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229 results for “wireless”
Dataset for paper entitled "A Wireless Inductive Sensing Technology for Soft Pneumatic Actuators Using Magnetorheological Elastomers"
<p>This dataset includes all the experimental and FE results presented in the RoboSoft2019 paper "A Wireless Inductive Sensing Technology for Soft Pneumatic Actuators Using Magnetorheological Elastomers" (DOI: <a href="https://doi.org/10.1109/ROBOSOFT.2019.8722800">10.1109/ROBOSOFT.2019.8722800</a>).</p> <p>https://ieeexplore.ieee.org/abstract/document/8722800</p> <p>List of data:</p> <p>Fig.3-EXP_Coil size.xlsx<br> Fig.4-MRE Characterization.xlsx<br> Fig.6-FE modeling results.xlsx<br> Fig.8-Flat SPA Characterization.xlsx<br> Fig.9-EXP-external load.xlsx<br> Fig.10-Exp-Bending SPA.xlsx</p>
PMU measurements altered by wireless communication such as 3G, 4G, 5G
<p><span>Dataset which shows the effect of three types of wireless communication (e.g., 3G, 4G, and 5G, respectively) on data integrity of two real Phasor Measurement Unit (PMU) measurements which are sent to a virtual Phasor Data Concentrator (vPDC) for timestamp synchronization function. Each file contains the values of the two real PMUs installed at each end of a high voltage (HV) transmission line located in a transmission power grid in South Europe. The datasets were collected using an advanced Power-Hardware In the Loop (P-HIL) setup, including in the communication loop between the PMUs and the vPDC a hardware network emulator. The later had the role to realistically emulate the macroscopic behavior of the communication delays and packet data loss of the three wireless communication networks. The delays and data packet loss were imposed only at one of the two PMUs (PMU Lab1) considering a power grid system running in balanced operation conditions. Therefore, only phase A was recorded in the data, and the reason why the current values from phase B and phase C are almost zero. </span></p>
Jornada Basin LTER wireless meteorological station at MNORT wind tower site: 5-minute summary data, 2006 - ongoing (provisional)
This dataset contains 5-minute summary data from the MNORT wind tower station. Average air temperature, wind speed and wind direction at multiple heights are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER MNORT site (different than the M-NORT NPP site). Wind speed is measured at 135 cm, 230 cm, 345cm, 705cm, and 1515 cm, wind direction at 250cm and 850cm, and air temperature at 80cm and 1440cm. This climate station is operated by the Jornada LTER Program and this is an ongoing dataset. CAUTION: little to no QA/QC has been applied to this dataset and these data are therefore provisional.
Wireless Sensor Network Deployments (2013-2017)
<p><strong>Wireless Sensor Network Deployments (2013-2017)</strong></p> <p>This is an open data repositiory.</p> <p>It is concerned with systematically reviewing scientific publications containing actual Wireless Sensor Network deployments in five year span from 2013 to 2017.</p> <p><strong><em>Identification</em></strong></p> <p>Articles were first searched for in SCOPUS and Web of Science databases on 2018-06-12 using these queries/settings:</p> <p>SCOPUS</p> <p>Query: KEY({sensor network} OR {sensor networks}) AND TITLE-ABS-KEY(test* OR experiment* OR deploy*) AND NOT TITLE-ABS-KEY(review) AND NOT TITLE-ABS-KEY(simulat*) AND ( LIMIT-TO ( PUBYEAR,2017 ) OR LIMIT-TO ( PUBYEAR,2016 ) OR LIMIT-TO ( PUBYEAR,2015 ) OR LIMIT-TO ( PUBYEAR,2014 ) OR LIMIT-TO ( PUBYEAR,2013 ) )</p> <p>Raw results: 11536 articles</p> <p>De-duplicated results: 11374 articles</p> <p>Contained 4814 articles not found in Web of Science</p> <p>Web Of Science</p> <p>Querry: TS = ("sensor network" OR "sensor networks") AND TS = (test* OR experiment* OR deploy*) NOT TI="review" NOT TS=simulat*</p> <p>Additional query parameters: Indexes=SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC Timespan=2013-2017</p> <p>Raw results: 10204 articles</p> <p>De-duplicated results: 10196</p> <p>Contained 3636 articles not found in SCOPUS</p> <p>Final results</p> <p>When article results were merged from both databases finally 15010 articles were identified as possible candiates. Of those 6560 were found in both databases.