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139 results for “iot”
Assessing the Linguistic Quality of REST APIs for IoT Applications
<p>Detection of Linguistic Antipatterns in IoT APIs:</p> <p>The dataset contains the 19 APIs for IoT applications and a subset of their URIs and documentation.<br> The dataset also contains the detection results of nine Linguistic Antipatterns in 19 IoT APIs.</p>
Dataset used for training IoT C&C classifier
<p>This dataset was used for training the IoT C&C classifier. It is provided in the form of extended bidirectional flow data. The flow data were generated by <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a> flow exporter and converted into CSV files. Apart from traditional flow information (IP addresses, ports, amount of transferred data), ipfixprobe was set with default timeouts (5 minutes active, 30 s inactive) to generate per-packet information for the first 30 packets. The flow records were then aggregated into 5-minute intervals - when the flow was split due to inactivity, the aggregator then stitched the flow back into a single one.</p> <p>The column headers in provided CSV files stand for:</p> <table> <thead> <tr> <th>Column Name</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>ipaddr DST_IP</td> <td>Source IP address</td> </tr> <tr> <td>ipaddr SRC_IP</td> <td>Destination IP address</td> </tr> <tr> <td>uint64 BYTES</td> <td>The number of transmitted bytes from SRC->DST</td> </tr> <tr> <td>uint64 BYTES_REV</td> <td>The number of transmitted bytes from DST->SRC</td> </tr> <tr> <td>time TIME_FIRST</td> <td>Timestamp of the first packet in the flow in format YYYY-MM-DDTHH-MM-SS</td> </tr> <tr> <td>time TIME_LAST</td> <td>Timestamp of the last packet in the flow in format YYYY-MM-DDTHH-MM-SS</td> </tr> <tr> <td>macaddr DST_MAC</td> <td>Destination MAC address</td> </tr> <tr> <td>macaddr SRC_MAC</td> <td>Source MAC address</td> </tr> <tr> <td>uint32 COUNT</td> <td>Number of aggregated flow records</td> </tr> <tr> <td>uint32 PACKETS</td> <td>The number of packets transmitted from Source to Destination</td> </tr> <tr> <td>uint32 PACKETS_REV</td> <td>The number of packets transmitted from Destination to Source</td> </tr> <tr> <td>uint16 DST_PORT</td> <td>Destination port</td> </tr> <tr> <td>uint16 SRC_PORT</td> <td>Source port</td> </tr> <tr> <td>uint8 DIR_BIT_FIELD</td> <td>Flag for distinguishin WAN(1)/LAN(0)</td> </tr> <tr> <td>uint8 PROTOCOL</td> <td>The number of transport protocol</td> </tr> <tr> <td>uint8 TCP_FLAGS</td> <td>Logic OR across all TCP flags in the packets transmitted SRC->DST</td> </tr> <tr> <td>uint8 TCP_FLAGS_REV</td> <td>Logic OR across all TCP flags in the packets transmitted DST->SRC</td> </tr> <tr> <td>int8* PPI_PKT_DIRECTIONS</td> <td>Array with packets' direction (1)- SRC->DST, (-1)-DST->SRC</td> </tr> <tr> <td>uint8* PPI_PKT_FLAGS</td> <td>Array with packets' TCP flags</td> </tr> <tr> <td>uint16* PPI_PKT_LENGTHS</td> <td>Array with packets' payload lengths</td> </tr> <tr> <td>time* PPI_PKT_TIMES</td> <td>Array with packets' timestamps</td> </tr> </tbody> </table> <p>Dataset consists of two parts: a benign part captured on the real ISP network and a malicious part captured in a lab environment.</p> <p><strong>Bening part captured on the real ISP network</strong><br> This part was created by packet capturing on the metering points located at the perimeter of the CESNET2 network. The metering points monitor 100 Gbps backbone peering lines used by approximately half a million users. We performed packet filtering based on ports for the capture. The CESNET training capture was used as benign traffic in the C&C model training and testing pipeline to cover potential nuances and variability of benign data seen in the ISP-level network. Since we deal with data from the production network,<br> we cannot guarantee a benign nature of all captured communication. However, we verified every IP address according to the internal blocklist of the CESNET association and external ones. We used <a href="https://www.abuseipdb.com">AbuseIPDB</a> and <a href="https://urlhaus.abuse.ch/)">URLhaus</a> blocklists.</p> <p>Since we are dealing with the real captures, the IP addresses, and MAC addresses<br> were anonymized.</p> <p><br> <strong>Malicious part created in the controlled lab-created environment</strong><br> From leaked source codes, we picked one variant from each of the most prevalent client-server IoT botnet families: (1) Tsunami, (2) Gafgyt, (3) Mirai. Each implements a distinct communication protocol; Tsunami is an example of an IRC bot; Gafgyt<br> uses a simple text-based protocol; Mirai implements a custom binary protocol. Afterward, we prepared virtualized testing environment.