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139 results for “iot”
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>
Analyzed papers in an open IoT platform systematic mapping study
<p>As part of our systematic mapping study related to Open IoT Platforms, here we present the overall included and analyzed papers for our study. As a process, the papers are classified into five categories, such as used, indicated, proposed, defined and no explanation studies.</p> <p>The result of the classification process is: -use: 134 papers, -indicate: 56 papers -propose: 28 papers, -define: 3 papers, and -no explanation: 59 papers</p>
IoT device identification - Cut and dimensionality reduced measurements
<p>This repository contains:</p> <p>- Cut measurements corresponding to all the original data, but reduced in bandwidth and augmented as described in the associated paper</p> <p>- The PCA dimensionality reduced data based on the single and multi user data.</p> <p>Frequency: 863-870 MHz (center 866,5 MHz)</p> <p>Sample Frequency: 10 MSPS</p> <p>Date of measurement: 15 November 2018</p> <p>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</p>
IoT device identification - Multi user data
<p>Artificial multi user observations generated as described in the associated paper Section III.</p> <p>Frequency: 863-870 MHz (center 866,5 MHz)</p> <p>Sample Frequency: 10 MSPS</p> <p>Date of measurement: 15 November 2018</p> <p>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</p>
Dataset for: Buffer Management for Trust Computation in Resource-constrained IoT Networks
<p>Dataset for Buffer Management for Trust Computation in Resource-constrained IoT Networks</p>
Empowering Coffee Farming Using Counterfactual Recommendation based RNN-IoT Integrated Soil Fertility Control System
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IoTS_Dataset: QoS data about IoT services
<p>This data set is a QoS data set of iot services generated by a random algorithm. Iot services have the following QoS attributes: execution time, service cost, reputation, reliability. In this dataset, each QoS attribute value is randomly generated by a random algorithm within the value range of the attribute. The dataset is divided into data of different iot service scales, namely IoTS10X50, IoTS10X100, IoTS20X50, IoTS20X100, IoTS30X50 and IoTS30X100. The dataset consists of the following parts:</p> <p>IoTS10X50: There are 10 Excel data files representing 10 tasks, each task is equivalent to the abstract IoT service, and there are 50 candidate IoT services in each task (that is, each abstract IoT service), that is, 50 functionally identical or similar but non-functionally (QoS) different IoT services.</p> <p>IoTS10X100: There are 10 Excel data files representing 10 tasks, each task is equivalent to abstract IoT services, and in each task, that is, each abstract IoT service, there are 100 candidate IoT services, that is, 100 functionally identical or similar but non-functional (QoS) Different IoT services.<br>IoTS20X50: There are 20 Excel data files representing 20 tasks, each task is equivalent to abstract iot services, and within each task (that is, each abstract iot service) there are 50 candidate iot services, that is, 50 functionally identical or similar but non-functionally (QoS) different iot services.<br>IoTS20X100: There are 20 Excel data files representing 20 tasks, each task is equivalent to abstract iot services, and in each task, that is, each abstract iot service, there are 100 candidate iot services, that is, 100 A number of iot services with the same or similar functionality but different non-functional (QoS).<br>IoTS30X50: There are 30 Excel data files representing 30 tasks, each task is equivalent to abstract iot services, and within each task (that is, each abstract iot service) there are 50 candidate iot services, that is, 50 functionally identical or similar but non-functionally (QoS) different iot services.<br>IoTS30X100: There are 30 Excel data files representing 30 tasks, each task is equivalent to abstract iot services, and in each task, that is, each abstract iot service, there are 100 candidate iot services, that is, 100 A number of iot services with the same or similar functionality but different non-functional (QoS).</p>
CoNEXT21: Mind the Gap: Multi-hop IPv6 over BLE in the IoT - Experiment Result Data
<p>This dataset contains the all raw experiment data that was used in our paper "Mind the Gap: Multi-hop IPv6 over BLE in the IoT" published at CoNEXT21.</p>
Continuous and proactive software architecture evaluation: An IoT case -- Dataset generated from iFogSim
<p>There will always be a trade-off between using the simulators and physical IoT devices in experimentation and data generation. This is due to the high cost of the actual deployment of IoT devices as compared to simulators. However, some companies, such as Amazon, IBM, and Intel, are motivating the need for having IoT simulation instrumenting what-if test scenarios, typically used during the architecture analysis and refinement stages to evaluate the response and sensitivity of the architecture to these tests. </p> <p>Additionally, many researchers are currently looking for an IoT dataset that provides QoS for IoT architectures. This work provides a dataset well-tested for the most important quality attributes when evaluating IoT architectures.</p> <p>In particular, this work used iFogSim to generate QoS of various IoT architectures in the form of Response Time, Energy consumption, and network usage. After that, MOA framework was used to generate the Forecast QoS values using different time series forecasting algorithms.</p>
