseline of platoon formation and platooning, without any IoT, so no speed advices.<br> - No live traffic light data available for planner<br> - Starting at default locations<br> - Meeting at the location of vehicle2 on the Automotive Campus<br> - Platooning (CACC and lane keeping) on the A270 when possible.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonFormation</strong>: Data sent from PlatoonService to vehicle</p> <p>This dataset contains information about the route and speed for a specific vehicle for forming a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningAction</strong>: Data logged by vehicle</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningEvent</strong>: Data logged by vehicle</p> <p>This dataset contains information about the identifiers used for each specific platooning event</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonStatus</strong>: Data sent by vehicle to PlatoonService</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystemResample</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PSInfo</strong>: Data sent by PlatoonService to the vehicle</p> <p>This dataset contains speed and route information for the vehicle to create a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Target</strong>: Data from sensors on the vehicle</p> <p>Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
IoT device identification - Background
<p>Background measurements of the 868 MHz ISM band.</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>
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.