</p> <p>We deployed the malware in a controlled manner, filtering out its scanning and exploiting activities. The dataset covers the most notable C&C behavior. As previously recognized, the C&C communication consists of C&C heartbeat and<br> bot commands. Thus, for each of the three prepared malware variants, we first imagine the malware running with no received commands. That includes the initiation of the TCP connection to the C&C server, which continues for one hour. And then, we imagine the malware receiving commands from its C&C server. The position of the command packets is chosen arbitrarily relative to the background heartbeat packets because, in the real-world scenario, the timing of the commands is tied to a random human action.</p> <p><br> <strong>Directory tree of provided dataset</strong><br> </p> <pre><code>. ├── README.md ├── benign │ ├── AN_p20-21-25-143-3389.agg.head.csv │ ├── AN_p22.agg.head.csv │ ├── AN_p443.agg.head.csv │ ├── AN_p80.agg.head.csv │ └── AN_p8080.agg.head.csv └── cnc ├── kaiten │ ├── cnc.csv │ ├── command-01.csv │ ├── command-02.csv │ ├── command-03.csv │ ├── command-04.csv │ ├── command-05.csv │ ├── command-06.csv │ ├── command-07.csv │ └── command-08.csv ├── mirai │ ├── cnc.csv │ ├── command-01.csv │ ├── command-02.csv │ ├── command-03.csv │ ├── command-04.csv │ ├── command-05.csv │ ├── command-06.csv │ ├── command-07.csv │ └── command-08.csv └── qbot ├── cnc.csv ├── command-01.csv ├── command-02.csv ├── command-03.csv └── command-04.csv </code></pre> <p><strong>Acknowledgment</strong><br> This research was funded by the Ministry of Interior of the Czech Republic,<br> grant No. VJ02010024: Flow-Based Encrypted Traffic Analysis and also by the<br> Grant Agency of the CTU in Prague, grant No. SGS20/210/OHK3/3T/18 funded by<br> the MEYS of the Czech Republic.</p> <p> </p>
IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review
<p>Data collected from primary studies.</p> <p>We 1) identified the main approaches and services that are applied in the city and SGB-level SWM systems, 2) listed sensors and actuators and analyzed their application in various types of SWM systems, 3) listed the direct and indirect stakeholders of the SWM systems, 4) identified the types of data shared between the SWM systems and stakeholders, and 5) identified the main promising directions and research gaps in the field of SWM systems.</p>
Gerenciamento e Proteção do Serviço de Autenticação Adaptativa Social para Ambientes IoT Contra Ataques de Difamação - Dataset
<p>The IoT devices usually have limited computational, energetic and communication capabilities, and also they have to deal with challenges related to security and communication reliability. The IoT network employs authentication in order to maintain its security and the diversity of authentication mechanisms derived from the heterogeneity of the network has led to emergence of adaptive authentication systems. These systems resort to input factors in order to determine autonomously the suitable mechanism for a specific situation. However, current adaptive authentication approaches are not suitable for ad hoc networks, since they restrict themselves in using one input factor, such as context, and do not take into account other factors of the devices it interacts. In addition, they employ techniques that are not suitable to the devices processing restrictions. This master thesis investigated the benefits come from social perception, derived from the Social Internet of Things paradigm (SIoT), as adaptation factor in order to exchange services among IoT devices according to context compatibility. Thus, it is presented the GALENA system (manaGement of Adaptive authentication based on poLiciEs aNd sociAl trust), which decides the suitable authentication mechanism to apply in authentication procedures in IoT devices. The system address the devices’ computational restrictions employing policies that dictates the need for adaptation. Moreover, in order to prevent malicious behaviour aimed at the social recommendations exchanged, such as the badmouthing attack that seeks to diminish the trust over a device, GALENA employs Data Provenance techniques to authenticate the source of the recommendation. Thus, it is able to discard manipulated recommendations that aim to defame other devices. GALENA was evaluated through simulations in NS-3 and the results demonstrate its efficiency in adapting and selecting the suitable authentication mechanism each interaction. According to the research data, the system achieved a compatibility rate of 97% in an IoT network with 200 devices and 98% in one with 400 devices, with trust accuracy of 64% in both configurations. It also achieved a detection rate of the badmouthing attack up to 90,62% with 200 devices and up to 74,76% with 400 devices, with an detection accuracy of 91% and of 79% respectively. Further, the system reached a low false negative rate, around 10% with 200 devices and 31% with 400 devices.</p>