</p> <p><em><strong>Screening</strong></em></p> <p>Data was exported as bibtex files and imported in Mendeley software for screening.</p> <p>4910 articles were left after the screening phase</p> <p><em><strong>Eligibility check</strong></em></p> <p>Then all screened included articles were checked for eligibility and 3017 eligible articles were identified.</p> <p><em><strong>Data extraction</strong></em></p> <p>In these articles 3059 wireless sensor network deployments were identified and codified data extracted from them.</p> <p><em><strong>Timeline</strong></em></p> <p>This data analysis took total time (including validation and error checking) from 2018-06-12 till 2020-05-29, after which the data was prepared for publiching till 2020-07-02.</p>
Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
MeshDapp: Blockchain-enabled Payment System for Wireless Mesh Networks - Dataset
<p>The MeshDapp dataset includes data from the MeshDapp project - Fed4Fire+ Open Call 9 winner:</p> <p>Data includes: Network Measurement from the CityLab - Antwerp testbed and IRIS (TCD) testbed. </p> <p>MeshDapp Paper:https://mvdsi.seeu.edu.mk/mselimi/papers/DAPPS20ms.pdf </p>
SIMBED - Offline Real-World Wireless Networking Experimentation using ns-3
<p>R&D in wireless networking typically depends on experimentation to make realistic evaluations, since simulation is inherently a simplification of the real-world. However, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility.</p> <p>Real wireless experiments are hardly repeatable. Given the same input they can produce very different output results, since wireless communications are influenced by external random phenomena such as noise, interference, and multipath. Real experiments are also difficult to reproduce: either the original community testbed is unavailable – offline or running other experiments – or the custom testbed used is inaccessible.</p> <p>Fed4FIRE+ wireless testbeds such as w-iLab.t and NITOS, although deployed in controlled environments, do not fully address the problem. The CONCRETE tool used in such testbeds assures the repeatability and reproducibility of experiments, but ignores executions whose results are also representative of the system operation and often reveal unpredicted behaviour that must be understood.</p> <p>What if we could make any wireless experiment repeatable and reproducible under the same exact conditions? What if we could share the same Fed4FIRE+ testbed execution conditions among an "infinite" number of users? What if we could run wireless experiments faster than in real time?</p> <p>INESC TEC has been developing the Offline Experimentation (OE) approach that combines the best of simulation and experimentation to achieve the above-mentioned goals. By relying on Network Simulator 3 (ns-3) and its good simulation capabilities from the MAC to the application layer, we have been exploring how ns-3 can be used to replicate real-world wireless experiments using real traces containing 1) position of nodes and 2) the quality of each radio link.</p> <p>The <strong>SIMBED </strong>project aimed at running a set of wireless experiments on top of the controlled environments of w-ilab.t and NITOS Fed4FIRE+ testbeds to further validate the OE approach. For that purpose, we configured different fixed and mobile experimental scenarios, representative of Wi-Fi range of operation, and measured the attained network performance using metrics such as throughput and Round-Trip Time (RTT). Then, we repeated each experiment using, both, Pure Simulation (PS) and OE approaches based on ns-3, also measuring the network performance for the same set of executions of experiments for all the different scenarios.