Replication Package - IoT Roadmap Feasibility Study
<p>Replication package for a Feasibility Study.</p> <p>GOAL: This work aims to report a study with software engineers to characterize an IoT Roadmap's viability, considering the artifacts generated in the context of IoT software systems design already concluded.</p>
Replication Package - IoT Roadmap Observation Study
<p>Replication package for a Observation Study.</p> <p>GOAL: This work aims to analyze the use of the IoT Roadmap with the purpose of understanding in relation to its applicability from the point of view of junior software engineers in the context of the IoT project for COVID-19 developed at the Federal University of Rio de Janeiro.</p>
Dataset used for training IoT C&C classifier
<p>No description provided.</p>
IoT Agriculture 2014
<h3>Data Sources 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 was collected from a smartly-equipped greenhouse. The study was supervised by Assistant Professor Wissam Dawood Abdullah, Director of the Cisco Networking Academy at Tikrit University. It involved the construction of a smart greenhouse equipped with advanced technologies for monitoring and controlling environmental conditions. The study included an application that links data to Google Sheets for remote monitoring and control, providing an effective platform for efficient management of the greenhouse. ( 13 features , 37923 Row)</p> <h3>Columns and Data Types:</h3> <p>date (datetime64): The date and time the measurements were recorded.<br>temperature (int64): The recorded temperature in degrees Celsius.<br>humidity (int64): The percentage of humidity in the environment.<br>water_level (int64): The water level as a percentage.<br>N (int64): The nitrogen level in the soil, scaled from 0 to 255.<br>P (int64): The phosphorus level in the soil, scaled from 0 to 255.<br>K (int64): The potassium level in the soil, scaled from 0 to 255.<br>Fan_actuator_OFF (float64): Indicator for the fan actuator if it is off (0 or 1).<br>Fan_actuator_ON (float64): Indicator for the fan actuator if it is on (0 or 1).<br>Watering_plant_pump_OFF (float64): Indicator for the plant watering pump if it is off (0 or 1).<br>Watering_plant_pump_ON (float64): Indicator for the plant watering pump if it is on (0 or 1).<br>Water_pump_actuator_OFF (float64): Indicator for the water pump actuator if it is off (0 or 1).<br>Water_pump_actuator_ON (float64): Indicator for the water pump actuator if it is on (0 or 1).</p> <h3>Additional Details:</h3> <p>The data was cleaned by removing duplicate rows and missing values.<br>Categorical columns were encoded using One-Hot Encoding technique to facilitate the use of the data in machine learning.<br>The file is ready for analysis and modeling using machine learning tools.</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>
DIVINE Pilot 4 - On Farm - Davis Instruments Weather Station and IoT sensors Datasets - (June 23 - May 24)
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Exploring the CI/CD Pipeline in FLOSS Repositories of IoT Embedded Systems
<p>Spreadsheets, scripts, and graphs.</p> <p>Data for responses from the first round of review.</p>
Figure 1: Comparison of Redactable Blockchain Implementation for IoT Applications
<p>This figure presents a comparison of redactable blockchain implementations for IoT applications.</p> <p>This file contains a high-resolution version of figure 1 from the paper "Redactable Blockchain Solutions for IoT: A Review of Mechanisms and Applications". The file includes detailed data that is difficult to read in the main manuscript. For the full context and additional information, please refer to the main manuscript.</p> <p> </p>
IoT Sensor based Sports Activity Monitoring using Skill Inheritance and Optimization based on AI
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Energy Consumption of IoT Monitoring Software Architectures in the Edge
<p>Data repository with the raw and synthesized data of the paper published in the ECSA 2024 "<strong>Energy Consumption of IoT Monitoring Software Architectures in the Edge</strong>"</p> <p>This repository presents the experimental results of an exploratory study that measures the energy consumption of four Edge software architecture configurations of an indoor environmental monitoring IoT system. This dataset provides the raw measurements, the data analysis and the results comparison of the four architectures.<br>This repository is composed of five folders. Their content is explainded following:<br>- AdditionalMetrics: It includes the raw data obtained from the 24 experiments that are not used for calculating the energy consumption but it was provided by the measurement tools.<br>- BasalEnergyConsumption: It includes the raw data of the experiments launched to measure the basal consumption of the Smart Gateway. In addition, the excel file with the calculation of the Basal Energy Consumption is also provided.<br>- DataSynthesis: The data analysis and synthesis from the results of energy consumption are included in this folder. <br>- EnergyMeasurementExperiments: It includes the raw energy consumption data obtained from the 24 experiments.<br>- Figures: It includes the figures generated from the data obtained.</p>
Development of an IoT-Based Early Warning System in Irrigation Channels to Supports Sustainable Environmental Management in Yogyakarta
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
A Framework for Improving Social Inclusion using Network Analysis and IoT-based Contact Tracing, Dataset and Source Code
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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.