Artifact: Reflecting on Recurring Failures in IoT Development
<p>Artifact for Reflecting on Recurring Failures in IoT Development</p>
Tutorial for the 2022 ACM SIGMOD Conference: Spatial Data Quality in the IoT Era: Management and Exploitation
<p>Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications.</p>
Farm-Flow | AG-IoT Security: Intrusion Detection in Smart Agriculture Dataset
<div> <div> <p><strong>Introduction:</strong></p> <p>The "Farm-Flow" dataset was created to emulate real-world Agricultural Internet of Things (AG-IoT) systems, encompassing network attacks and data collection. Following comprehensive cleaning and processing, the "Farm-Flow" dataset comprises 532 MB of data with 1,310,000 instances, structured around "flows," which represent consecutive series of packets transmitted from a single source to a specific destination. The dataset demonstrates an intrusion detection accuracy of 92.67% and is intended to enhance the security of AG-IoT systems, safeguarding information such as crop health, weather patterns, and soil conditions</p> <p><strong>Captures:</strong></p> <p>The captures comprises three months of network traffic: August, September, and October of 2022. Each month is divided into folders, which categorize the network traffic. These folders contain numerous .pcap files, which have been divided into 5-second intervals. This segmentation is necessary because, as previously mentioned, flows aggregate packets, resulting in only one row of flow data for ongoing connections. To address this, a script was developed to segment the .pcap files into 5-second increments. This approach allows for the generation of multiple rows of flow connections, thereby providing more quantity of data for model training.</p> <p><strong>Dataset:</strong></p> <p>The dataset comprises 532 MB of data, encompassing 1,310,000 instances. These instances have been classified into eight distinct attack types and one category for normal traffic. The identified attacks include Arp Spoofing, BotNet DDoS, HTTP Flood, ICMP Flood, MQTT Flood, Port Scanning, TCP Flood, and UDP Flood. Among the data set, there are 27,458 instances of normal traffic and 1,282,429 instances of aggregated attack traffic.</p> <p><strong>Zip Folder:</strong></p> <p>The zip folder is structured into two main directories: Captures and Dataset. The Captures directory is organized by the month of capture and further categorized by network traffic type. The Datasets directory includes the Farm-Flow Dataset, alongside four additional datasets that have undergone pre-processing: the training and testing datasets for binary classification, and the training and testing datasets for multiclass classification. Additionally, there are further datasets categorized by month and type of network traffic.</p> <p><strong> Article Information:</strong></p> <p>The work involved in developing the Farm-Flow dataset is described in the following paper. Please cite the paper and the dataset when using the Farm-Flow dataset.</p> <blockquote> <p>Rafael Ferreira, Ivo Bispo, Carlos Rabadão, Leonel Santos, and Rogério Luís de C. Costa (2025). <em>Farm-flow dataset: Intrusion detection in smart agriculture based on network flows</em>, Computers and Electrical Engineering, Volume 121, 109892, DOI: <a href="https://doi.org/10.1016/j.compeleceng.2024.109892." target="_blank" rel="noopener"> 10.1016/j.compeleceng.2024.109892</a></p> </blockquote> </div> </div>
Failure Handling Website States for Potato GUSC IoT System
<p><span>Screenshots of the Statuses section of </span><span>the website in ERS-FH after sensor node software and sensor </span><span>failures occur. The screenshots of the website after the sensor </span><span>node hardware and lost data failures are shown in Figure 7 of the paper.</span></p>
Appendices for the ERS Elixir/Potato GUSC IoT Paper