</p> <p>By comparing the performance metrics of each real experiment with its PS and OE counterparts, we were able to measure the relative error of each simulation approach relatively to the real experiments, as well as the accuracy gains introduced by the OE approach when compared to the PS traditional alternative. The main results show that it is possible to repeat and reproduce real experiments in ns-3, using the OE approach, achieving closer to real performance than using the PS approach. For all the experiments performed in SIMBED, using the OE approach resulted in an average accuracy gain of 59% when comparing to the PS approach. </p> <p>These results were important for validating a PhD thesis contribution related to the OE approach, as well as for producing two conference papers and one journal paper. The SIMBED results increased our confidence on the accuracy of the OE approach and are envisioned to foster the adoption of the OE approach by the networking community, in complement to the use of real experimentation.</p> <p> </p> <p>The following dataset presents the results of the SIMBED project, organized in different folders, for each subset of experiments carried on:</p> <ul> <li><strong><em>SubExp#1: </em></strong><em>Static point-to-point Wi-Fi communications using auto-rate (Minstrel) </em> <ul> <li><strong><em>SubExp#1.1:</em></strong><em> Using w-iLab.2 (medium to high SNR scenarios)</em></li> <li><strong><em>SubExp#1.2:</em></strong><em> Using w-iLab.2 (low SNR scenarios)</em></li> <li><strong><em>SubExp#1.3:</em></strong><em> Using NITOS</em></li> <li><strong><em>SubExp#1.4:</em></strong><em> Using w-iLab.1 (datacenter room)</em></li> </ul> </li> <li><strong><em>SubExp#2: </em></strong><em>Static point-to-point Wi-Fi communications using fixed</em> rate</li> <li><strong><em>SubExp#3: </em></strong><em>Mobile point-to-point Wi-Fi communications using auto-rate (Minstrel)</em></li> <li><strong><em>SubExp#4: </em></strong><em>Static multiple access Wi-Fi communications using auto-rate (Minstrel) </em> <ul> <li><strong><em>SubExp#4.1:</em></strong><em> Using w-iLab.2 (bidirectional) (medium to high SNR scenarios)</em></li> <li><strong><em>SubExp#4.2:</em></strong><em> Using w-iLab.2 (bidirectional) (low SNR scenarios)</em></li> <li><strong><em>SubExp#4.3:</em></strong><em> Using NITOS (bidirectional)</em></li> <li><strong><em>SubExp#4.4:</em></strong><em> Using w-iLab.1 (bidirectional)</em></li> <li><strong><em>SubExp#4.5:</em></strong><em> Using NITOS (2 STAs)</em></li> <li><strong><em>SubExp#4.6:</em></strong><em> Using w-iLab.2 (2 STAs)</em></li> </ul> </li> <li><strong><em>SubExpExample</em></strong>: contains raw experimental logs, parsed data and simulation results, to show how data extracted from the nodes is processed to be compatible with the OE approach and comparable with OE and PS simulation results.</li> </ul> <p>Each experiment has an individual folder, named according to the date and time of the experiment and the nodes used. Inside, there’s a folder for the <strong>parsed</strong> experimental results, which contains</p> <p>This folder contains the details and parsed logs of the experiment, as follows:</p> <ul> <li><em>date_time</em><strong>.cfg </strong>– configuration details of the experiment</li> <li><em>date_time_NodeID<sup><a href="#_ftn1"><strong>[1]</strong></a></sup>_SenderID<sup><a href="#_ftn2"><strong>[2]</strong></a></sup>_ReceiverID<sup><a href="#_ftn3"><strong>[3]</strong></a></sup>_FlowType<sup><a href="#_ftn4"><strong>[4]</strong></a></sup>_Params<sup><a href="#_ftn5"><strong>[5]</strong></a></sup></em><strong>.snr </strong>– logs of the Signal/Noise ratio (1 file per node/flow) </li> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em><strong>.stats</strong> – logs of the packets received (1 file per node/flow) </li> <li><em>NodeID</em><strong>.</strong><strong>waypoints</strong> – coordinates of the static nodes</li> <li><em>date_time_MobileNodeID</em><strong>.</strong><strong>waypoints</strong> – waypoints of the mobile nodes (when applicable)</li> </ul> <p>The experiment’s folder also contains a folder for the simulations <strong>output</strong> with the simulations statistics files, for the multiple simulations approaches considered, as follows:</p> <ul> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em>.