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56 results for “internet of things”
Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things
<p>The data-set presents normal and anomalous values of twelve traffic parameters, generated by <strong>Bioluminescent bio-cyber Interfacing </strong>(BBI) in the I<strong>nternet of Bio Nano Things </strong>(IoBNT) based systems.</p> <p>The traffic parameters included in the data-set represent bio-electric and electro-bio transduction unit operation of BBI incorporating normal, as well as abnormal data to train and test machine/deep learning classifiers in discriminating attack scenarios.</p> <p>The parameters considered include the following: <strong>Cumulative concentration of released molecules, Elimination rate, Michaelis-Menten constant, Kinetic constant, Forward rate constant, Catalytic reaction constant, Ligand-receptor binding constant, Concentration of ATP, Concentration of information molecules, Release rate Reverse kinetic constant,</strong> and <strong>Reverse forward rate constant.</strong></p> <p>The data set is divided into training and testing data for simplified analysis, and application.</p>
Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"
<p>As the 3rd generation partnership project (3GPP) organization pushes out new releases,<br>positioning in heterogeneous mobile networks enables the achievement of the accuracy required<br>in the majority of industrial applications without dependence on global navigation<br>satellite systems (GNSS). This study presents the results gathered during an extensive measurement<br>campaign related to the practical applicability of localization in next-generation<br>heterogeneous networks. We present an accuracy comparison of basic timing advance (TA)<br>localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF),<br>extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural<br>network. Our results demonstrate that TA cannot be considered an optimal solution<br>from the perspective of localization accuracy because the error roughly corresponds to the<br>average separation distance from the base station (BS) to the end device (ED). In addition,<br>we found that the LSTM approach is not optimal for the outdoor localization of moving<br>ED because of the combination of multiple factors, with sparse deployment being the most<br>important. The median value of the location error of the LSTM was more than 200m higher<br>than that of the TA for the self-validation dataset. However, a simple KNN regression shows<br>solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN<br>provided the most accurate results of all methods, with median error values of approximately<br>12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.</p>
Leaflet Engage IoT Societal Engagements with the Internet of Things
<p>Leaflet of the project Engage IoT, funded by the Fundação para a Ciência e a Tecnologia (EXPL/SOC-SOC/1375/2021), in the shape of a fortune teller.</p> <p>Website of the project: https://engageiot.wordpress.com/</p>
Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices
<p>This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices"</p> <p>With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.</p> <p>For more detail about this dataset, please refer to the paper listed above.</p>
Performance measurements for in-depth energy analysis of security algorithms and protocols for the Internet of Things
<p>Performance dataset of cryptographic algorithms running on the following embedded devices (results in ms):</p> <p><strong>nuc </strong>The NUCLEO-L073RZ is a STM32 Nucleo-64 Development Board of STMicroelectronics. It features the STM32L073RZT6 32~MHz ARM Cortex-M0+ microcontroller with 192~KB flash memory and 20~KB RAM.<br> <strong>msp </strong>The TI SimpleLink MSP-EXP432P401R development kit uses the MSP432P401R 48~MHz ARM Cortex-M4F microcontroller with 256~KB flash and 64~KB RAM.<br> <strong>max </strong>The MAXREFDES\#100 health sensor platform features the MAX32620 96~MHz ARM Cortex-M4F microcontroller with 2~MB flash and 256~KB RAM. It has a wide range of sensors, like a human body temperature sensor and a heart rate sensor.</p> <p>The measured cryptographic operations:</p> <ul> <li><strong>The basic arithmetic operations for elliptic curve cryptography </strong>(point addition~(PA), point doubling~(PD), point multiplication~(PM), and fixed-point multiplication~(PMG))</li> <li><strong>The AES symmetric-key cipher in five modes of operations</strong> (Electronic Codebook (ECB), Cipher Block Chaining (CBC), Counter (CTR), Counter with CBC-MAC (CCM), and Galois/Counter Mode (GCM))</li> <li><strong>Hash functions </strong>(SHA256 and SHA3-256)</li> </ul> <p>The performance of all identified basic operations is measured on the three platforms. 50 time measurements are done for each basic operation using the platforms' available timer. Moreover, the AES cipher operation is an encryption on 256 Bytes of data. We have chosen a multiple of the AES block size, because, longer time periods ensure less influence of potential timing inaccuracies like an early start and late end. For the hash function, the maximum input size of the respective algorithm for one round is chosen as follows: 55~B for SHA256 and 135~B for SHA3-256. The total available internal state size is not used for SHA256 and SHA3-256, as we take into account the minimal padding or suffix that is required for the last block of input data. Note that the most optimal scenario, i.e. the maximum amount of input data to fill up the internal state completely, is used for each of the operations.</p> <p>All basic operations are implemented using software libraries and cross-compiled with the GNU Tools for ARM Embedded Processors version 6-2017-q2-update. Furthermore, the compiler is configured to optimise for size (-Os). The RELIC-toolkit library is used to implement the EC arithmetic and the SHA256 hash function. We use the SECG K-256 prime elliptic curve, BASIC;COMBA;COMBA;MONTY;MONTY;SLIDE configuration for the prime field arithmetic, and PROJC;LWNAF;COMBS;INTER}} configuration for the prime elliptic curve arithmetic. For more information on how to configure RELIC and other examples that use it, we refer to the relic-toolkit wiki. The AES ciphers are implemented using Mbed TLS and SHA3 using wolfCrypt. We use the SHA3-256 hash function as specified in FIPS PUB 202.</p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Dataset: Global X Internet of Things ETF (SNSR) 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.
