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16 results for “LoRaWAN”
LoRaWAN Dense Indoor Sensor Network (DISN) Transmission Meta Data
<p>We present a large data of indoor Long Range Wide Area Network (LoRaWAN) network metadata to study Dense Indoor Sensor Networks (DISN). We collected 14 million transmissions from 390 sensors between date February 2020 and date September 2020. The transmissions have been received by 3 gateways across 8 floors and distances up to 64 m. The prototype will run in the background throughout the project and the data set will be regularly updated.</p> <p> </p>
Supplementary Materials for "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor"
<p>This work corresponds to the results described in paper "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor": <a href="https://www.mdpi.com/2306-5729/6/6/62">https://www.mdpi.com/2306-5729/6/6/62</a></p> <p>The provided open-access dataset consists of JavaScript Object Notation (JSON) records stored in Comma-Separated Values (CSV) files, and the data were gathered in a span of multiple hours during two days of measurements. Each JSON file contains parameters as described below. In addition to the payload itself, every record on the server also contains additional metadata. Metadata contains general information about the LoRaWAN message and the array of parameters that provide more detailed message reception information for each Gateway (GW) receiving the message separately. Notably, these names may differ between LoRaWAN service providers. In the case of Ceske Radiokomunikace (CRa), the metadata contains the following parameters:</p> <ul> <li> <p>cmd—Command (message type): Incoming (uplink) message from the ED via the GW to the server. This also contains metadata from receiving GWs.</p> </li> <li> <p>seqno—Sequence number: The sequence number of the message in the form of a 32-bit integer. The Network Server generates this number.</p> </li> <li> <p>EUI—Extended Unique Identifier: A global identifier (64-bit) of the terminal device, which the manufacturer or owner assigns. The Institute of Electrical and Electronics Engineers (IEEE) Registration Authority manages the assignment of identifier pools. It is given in hexadecimal format. This identifier is used similarly to the MAC address of the network interface.</p> </li> <li> <p>ts—Timestamp: The time of the received message recorded at the first receiving GW. The parameter indicates the number of milliseconds since the Unix epoch (1 January 1970).</p> </li> <li> <p>fcnt—Frame count: Sequential number of the message (16-bit integer) sent from the device. In the case of a device reset, the value of the counter starts from zero. The value of this parameter can be used to detect a failure to receive messages.</p> </li> <li> <p>port—The port number is used to distinguish the type of application payload message. It is, therefore, not necessary to explicitly add it to the application payload. The Port parameter’s (8-bit integer) possible values range from 1 to 223 for the users. Other values are reserved.</p> </li> <li> <p>freq—Frequency: A value that corresponds to the frequency (expressed in Hertz) of the given LoRaWAN channel. Before transmitting each message, the ED pseudo-randomly selects from the range of available LoRaWAN channels on which it will transmit the message.</p> </li> <li> <p>toa—Time on Air: Message transmission time in milliseconds. This value is directly proportional to the data rate and message size.</p> </li> <li> <p>dr—Data Rate: The string parameter specifying the spreading factor, bandwidth, and coding rate. The spreading factor fundamentally affects the data rate and thus, the message time on-air. The value can be selected from the interval 7 to 12. Bandwidth values are only 125, 250, and 500 kHz. The larger the bandwidth, the higher the data rate.</p> </li> <li> <p>ack—Acknowledge: The parameter is of a Boolean type and indicates whether the ED requires confirmation of the sent message. The default is to avoid using acknowledgments to reduce network traffic.</p> </li> <li> <p>gws—Gateways: Contain an array of information objects from individual GWs, especially information about the parameters of the received signal, timestamp, identifier, and location of the GW.</p> <ul> <li> <p>rssi—Received Signal Strength Indicator: The received signal level on the GW, expressed in dBm. The threshold value of the Semtech SX1301 receiver is −142 dBm [<a href="https://www.mdpi.com/2306-5729/6/6/62/htm#B44-data-06-00062">44</a>].