<p>Note: all mentions of other figures/sections/tables are in reference to the paper.</p> <p><strong>website_sensor_node_software.png</strong> and <strong>website_sensors.png</strong> are screenshots of the Statuses section of the website in ERS-FH after sensor node software and sensor failures occur. The screenshots of the website after the sensor node hardware and lost data failures are shown in Figure 7.</p> <p><strong>full_code_coverage_barchart.png</strong> is a graph that provides the functional analysis for all seven codebases considered in the study, i.e. including the CWS and CRS codebases that use a non-idiomatic SQL DBMS, and hence are not suitable comparators for Section 4. The figure extends Figure 4.<br><em>Caption: "Comparing the percentage of code required to implement each functionality in tiered/tierless and resource-rich/constrained GUSC implementations."</em></p> <p><strong>full_sloc_table.png</strong> is a table that provides the comparative coverage analysis for all seven codebases considered in the study, i.e. including the CWS and CRS codebases. It extends Table 2 in Section 4. <br><em>Caption: "Comparing Tiered and Tierless GUSC Code Sizes for all seven codebases studied. PWS, PRS, CWS, CRS, CWS-NS, and CRS-NS values based on Table 2 in [7]. Original SQL-based CWS and CRS converted to idiomatic SDS-based CWS-NS and CRS-NS."</em></p>
Advanced IoT Agriculture 2024
<h3>Source Data with Authors</h3> <p>In the master's thesis research conducted by student Mohammed Ismail Lifta (2023-2024) at the Department of Computer Science, College of Computer Science and Mathematics- Tikrit University,Iraq.Data were collected from the Agriculture Lab on plants that grow in a IoT greenhouse and Traditional greenhouse .The study was supervised by Professor (Assistant) Wisam Dawood Abdullah, administrator of Cisco Networking Academy / Tikrit University.</p> <h3>Dataset Description</h3> <p>The dataset "Advanced_IoT_Dataset.csv" consists of 30,000 entries and 14 columns. Below are the detailed descriptions of each column:</p> <p>Random: An identifier for each record, likely indicating a random sample or batch (object type). Average of chlorophyll in the plant (ACHP): The average chlorophyll content in the plant (float type). Plant height rate (PHR): The rate of plant height growth (float type). Average wet weight of the growth vegetative (AWWGV): The average wet weight of vegetative growth (float type). Average leaf area of the plant (ALAP): The average leaf area of the plant (float type). Average number of plant leaves (ANPL): The average number of leaves per plant (float type). Average root diameter (ARD): The average diameter of the plant's roots (float type). Average dry weight of the root (ADWR): The average dry weight of the plant's roots (float type). Percentage of dry matter for vegetative growth (PDMVG): The percentage of dry matter in vegetative growth (float type). Average root length (ARL): The average length of the plant's roots (float type). Average wet weight of the root (AWWR): The average wet weight of the plant's roots (float type). Average dry weight of vegetative plants (ADWV): The average dry weight of vegetative parts of the plant (float type). Percentage of dry matter for root growth (PDMRG): The percentage of dry matter in root growth (float type). Class: The class or category to which the plant record belongs (object type).</p> <h3>More detailed description of the columns in the dataset:</h3> <p>Random: A categorical identifier for each record. This column appears to have values like R1, R2, and R3, which could represent different random samples.</p> <p>Average of chlorophyll in the plant (ACHP): This column contains float values representing the average chlorophyll content in the plant. Chlorophyll is vital for photosynthesis, and its measurement can indicate the health and efficiency of the plant in converting light energy into chemical energy.</p> <p>Plant height rate (PHR): This column contains float values representing the rate of growth in the height of the plant. This metric is essential for understanding the vertical growth dynamics of the plant over time.</p> <p>Average wet weight of the growth vegetative (AWWGV): This column contains float values representing the average wet weight of the vegetative parts of the plant. Wet weight can be an indicator of the water content and overall biomass of the plant's vegetative growth.</p> <p>Average leaf area of the plant (ALAP): This column contains float values representing the average leaf area of the plant. Leaf area is a critical factor in photosynthesis, as it determines the surface area available for light absorption.</p> <p>Average number of plant leaves (ANPL): This column contains float values representing the average number of leaves per plant. The number of leaves can correlate with the plant's ability to perform photosynthesis and its overall health.