Internet of Things from a Business Perspective - The IoT Literature Classification Framework
<p>The Internet of Things (IoT) is an important development in the context of business information systems. The IoT, as a foundation for cyber-physical information systems, has similar potential for revolutionizing business as cloud computing over the last decade. The purpose of the current article is to formulate and apply a framework in order to take a quantitative snapshot of current literature on the IoT from a business perspective. Our results give an overview on important areas and will support future studies in research and practice. </p> <p>This dataset contains the IoT literature classification framework as MS Excel file.</p>
Research data from the two surveys on IoT implementation for Article "User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe"
<p>The file includes data collected through two online surveys linked to the article "User and Professional Aspects for Sustainable Computing Based on the nternet of Things in Europe" published by journal Sensors in January 2023:</p> <ul> <li>Survey on factors that inlfuence IoT Adoption by non technical users</li> <li>Survey on recommended profile focused on IoT implementation for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician.</li> </ul>
Machine Learning-based Energy Optimisation in Smart City Internet of Things
<p>Dataset for the paper Machine Learning-based Energy Optimisation in Smart City Internet of Things accepted for publication at The First International Workshop on the Integration between Distributed Machine Learning and the Internet of Things, ACM MobiHoc 2023.</p> <p>The dataset is collected from a real-world deployment of environmental sensors in the city of Bern, Switzerland. Our proposed approach can be applied to determine the tradeoff between the accuracy of temperature measurements and reducing the energy consumption for a single sensor; hence, without loss of generality, the evaluation is conducted on a dataset from a single sensor. Overall, we acquired 3697 measurements, each long 138 seconds. To correct the measurements, we set the maximum ventilation duration of 138 seconds, during which the multivariate time series of humidity and temperature sensor values are recorded together with their corresponding timestamps. The sensor values are recorded at a fixed frequency.</p> <p>From this raw data, we created the training and test sets through data augmentation to simulate time series of different lengths. Namely, for each measurement, we generated 136 samples with the increasing length of measurement time-series, padding the residual time-series length with zeros until reaching a time-series length of 137.</p> <p>We released the source code and trained models on the following GitHub repository https://www.github.com/ricsamikwa/ml-iot-smartcitytemp</p>
Predicting time of failure of Internet of Things devices using Bayesian workflow
<p>Repository includes the environment details in ```energies_iot_env.yml``` file.</p> <p>All code for model analysis is included in the ```iot_tests_refactor.ipynb``` notebook.</p> <p>Code for computing simulation based calibration is in the ```compute_sbc.py``` file, and can be run by ```just_csv.ipynb``` notebook.</p> <p> </p> <p>The data set was created in the project NCN OPUS "Process Fault Prediction and Detection" (UMO-2021/41/B/ST7/03851)</p>
Public Defense of Doctoral Thesis: "Improving the Resilience of the Constrained Internet of Things" (raw video)
<p>This is the raw video footage of Renzo E. Navas' public PhD Thesis Defense: "Improving the Resilience of the Constrained Internet of Things". Original date: Wednesday 9th of December 2020.</p>
Raw Measurements for Low Delay Video Streaming on the Internet of Things Using Raspberry Pi
<p>Raw measurements for article "Low Delay Video Streaming on the Internet of Things<br /> Using Raspberry Pi"</p>
Internet of Things - transforming businesses, people's lives and driving growth in the coming years