</p> </li> <li> <p>snr—Signal-to-Noise Ratio: This parameter gives the ratio between the received power signal and the noise floor power level in dB. If the SNR is greater than 0, the received signal level is higher than the noise level.</p> </li> <li> <p>ts—Timestamp: The time of the received message in milliseconds since the Unix era (1 January 1970).</p> </li> <li> <p>tmms—Time in ms: GPS time in milliseconds since 6 February 1980. The GW must have GPS connectivity.</p> </li> <li> <p>time—UTC of the received message, with microsecond precision in the ISO 8601 format.</p> </li> <li> <p>gweui—GW extended unique identifier: The 64-bit number in a hexadecimal format specific for each GW.</p> </li> <li> <p>lat—Latitude: GW GPS latitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> <li> <p>lon—Longitude: GW GPS longitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> </ul> </li> <li> <p>bat—Battery status of the ED 8-bit integer value (0—external power supply, 255—battery status is unknown, 1–254—correspond to battery status 0–100%).</p> </li> <li> <p>data—The field contains HEX data, which is unique for the LoRaWAN device in question. It consists of information related to temperature, position, battery level, etc. In the case of our device, it represents our unique data format, which is specifically designed for the purposes of our measurements.</p> </li> <li> <p>device_Lat—Latitude of the measurement point gathered from the GPS.</p> </li> <li> <p>device_Lon—Longitude of the measurement point gathered from the GPS.</p> </li> </ul> <p>The undeniable advantage of the JSON format is that it is in a human-readable form. Thus, without the need for complex parsing, necessary information can be read immediately.</p>
A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN</strong>".</p> <p>Preprint: <a href="https://arxiv.org/abs/1908.05085">https://arxiv.org/abs/1908.05085</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8970177">https://ieeexplore.ieee.org/document/8970177</a></p> <p> </p> <p>The dataset used to create these sets was published in:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The full dataset is available here:</p> <pre><a href="https://doi.org/10.5281/zenodo.1212478">https://doi.org/10.5281/zenodo.1212478</a> </pre> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen and Weyn, Maarten.</p>
Joint Communication and Sensing: a Proof of Concept and Datasets for Greenhouse Monitoring using LoRaWAN
<p>The goal of these LoRaWAN based greenhouse monitoring datasets, is to provide the global research community with a benchmark tool to evaluate different techniques for precision agriculture in large greenhouse environments. An identical collection methodology was used for both of the two datasets over the same tomato crop: during a period of five months, respectively. Together with temperature and humidity values, network information such as receiving time of the message and Received Signal Strength Indicator (RSSI) were stored in the greenhouse monitoring datasets:</p> <ul> <li><strong>Greenhouse-1.csv</strong> <ul> <li>Data from 27 sensors denoted as AF 16-42 with an average of 19687 LoRaWAN messages per sensor from April till August 2020, obtained in the greenhouse for tomato crop in Belgium.</li> </ul> </li> <li><strong>Greenhouse-2.csv</strong> <ul> <li>Data from 19 sensors denoted as AF 49-67 with an average of 19009 LoRaWAN messages per sensor from July till November 2020, obtained in the other greenhouse for tomato crop in the Netherlands.</li> </ul> </li> <li><strong>Greenhouse-1-Transformed-Data.csv</strong> <ul> <li>Mean temperature, humidity, and RSSI values along with plant height for the same period.</li> </ul> </li> </ul> <p>Both the greenhouses, had no LoRaWAN connectivity, so individual gateway were installed for both locations. For Greenhouse-1 data, sensors were switched on in a room on 10<sup>th</sup> of April and brought to the greenhouse chamber on 17<sup>th</sup> April at 06:38 am for sensing. It would be crucial to accordingly use data set, considering the above time period.</p> <p>The collection methodology of datasets, and first results of a joint communication and sensing proof-of-concept are documented in the journal paper : https://www.mdpi.com/1424-8220/22/4/1326.</p> <p> </p>
LoRaWAN Network Attacks
<p>This dataset includes a dump of messages seen on a LoRaWAN network collected during validation of the GUARD project. It includes plain traffic from real devices installed on city bus and some attacks artificially generated by replicating or altering messages.</p>