<strong>simstats </strong>– logs of the packets received (simulation)</li> </ul> <p> </p> <p><sub><a href="#_ftnref1">[1]</a> ID of the node Logging node</sub></p> <p><sub><a href="#_ftnref2">[2]</a> ID of the Sender node</sub></p> <p><sub><a href="#_ftnref3">[3]</a> ID of the Receiver node</sub></p> <p><sub><a href="#_ftnref4">[4]</a> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access</sub></p> <p><sub><a href="#_ftnref5">[5]</a> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)</sub></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 for 'Zinc hybrid sintering for printed transient sensors and wireless electronics'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “Zinc hybrid sintering for printed transient sensors and wireless electronics”.</p> <p>This work aims to study and develop a method for the efficient sintering of printed zinc metal, with the aim to facilitate the fabrication of biodegradable electronics by additive manufacturing. Biodegradable electronic devices have potential in tackling the increasingly pressing challenge of electronic waste, and present opportunities for the fabrication of novel bioresorbable medical devices that can harmlessly degrade in the body and eliminate the need for re-operation. The method that is presented in this publication combines electrochemical and photonic sintering approaches to enable the fabrication of highly-conductive degradable metal tracks. Several sensors are shown as demonstrators (temperature, strain, pressure). The data that was collected in the frame of this work is present in this repository. It relates to both the study of the process introduced above as well as the characterization of the demonstrators. More information about the contents of the dataset is present in the included README files.</p>
Raw data of findings in the article "Sub-THz_wireless_transmission_based_on_graphene_integrated_optoelectronic_mixer" by A. Montanaro et al.
<p>Raw data containing all the plots in the manuscript "Sub-THz wireless transmission based on graphene integrated optoelectronic mixer" by A. Montanaro et al.</p>
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Wireless Link Quality Estimation on FlockLab - and Beyond
<p>This repository contains wireless link quality estimation data for the FlockLab testbed [1,2]. The rationale and description of this dataset is described in a the following abstract (pdf is included in this repository -- see below).</p> <blockquote> <p><strong>Dataset: Wireless Link Quality Estimationon FlockLab – and Beyond</strong><br> Romain Jacob, Reto Da Forno, Roman Trüb, Andreas Biri, Lothar Thiele<br> DATA '19 Proceedings of the 2nd Workshop on Data Acquisition To Analysis, 2019</p> </blockquote> <p><strong>Data collection scenario</strong></p> <p>The data collection scenario is simple. Each FlockLab node is assigned one dedicated time slot. In this slot, a node sends 100 packets, called strobes. All strobes have the same payload size and use a given radio frequency channel and transmit power. All other nodes listen for the strobes and log packet reception events (i.e., success or failed). </p> <p>The test scenario is ran every two hours on two different platforms: the TelosB [3] and DPP-cc430 [4] platforms. We used all nodes currently available at test time (between 27 and 29).</p> <p><strong>Final dataset status</strong></p> <ul> <li>3 months of data with about 12 tests per day per platform</li> <li>5 month of data with about 4 tests per day per platform</li> </ul> <p><strong>Data collection firmware</strong></p> <p>We are happy to share the link quality data we collected for the FlockLab testbed, but we also wanted to make it easier for others to collect similar datasets for other wireless networks. To achieve this, we include in this repository the data collection firmware we design. The entire data collection scheduling and control is done entirely in software, in order to make the firmware usable in a large variety on wireless networks. We implemented our data collection software using Baloo [5], a flexible network stack design framework based on Synchronous Transmission. Baloo efficiently handles network time synchronization and offers a flexible interface to schedule communication rounds. The firmware source code is available in the Baloo repository [6].