</p> <p>Average root diameter (ARD): This column contains float values representing the average diameter of the plant's roots. Root diameter can affect the plant's ability to absorb water and nutrients from the soil.</p> <p>Average dry weight of the root (ADWR): This column contains float values representing the average dry weight of the plant's roots. Dry weight is a measure of the plant's biomass after removing water content and is an indicator of the root's structural and storage capacity.</p> <p>Percentage of dry matter for vegetative growth (PDMVG): This column contains float values representing the percentage of dry matter in the vegetative parts of the plant. This metric indicates the proportion of the plant's biomass that is not water, which can be crucial for understanding its structural and nutritional status.</p> <p>Average root length (ARL): This column contains float values representing the average length of the plant's roots. Root length can influence the plant's ability to explore and absorb nutrients and water from the soil.</p> <p>Average wet weight of the root (AWWR): This column contains float values representing the average wet weight of the plant's roots. Wet weight includes the water content in the roots, indicating their overall biomass and water retention capacity.</p> <p>Average dry weight of vegetative plants (ADWV): This column contains float values representing the average dry weight of the vegetative parts of the plant. This measure reflects the structural biomass of the plant without water content.</p> <p>Percentage of dry matter for root growth (PDMRG): This column contains float values representing the percentage of dry matter in the plant's roots. This metric shows the proportion of the root biomass that is not water, important for assessing root health and function.</p> <p>Class: A categorical column indicating the class or category to which the plant record belongs. This could represent different groups or conditions under which the plants were studied or classified.</p> <p>The dataset provides comprehensive information about various plant metrics related to both vegetative and root growth, along with classification labels that could be used for analysis or machine learning purposes.</p> <h3>How to Use</h3> <p>This data can be used for environmental research and studies. Proper attribution must be given when using this data in any publication.No Change the dataset.</p> <h3>Contact</h3> <p>For more information or inquiries, please contact the principal researcher: Professor ( Assistant) Wisam Dawood Abdullah (Email: wisamdawood@tu.edu.iq).</p>
An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting
<p>This repository is related with the next paper. Please cite us if this code is useful to you.</p> <p>Barral, V., Dominguez-Bolano, T., Escudero, C. J., & Garcia-Naya, J. A. An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting.</p> <h2>Python Scripts</h2> <ul> <li><em>count_plot.py</em>: Generates a plot comparing image tracking estimation, radar with DBSCAN, and radar with OPTICS. Use example:</li> </ul> <pre><code>python count_plot.py count_video_full.log count_radar_moving_average_30_optics.log count_radar_moving_average_30_dbscan.log "Video" "Radar with OPTICS" "Radar with DBSCAN" average_count_full_optics_dbscan.pdf </code></pre> <h2>Radar measurements</h2> <ul> <li><em>test_0.bag</em>: FMCW mmWave radar measurements from three IWR6843 devices (ISK and AOP). Is a ROS (Robotic Operative System) log, can be played with <code>rosbag play test_0.bag</code></li> </ul> <h3>Radar people counting logs</h3> <ul> <li> <p><strong>count_radar_moving_average_30_dbscan.log</strong>: radar people counting estimation using a 30 seconds moving average and DBSCAN as clustering algorithm.</p> </li> <li> <p><strong>count_radar_moving_average_30_optics.log</strong>: radar people counting estimation using a 30 seconds moving average and OPTICS as clustering algorithm.</p> </li> </ul> <h2>Image tracking</h2> <h3>Videos</h3> <ul> <li><em>test_0_cam_0_anonymized.mp4</em>: Camera 0 capture.</li> <li><em>test_0_cam_1_anonymized.mp4</em>: Camera 1 capture.</li> </ul> <h3>Image tracking logs</h3> <ul> <li><em>test_0_cam_0_interp.txt</em>: People count in camera 0</li> <li><em>test_0_cam_1_interp.txt</em>: People count in camera 1</li> </ul> <p>The files have the following format:</p> <p>frame_number, track_id, bb_left, bb_top, bb_width, bb_height, conf, x, y, z The coordinates x, y, z are always -1 The bb fields define the bounding box of the detection If multiple people are detected in a frame, there are several lines with that frame_number, but with different track_ids.</p> <h3>Image people counting logs</h3> <ul> <li><strong>count_video_full.log</strong>: image tracking people counting estimation using a 30 seconds moving average.</li> </ul>
IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis
<p>Included here are individual burn maps used for evaluating algorithm results in the paper: IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis. This paper will be presented at the IEEE World Forum on Internet of Things in November 2024 and available on IEEE Xplore after that.</p> <p>Also included are maps of fuel load and elevation (geotifs) and the daily weather (in .csv format) for the region of interest used in the burn probability simulator Burn-P3+ to generate the individual burn maps.</p> <p>This paper investigates various algorithms for distributing Internet of Things sensors within the Wildland-Urban Interface to enhance early wildland fire detection. Utilizing geospatial data analysis and a validated wildland fire growth model burn maps were generated to guide sensor placement strategies across a defined region of interest. The algorithms evaluated include an even grid distribution, random distributions, and genetic algorithm-based methods. Each algorithm was tested against 50,000 selected burn maps to assess detection rates, with sensor counts ranging from 50 to 800 across 500 experimental runs. Results indicate that while the even grid distribution yielded the highest detection rates, the practicality of such a method in real-world applications is limited. Genetic algorithms showed promise, but require further exploration to more accurately simulate random distribution used in field deployment. Surprisingly, weighting sensor placement based on wildland fire growth risk did not significantly impact detection effectiveness, suggesting the need for additional research into the representativeness of selected burn maps.</p> <p>Partial code for the sensor deployment algorithms discussed in the above mentioned paper is <a href="https://github.com/richardjpurcell/sensor-deployment-algorithms">available on GitHub</a>.</p>
Design and Implementation of a flexible Node for IoT supporting 6loWPAN and a Sensor Shield for Home Automation Application
<p>Simulation data and measurment of the developed flexible IoT board.</p>
A taxonomy for improving industry-academia communication in IoT vulnerability management. Additional Material
<p>Research interview and Workshop Protocol from the paper "A taxonomy for improving industry-academia communication in IoT vulnerability management".</p>
Dataset of Fault-Injection experiments in a publisher/subscriber IoT system
<p>Dataset of Fault-Injection experiments in a publisher/subscriber IoT system with <a href="https://github.com/SIGNEXT/instrumentable-aedes">github/instrumentable-aedes</a>.</p> <p>The file contains data related to the used Node-RED flows, sanity checks and scenarios.</p>
Artifacts for the IEEE Internet of Things Journal Publication: Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT
<p>Artifacts for the evaluation of the publication <em>Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT </em>which will be published in the IEEE Internet of Things journal. More information is available in the provided README.md file.</p>
Artifact: Incorporating Failure Knowledge into Design Decisions for IoT Systems: A Controlled Experiment on Novices
<p>Artifact for "Incorporating Failure Knowledge into Design Decisions for IoT Systems: A Controlled Experiment on Novices"</p>
Leveraging IoT Data Stream for Near-Real-Time Calibration of City-Scale Microscopic Traffic Simulation
<p>This repository includes input and output data of the methodology presented in the <a href="https://arxiv.org/abs/2210.17315">paper</a> for generating a calibrated dynamic microscopic traffic simulation.</p> <ul> <li>The input data includes the network, initial normalized origin-destination matrix, and hourly traffic counts from stationary city sensors.</li> <li>The output is a 24-hour calibrated microscopic traffic simulation for the city of Tartu, Estonia.</li> </ul> <p>All source codes are available at <a href="https://github.com/Khoshkhah/NRTCalib">https://github.com/Khoshkhah/NRTCalib</a>.<br> </p>
IoT network traffic dataset using the custom flow representation
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Measurement data of the industrial IoT scenario
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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.