Zigbee sensor range extension experiments with LoRaWAN in Langenlois Austria
<p>This dataset contains log files and a brief overview of a field expedition in Langenlois/Austria, where range extension of Zigbee sensors with LoRaBridge in out-door conditions was successfully verfied.</p>
LoRaWAN Traffic Analysis Dataset
<p>This dataset was created by a LoRaWAN sniffer and contains packets, which are thoroughly analyzed in the paper <a href="https://www.mdpi.com/1424-8220/23/17/7333"><em>Exploring LoRaWAN Traffic: In-Depth Analysis of IoT Network Communications</em></a>. Data from the LoRaWAN sniffer was collected in four cities: Liege (Belgium), Graz (Austria), Vienna (Austria), and Brno (Czechia).</p> <p>Gateway ID: <code>b827ebafac000001</code></p> <ul> <li>Uplink reception (end-device => gateway)</li> <li>Only packets containing CRC, inverted IQ</li> <li>RX0: 867.1 MHz, 867.3 MHz, 867.5 MHz, 867.7 MHz, 867.9 MHz - BW 125 kHz and all SF</li> <li>RX1: 868.1 MHz, 868.3 MHz, 868.5 MHz - BW 125 kHz and all SF</li> </ul> <p>Gateway ID: <code>b827ebafac000002</code></p> <ul> <li>Downlink reception (gateway => end-device)</li> <li>Includes packets without CRC, non-inverted IQ</li> <li>RX0: 867.1 MHz, 867.3 MHz, 867.5 MHz, 867.7 MHz, 867.9 MHz - BW 125 kHz and all SF</li> <li>RX1: 868.1 MHz, 868.3 MHz, 868.5 MHz - BW 125 kHz and all SF</li> </ul> <p>Gateway ID: <code>b827ebafac000003</code></p> <ul> <li>Downlink reception (gateway => end-device) and Class-B beacon on 869.525 MHz</li> <li>Includes packets without CRC, non-inverted IQ</li> <li>RX0: 869.525 MHz - BW 125 kHz and all SF, BW 125 kHz and SF9 with implicit header, CR 4/5 and length 17 B</li> </ul> <p>To open the <code>pcap</code> files, you need Wireshark with current support for LoRaTap and LoRaWAN protocols. This support will be available in the official 4.1.0 release. A working version for Windows is accessible in the <a href="https://www.wireshark.org/download/automated/win64/">automated build system</a>.</p> <p>The source data is available in the <code>log.zip</code> file, which contains the complete dataset obtained by the sniffer. A set of conversion tools for log processing is <a href="https://github.com/alpov/lorawan-sniffer">available on Github</a>. The converted logs, available in Wireshark format, are stored in <code>pcap.zip</code>. For the LoRaWAN decoder, you can use the attached root and session keys. The processed outputs are stored in <code>csv.zip</code>, and graphical statistics are available in <code>png.zip</code>.</p> <p>This data represents a unique, geographically identifiable selection from the full log, cleaned of any errors. The records from Brno include communication between the gateway and a node with known keys.</p> <p>Test file :: <code>00_Test</code></p> <ul> <li>short test file for parser verification</li> <li>comparison of LoRaTap version 0 and version 1 formats</li> </ul> <p>Brno, Czech Republic :: <code>01_Brno</code></p> <ul> <li>49.22685N, 16.57536E, ASL 306m</li> <li>lines 150873 to 529796</li> <li>time 1.8.2022 15:04:28 to 17.8.2022 13:05:32</li> <li>preliminary experiment</li> <li>experimental device <ul> <li>Device EUI: <code>70b3d5cee0000042</code></li> <li>Application key: <code>d494d49a7b4053302bdcf96f1defa65a</code></li> <li>Device address: <code>00d85395</code></li> <li>Network session key: <code>c417540b8b2afad8930c82fcf7ea54bb</code></li> <li>Application session key: <code>421fea9bedd2cc497f63303edf5adf8e</code></li> </ul> </li> </ul> <p>Liege, Belgium :: <code>02_Liege</code> :: <strong>evaluated in the paper</strong></p> <ul> <li>50.66445N, 5.59276E, ASL 151m</li> <li>lines 636205 to 886868</li> <li>time 25.8.2022 10:12:24 to 12.9.2022 06:20:48</li> </ul> <p>Brno, Czech Republic :: <code>03_Brno_join</code></p> <ul> <li>49.22685N, 16.57536E, ASL 306m</li> <li>lines 947787 to 979382</li> <li>time 30.9.2022 15:21:27 to 4.10.2022 10:46:31</li> <li>record contains OTAA activation (Join Request / Join Accept)</li> <li>experimental device: <ul> <li>Device EUI: <code>70b3d5cee0000042</code></li> <li>Application key: <code>d494d49a7b4053302bdcf96f1defa65a</code></li> <li>Device address: <code>01e65ddc</code></li> <li>Network session key: <code>e2898779a03de59e2317b149abf00238</code></li> <li>Application session key: <code>59ca1ac91922887093bc7b236bd1b07f</code></li> </ul> </li> </ul> <p>Graz, Austria :: <code>04_Graz</code> :: <strong>evaluated in the paper</strong></p> <ul> <li>47.07049N, 15.44506E, ASL 364m</li> <li>lines 1015139 to 1178855</li> <li>time 26.10.2022 06:21:07 to 29.11.2022 10:03:00</li> </ul> <p>Vienna, Austria :: <code>05_Wien</code> :: <strong>evaluated in the paper</strong></p> <ul> <li>48.19666N, 16.37101E, ASL 204m</li> <li>lines 1179308 to 3657105</li> <li>time 1.12.2022 10:42:19 to 4.1.2023 14:00:05</li> <li>contains a total of 14 short restarts (under 90 seconds)</li> </ul> <p>Brno, Czech Republic :: <code>07_Brno</code> :: <strong>evaluated in the paper</strong></p> <ul> <li>49.22685N, 16.57536E, ASL 306m</li> <li>lines 4969648 to 6919392</li> <li>time 16.2.2023 8:53:43 to 30.3.2023 9:00:11</li> </ul>