</p> <p>A set of experiment parameters can be patched directly in the firmware, which let the user tune the data collection without having to recompile the source code. This improves usability and facilitates automation. An example patching script is included in this repository. Currently, the following parameters can be patched:</p> <ul> <li>rf_channel,</li> <li>payload,</li> <li>host_id, and</li> <li>rand_seed</li> </ul> <p><em>Current supported platforms</em></p> <ul> <li>TelosB [3]</li> <li>DPP-cc430 [4]</li> </ul> <p><strong>Repository versions</strong></p> <ul> <li><strong>v1.4.1</strong><br> Updated visualizations in the notebook</li> <li><strong>v1.4.0</strong><br> Addition of data from November 2019 to March 2020.<br> Data collection is discontinued (the new FlockLab testbed is being setup).</li> <li><strong>v1.3.1</strong><br> Update abstract and notebook</li> <li><strong>v1.3.0</strong><br> Addition of October 2019 data.<br> The frequency of tests has been reduced to 4 per day, executing at (approximately) 1:00, 7:00, 13:00, and 19:00.<br> From October 28 onward, time shifted by one hour (2:00, 8:00, 14:00, 20:00).</li> <li><strong>v1.2.0</strong><br> Addition of September 2019 data.<br> Many missing tests on the 12, 13, 19, and 20 of September (due to construction works in the building).</li> <li><strong>v1.1.4</strong><br> Update of the abstract to have hyperlinks to the plots. Corrected typos.</li> </ul> <ul> <li><strong>v1.1.0</strong><br> Initial version.<br> Add the data collected in August 2019.<br> Data collected was disturbed at the beginning of the month and resumed normally on the August 13. Data from previous days are incomplete.</li> <li><strong>v1.0.0</strong><br> Initial version.<br> Contain collected data in July 2019, from the 10th to 30th of July.<br> No data were collected on the 31st of July (technical issue).</li> </ul> <p><strong>List of files</strong></p> <ul> <li><strong>yyyy-mm_raw_platform.zip</strong><br> Archive containing all FlockLab test result files (one .zip file per month and per platform).</li> <li><strong>yyyy-mm_preprocessed_all.zip</strong><br> Archive containing preprocessed csv files, one per month and per platform.</li> <li><strong>firmware.zip</strong><br> Archive containing the firmware for all supported platform.</li> <li><strong>firmware_patch.sh</strong><br> Example bash script illustrating the firmware patching.</li> <li><strong>parse_flocklab_results.ipynb </strong>[<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3731498/files/parse_flocklab_results.ipynb">open in nbviewer</a>]<br> Jupyter notebook used to create the pre-process data files. Also includes some example of data visualization.</li> <li><strong>parse_flocklab_results.html</strong><br> HTML rendering of the notebook (static).</li> <li><strong>plots.zip</strong><br> Archive containing high resolution visualization of the dataset, generated by the <strong>parse_flocklab_results </strong>notebook, and presented in the <strong>abstract</strong>.</li> <li><strong>abstract.pdf</strong><br> A 3 page abstract presenting the dataset.</li> <li><strong>CRediT.pdf</strong><br> The list of contributions from the authors.</li> </ul> <p><strong>References</strong></p> <p>[1] R. Lim, F. Ferrari, M. Zimmerling, C. Walser, P. Sommer, and J. Beutel, “FlockLab: A Testbed for Distributed, Synchronized Tracing and Profiling of Wireless Embedded Systems,” in <em>Proceedings of the 12th International Conference on Information Processing in Sensor Networks</em>, New York, NY, USA, 2013, pp. 153–166.</p> <p>[2] “FlockLab,” <em>GitLab</em>. [Online]. Available: <a href="https://gitlab.ethz.ch/tec/public/flocklab/wikis/home">https://gitlab.ethz.ch/tec/public/flocklab/wikis/home</a>. [Accessed: 24-Jul-2019].</p> <p>[3] Advanticsys, “MTM-CM5000-MSP 802.15.4 TelosB mote Module.” [Online]. Available: <a href="https://www.advanticsys.com/shop/mtmcm5000msp-p-14.html">https://www.advanticsys.com/shop/mtmcm5000msp-p-14.html</a>. [Accessed: 21-Sep-2018].</p> <p>[4] Texas Instruments, “CC430F6137 16-Bit Ultra-Low-Power MCU.” [Online]. Available: <a href="http://www.ti.com/product/CC430F6137">http://www.ti.com/product/CC430F6137</a>. [Accessed: 21-Sep-2018].</p> <p>[5] R. Jacob, J. Bächli, R. Da Forno, and L. Thiele, “Synchronous Transmissions Made Easy: Design Your Network Stack with Baloo,” in <em>Proceedings of the 2019 International Conference on Embedded Wireless Systems and Networks</em>, 2019.</p> <p>[6] “Baloo,” Dec-2018. [Online]. Available: <a href="http://www.romainjacob.net/research/baloo/">http://www.romainjacob.net/research/baloo/</a>.</p> <p> </p>
WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research
<p>The WIDEFT data corpus has been created to provide bursts from wireless devices in the spectrum of Bluetooth, WiFi, and other RF signals to further research of the acquisition and usage of wireless device fingerprints. WIDEFT was developed through the efforts of the Physical Science Laboratories (PSL) at New Mexico State University (NMSU). Data collection was recorded at PSL and at NMSU main campus and cataloged, maintained, and prepared for release at PSL.</p> <p>Please cite the following article:</p> <p>A. Bucker Siddik, D. Drake, T. Wilkinson, P. L. De Leon, S. Sandoval, and M. Campos, “WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research,” <em>IEEE Int. Symp. Technol. Homel. Secur. (HST)</em>, 2021.</p>
Dataset: On the Properties of Next Generation Wireless Backhaul
<p>This dataset contains all the data used in the Paper: "On the Properties of Next Generation Wireless Backhaul" sent to Transaction of Network Science and Engineering.</p> <p>It is divided into three main archives:</p> <ul> <li>The first archive, called <strong>geodata.zip</strong>, contains the Data Surface Model (DSM) and the topographical maps used to generate the intervisibility graphs. These maps have been aggregated from different sources and they are all released under a CC-BY-SA 4.0 license. It is divided into two folders: 'dsm' and 'topo_maps': <ul> <li> 'dsm' contains the Data Surface Model (DSM) of the nine areas used in our research. Each area is saved in a separate .tif file and can be used directly from the tool. The projection system is EPSG:3003.</li> <li>'topo_mapsì contains the PostGIS dump of the tables containing the topographical maps of the cities. In order to use them, you have to import those in PostGIS and connect our tool to the PostGIS DB.<br> The CTR files (technical region maps) are divided by region, while the openstreetmap file (osm.tar) contains the whole Italian peninsula. All the geographical data are projected in the reference system EPSG:3003. The CTR of Campania is unavailable due to licensing incompatibilities.</li> </ul> </li> <li>The second archive, called <strong>visibility_graphs.zip</strong>, contains the visibility graph we have generated using our tool. It is released as a CC-BY-SA 4.0 license. It is divided into folders, one for each area of the analysis. Each folder contains two files: <ul> <li>best_p.csv : contains the ids of the nodes associated with their coordinates on a cartesian projection. The projection is EPSG:3003</li> <li>intervisibility.adj : contains the intervisibility graph represented as an adjacency matrix. It can be read by common libraries such as networkx or igraph. The intervisibility has been calculated 2m above the building.</li> </ul> </li> <li>The third folder, called <strong>network_topologies.zip</strong>, contains the network topologies we have computed using a State-of-the-Art algorithm from Polese et Al. The folder is divided into two folders: '30gNB' and '60gNB', corresponding to the density of base stations for square km. It s further divided in 9 folders (one for each area), then divided on the basis of the model we use for the visibility graph. You can find more information about these models in section V of the paper. In this last folder we have two sets of 50 files, which are the intervisibility graph of that set of Base Stations and the topology produced. This dataset has been released under a CC-BY-SA 4.0 license.</li> </ul> <p>All the results can be replicated using our code which will be released with an open-source license as soon as the article is published.</p>
WCEbleedGen: A wireless capsule endoscopy dataset containing bleeding and non-bleeding frames
<p>WCEbleedGen is a wireless capsule endoscopy dataset containing bleeding and non-bleeding frames. The dataset promotes generalized comparison with existing state-of-the-art methods, and contribute to better interpretability, and reproducibility of such automated systems. </p><p>19-11-2023 Update : Multiple bleeding frames have been reannotated. New XML and YOLO-TXT have been added.</p>
Dataset for Anomaly Detection in a Production Wireless Mesh Community Network
<p>CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24). </p> <p>Live mesh network monitoring link: <a href="http://dsg.ac.upc.edu/qmpsu">http://dsg.ac.upc.edu/qmpsu</a></p> <p>The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00". The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture.</p> <p>A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid.</p> <p><br> Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:<br> feature.rate are columns computed from feature as<br> feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])<br> feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted<br> where n is the number of samples</p> <p><strong>features</strong><br> - processes number of processes<br> - loadavg.m1 1 minute load average<br> - softirq.rate servicing softirqs<br> - iowait.rate waiting for I/O to complete<br> - intr.rate <br> - system.rate processes executing in kernel mode<br> - idle.rate twiddling thumbs<br> - user.rate normal processes executing in user mode<br> - irq.rate servicing interrupts<br> - ctxt.rate total number of context switches across all CPUs<br> - nice.rate niced processes executing in user mode<br> - nr_slab_unreclaimable The part of the Slab that can't be reclaimed under memory pressure<br> - nr_anon_pages anonymous memory pages<br> - swap_cache Memory that once was swapped out, is swapped back in but still also is in the swapfile<br> - page_tables Memory used to map between virtual and physical memory addresses<br> - swap <br> - eth.txe.rate tx errors over all ethernet interfaces<br> - eth.rxe.rate rx errors over all ethernet interfaces<br> - eth.txb.rate tx bytes over all ethernet interfaces<br> - eth.rxb.rate rx bytes over all ethernet interfaces<br> - eth.txp.rate tx packets over all ethernet interfaces<br> - eth.rxp.rate rx packets over all ethernet interfaces<br> - wifi.txe.rate tx errors over all wireless interfaces<br> - wifi.rxe.rate rx errors over all wireless interfaces<br> - wifi.txb.rate tx bytes over all wireless interfaces<br> - wifi.rxb.rate rx bytes over all wireless interfaces<br> - wifi.txp.rate tx packets over all wireless interfaces<br> - wifi.rxp.rate rx packets over all wireless interfaces<br> - txb.rate tx bytes over all ethernet and wifi interfaces<br> - txp.rate tx packets over all ethernet and wifi interfaces<br> - rxb.rate rx bytes over all ethernet and wifi interfaces<br> - rxp.rate rx packets over all ethernet and wifi interfaces<br> - sum.xb.rate tx+rx bytes over all ethernet and wifi interfaces<br> - sum.xp.rate tx+rx packets over all ethernet and wifi interfaces<br> - diff.xb.rate tx-rx bytes over all ethernet and wifi interfaces<br> - diff.xp.rate tx-rx packets over all ethernet and wifi interfaces</p>
Figure 2. Experimental wireless sensors setup in greenhouse-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>Figure 2 illustrates how the sensor<br> nodes were deployed to the greenhouse block. The idea of the vertical deployment was to get a<br> better understanding of the microclimate layers which typically exist in the greenhouse, and to<br> figure out what kind of differences occur in the climate between lower and upper flora.</p>
Dataset for "On a Collision Course: Unveiling Wireless Attacks to the Aircraft Traffic Collision Avoidance System (TCAS)"
<p>The dataset associated with "On a Collision Course: Unveiling Wireless Attacks to the Aircraft Traffic Collision Avoidance System (TCAS)"</p>
Dataset: Franklin Wireless Corp. (FKWL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Synthetic dataset for mobile wireless networks with SUMO -- aggregated traces
<p>Aggregated dataset from a published wireless dataset generator base in SUMO mobility model. </p> <p> </p> <p>This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UIDB/50021/2020 and SFRH/BD/132053/2017.</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.