LoRaWAN for smart city IoT deployments: A long term evaluation
<p>Data set to accompany paper titled LoRaWAN for smart city IoT deployments: A long term evaluation</p>
Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
<p><strong>INTRODUCTION</strong></p> <p>The goal of these LPWAN datasets is to provide the global research community with a benchmark tool to evaluate fingerprint localization algorithms in large outdoor environments with various properties. An identical collection methodology was used for all datasets: during a period of three months, numerous devices containing a GPS receiver periodically obtained new location data, which was sent to a local data server via a Sigfox or LoRaWAN message. Together with network information such as the receiving time of the message, base station IDs' of all receiving base stations and the Received Signal Strength Indicator (RSSI) per base station, this location data was stored in one of the three LPWAN datasets: </p> <ul> <li> <p><strong>lorawan_dataset_antwerp.csv</strong></p> </li> <li> <p>130 430 LoRaWAN messages, obtained in the city center of Antwerp</p> </li> <li> <p><strong>sigfox_dataset_antwerp.csv</strong></p> </li> <li> <p>14 378 Sigfox messages, obtained in the city center of Antwerp</p> </li> <li> <p><strong>sigfox_dataset_rural.csv</strong></p> </li> <li> <p>25 638 Sigfox messages, obtained in a rural area between Antwerp and Ghent</p> </li> </ul> <p>As the rural and urban Sigfox datasets were recorded in adjacent areas, many base stations that are located at the border of these areas can be found in both datasets. However, they do not necessarily share the same identifier: e.g. ‘BS 1’ in the urban Sigfox dataset could be the same base station as ‘BS 36’ in the rural Sigfox dataset. If the user intends to combine both Sigfox datasets, the mapping of the ID's of these base stations can be found in the file:</p> <ul> <li> <p><strong>sigfox_bs_mapping.csv</strong></p> </li> </ul> <p>The collection methodology of the datasets, and the first results of a basic fingerprinting implementation are documented in the following journal paper:<br> <a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p> </p> <p><strong>UPDATES IN VERSION 1.3</strong></p> <p>We added the file <strong>lorawan_antwerp_gateway_locations.json.txt. </strong>As the filename suggests, this file contains the coordinates of the LoRaWAN gateways in Antwerp. (The .txt file type had to be appended, otherwise the file could not be uploaded to Zenodo).</p> <p> </p> <p><strong>UPDATES IN VERSION 1.2</strong></p> <p>In this version of the LPWAN dataset, only the LoRaWAN set has been updated. The Sigfox datasets remain identical to version 1.0 and 1.1. The main updates in the LoRaWAN set are the following:</p> <ul> <li> <p><strong>New data</strong>: the LoRaWAN messages in the new set are collected 1 year after the previous dataset version. To be consistent with the previous versions, the new LoRaWAN set is uploaded in the same .CSV format as before. This upload can still be found in this repository as ‘<strong>lorawan_dataset_antwerp.csv</strong>’.</p> </li> <li> <p><strong>More gateways</strong>: Compared to the previous dataset, 4 gateways were added to the LoRaWAN network. The RSSI of these gateways are shown in columns ‘BS 69’, ‘BS 70’,‘BS 71’ and ‘BS 72’. All other ‘BS’ columns are in the same order as in previous dataset versions.</p> </li> <li> <p><strong>More metadata: </strong>In the previous LoRaWAN dataset, metadata was limited to 3 receiving gateways per message. In the new dataset version, metadata from all receiving gateways is included in every message. Moreover, some gateways provide a timestamp with nanosecond precision, which can be used to evaluate Time Difference of Arrival localization methods with LoRaWAN.</p> </li> <li> <p><strong>2 file formats: </strong>As more metadata becomes available, we find it important to share the dataset in a clearer overview. This also allows researchers to evaluate the performance of LoRaWAN in an urban environment. Therefore, we publish the new LoRaWAN dataset as a .CSV file as described above, but also as a .JSON file (<strong>lorawan_antwerp_2019_dataset.json.txt,</strong> the .txt file type had to be appended, otherwise the file could not be uploaded to Zenodo) An example of one message in this JSON format can be seen below:</p> <ul> <li> <p>JSON format description:</p> <ul> <li> <p><strong>HDOP</strong>: Horizontal Dilution of Precision</p> </li> <li> <p><strong>dev_addr: </strong>LoRaWAN device address</p> </li> <li> <p><strong>dev_eui: </strong>LoRaWAN device EUI</p> </li> <li> <p><strong>sf: </strong>Spreading factor</p> </li> <li> <p><strong>channel: </strong>TX channel (EU region)</p> </li> <li> <p><strong>payload: </strong>application payload</p> </li> <li> <p><strong>adr: </strong>Adaptive Data Rate (1 = enabled, 0= disabled)</p> </li> <li> <p><strong>counter: </strong>device uplink message counter</p> </li> <li> <p><strong>latitude: </strong>Groundtruth TX location latitude</p> </li> <li> <p><strong>longitude: </strong>Groundtruth TX location longitude</p> </li> <li> <p><strong>airtime: </strong>signal airtime (seconds)</p> </li> <li> <p><strong>gateways:</strong></p> <ul> <li> <p><strong>rssi: </strong>Received Signal Strength</p> </li> <li> <p><strong>esp: </strong>Estimated Signal Power</p> </li> <li> <p><strong>snr: </strong>Signal-to-Noise Ratio</p> </li> <li> <p><strong>ts_type: </strong>Timestamp type. If this says "GPS_RADIO", a nanosecond precise timestamp is available</p> </li> <li> <p><strong>time: </strong>time of arrival at the gateway</p> </li> <li> <p><strong>id: </strong>gateway ID</p> </li> </ul> </li> </ul> </li> <li> <p>JSON example</p> <pre><code class="language-json">{ "hdop": 0.7, "dev_addr": "07000EFE", "payload": "008d000392d54c4284d18c403333333f04682aa9410500e8fd4106cabdbc420f00db0d470ce32ac93f0d582be93f0bfa3f8d3f", "adr": 1, "latitude": 51.20856475830078, "counter": 31952, "longitude": 4.400575637817383, "airtime": 0.112896, "gateways": [ { "rssi": -115, "esp": -115.832695, "snr": 6.75, "rx_time": { "ts_type": "None", "time": "2019-01-04T08:59:53.079+01:00" }, "id": "08060716" }, { "rssi": -116, "esp": -125.51497, "snr": -9.0, "rx_time": { "ts_type": "GPS_RADIO", "time": "2019-01-04T08:59:53.962029179+01:00" }, "id": "FF0178DF" } ], "dev_eui": "3432333853376B18", "sf": 7, "channel": 8 }</code></pre> <p> </p> </li> </ul> </li> </ul> <p> </p>
A dataset for RSSI based outdoor localization using LoRaWAN in a harbor as a harsh and industrial environment
<p>Enabling precise device localization is a critical requirement for the future of industry. Leveraging signal features for location determination has emerged as a leading approach and good alternative for Global Navigation Satellite Systems (GNSS) because of their limitations (low accuracy for indoor environments, expensive chips, and high energy consumption). On this basis, to provide localization for IoT in an industry with a harsh environment, the adopted wireless networks should have a long range coverage area. LoRaWAN is one of the most common communication networks that can provide large coverage with low power consumption and low implementation cost. Between various signal features that can be used for localization, Received Signal Strength (RSS) received more attention because of their low-cost deployment. But, RSS is highly dependent and sensitive to environmental changes, such as temperature, humidity, and background noise. This sensitivity becomes more intensive in an industrial environment with a harsh and dynamic environment. In order to evaluate the environmental effects on RSS in the harsh and highly dynamic industry, we present a comprehensive repository of LoRaWAN Received Signal Strength Indicator (RSSI) measurements, collected in a harbor as a testbed featuring three LoRaWAN gateways and one mobile end node. During the data collecting process, the mobile device obtains its location via a GPS and transmits it as the LoRaWAN message. In addition, to provide more insight of the effect of dynamic environment on the RSSI, two end nodes are implemented in fixed locations. These end nodes transmit messages with fixed time intervals including their unique id. The collected dataset includes RSSI and SNR measurements recorded by multiple gateways for each transmitted packet by fixed or mobile end nodes, and timestamp. This dataset enables the development and evaluation of RSSI-based localization and allows researchers to explore the challenges and opportunities associated with localization in dynamic IoT deployments.</p>
Raw results of the numerical experiments performed to evaluate different MAC schemes for LoRaWAN networks
<p>This zip file contains the scripts, gnuplot and data files needed to generate the figures showing the numerical results presented in [1].</p> <p>[1] S. Herrería-Alonso, A. Suárez-González, M. Rodríguez-Pérez and C. López-García, "Enhancing LoRaWAN scalability with Longest First Slotted CSMA," in <em>Computer Networks</em>, vol. 216, article number 109252, Oct. 2022, doi: 10.1016/j.comnet.2022.109252.</p>
LoRaWAN Measurement Campaigns in Lebanon
<p>LoRaWAN is a promising technology for the internet of things (IoT) in order to support low power and long-range communications. However, real-world deployment and network optimization require an accurate path-loss modeling in order to estimate network coverage and performance.</p> <p>We carried out extensive measurement campaigns in both indoor and outdoor environments considering urban and rural areas in three locations in Lebanon (Saint Joseph University campus, Beirut city, and Bekaa valley). We further consider 3 different antennas heights of the end-device (0.2m-3m). This dataset provides these measurements campaigns, which enable us to derive Path-loss (PL) models for Lora communication. A text file “Readme_data” is provided to describe the measurements in addition a Matlab function of the proposed model “Path-Loss-Model” is given.</p>
City scale particulate matter monitoring using LoRaWAN based air quality IoT devices
<p>Air Quality (AQ) is a very topical issue for many cities and has a direct impact on citizen health. The AQ of a large UK city is being investigated using low-cost Particulate Matter (PM) sensors, and the results obtained by these sensors have been compared with government operated AQ stations. In the first pilot deployment six AQ Internet of Things (IoT) devices have been designed and built, each with four different low-cost PM sensors, and they have been deployed at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide Area Network (LPWAN) coverage. The study concludes that i) the physical device developed can operate at a city scale ii) some low-cost PM sensors are viable for monitoring AQ and for detecting PM trends iii) LoRaWAN is suitable for city scale sensor coverage where connectivity is an issue. Based on the findings from this first pilot project a larger LoRaWAN enabled AQ sensor network is being deployed across the city of Southampton in the UK.</p>
LoED: The LoRaWAN at the Edge Dataset
<p>This repository contains dataset from nine LoRaWAN gateways collected in an urban environment. The dataset contains raw payload information, along with other metadata from the gateway. The dataset can be cited at:</p> <p><strong>Dataset: LoED: The LoRaWAN at the Edge Dataset</strong><br> Laksh Bhatia, Michael Breza, Ramona Marfievici, Julie A. McCann<br> Proceedings of the Third Workshop on Data Acquisition To Analysis (DATA '20), 2020</p> <p><strong>Files in the folder</strong></p> <ul> <li><strong>README.md</strong><br> A readme file detailing the gateway location, model and statistics about the data collected at each of the gateways.</li> <li><strong>Laksh_Bhatia_LoED_LoRaWAN_at_the_edge.pdf</strong><br> A readme file detailing the gateway location, model and statistics about the data collected at each of the gateways.</li> <li><strong>LoED_LoRaWAN_at_edge_dataset.zip</strong><br> A zip file containing all data files. Data files are in <strong>dd_mm_yyyy.csv</strong> format with one file for every day of the collection campaign</li> <li><strong>LoED_LoRaWAN_at_edge_dataset-SAMPLE.zip</strong><br> Sample folder with only six days of data collection.</li> <li><strong>LoED_parser.ipynb</strong><br> A jupyter notebook to generate statistics of the dataset</li> <li><strong>LoED_parser.html</strong><br> HTML rendering of the LoRaDatasetParser.ipynb notebook</li> <li><strong>LoED_parser.py</strong><br> A python file to generate basic statistics from the dataset</li> </ul>
LoRaWAN gateway performance and vehicle tracking data in AERPAW testbed
Open the record for dataset details and reuse information.
LoRaWAN for city scale IoT deployments
<p>Data set to accompany paper,</p>
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Allen Brain Atlas
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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.