<p><b>Abstract</b></p><p class="dhik-abstract-content">IoT is the biggest computer revolution and will transform businesses, people's lives and drive growth in the coming years. We discuss novel IoT systems, technologies, and future trends to increase productivity, efficiency and quality in manufacturing, smart buildings, energy and wireless industries.</p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li><b>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (<a href="#collapseTwo">Video</a>)</b></li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (DOI:<a href="https://zenodo.org/record/7123816">10.5281/zenodo.7123816</a>)</li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Available Wireless Sensor Network and Internet of Things testbed facilities: dataset
<p>In this data set, we present data collected for the purpose of carrying out a systematic review of the available Wireless Sensor Network and Internet of Things testbed facilities. The data was collected through multiple stages and in each stage the pre-defined criteria were applied. We provide a dataset describing the hardware and software aspects of Wireless Sensor Network and Internet of Things testbed facilities available in the market and scientific community. The data were gathered through an extensive systematic review process of scientific articles published between the years 2011 and 2021. The review aims to obtain good quality data for people who are actively researching the Internet of Things facilities or anyone who is interested in that field.</p>
Dataset: User side acquisition of People-Centric Sensing in the Internet-of-Things
<p>- This archive contains the files submitted to the 2nd International<br> Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files<br> provided in this package are associated with the paper titled<br> "Dataset: User side acquisition of People-Centric Sensing in the<br> Internet-of-Things"</p> <p>- Content of the package:</p> <p> + 1_beacon_table.pkl: The beacon table in Pickle format. It contains<br> 20612286 data points where each data point represents a Bluetooth<br> beacon with 15 attributes as follows: <_id, host_id, ble_address,<br> sound_avg_peak, sound_max_peak, sound_count_over_thres_per_frame,<br> sound_avg_all, sound_avg_over_thres, temperature, humidity,<br> pressure, eco2_ppm, tvoc_ppb, rssi, timestamp>.</p> <p> + 2_device_description_table.pkl: The device description table<br> provides the mapping between a device's Bluetooth address and its<br> physical identity (device_id, description, type).</p> <p> + 3_checkin_table.pkl: The check-in table provides a timeseries of<br> user interactions with three Android tablets (i.e. tuples of <time,<br> host_id, checkpoint device>).</p> <p> + 4_sample_beacon_table.pkl: The sample beacon table in Pickle<br> format. It contains 1000 data points where each data point<br> represents a Bluetooth beacon with 15 attributes as follows: <_id,<br> host_id, ble_address, sound_avg_peak, sound_max_peak,<br> sound_count_over_thres_per_frame, sound_avg_all,<br> sound_avg_over_thres, temperature, humidity, pressure, eco2_ppm,<br> tvoc_ppb, rssi, timestamp>.</p> <p> + 5_sample_device_description_table.pkl: The sample device description<br> table provides the mapping between a device's Bluetooth address and<br> its physical identity (device_id, description, type).</p> <p> + 6_sample_checkin_table.pkl: The check-in table provides a<br> timeseries of user interactions with three Android tablets<br> (i.e. tuples of <time, host_id, checkpoint device>).</p> <p> + print_table_heads.py: A Python script which fetches Pickle tables<br> as DataFrames and prints out the sample entries.</p> <p><br> - ACM Reference Format: Chenguang Liu, Jie Hua, Tomasz Kalbarczyk,<br> Sangsu Lee, and Christine Julien. 2019. Dataset: User side<br> acquisition of People-Centric Sensing in the Internet-of-Things. In<br> The 2nd Workshop on Data Acquisition To Analysis(DATA’19), November<br> 10, 2019, New York, NY, USA. ACM, New York, NY, USA, 3 pages.<br> https://doi.org/10.1145/3359427.3361914</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>
Code and Training Data for "Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands based on In-situ Internet of Things Monitoring"
Open the record for dataset details and reuse information.
Data of Black-Box Side-Channel Detection and Mitigation for Internet of Things
<p>This repository contains the datasets used in the experiments of the paper "Black-Box Side-Channel Detection and Mitigation for Internet of Things" and the results of the experiments.</p>
Smart Pain Assesment Tool Based on Internet of Things
ClinicalTrials.gov study NCT03061240. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
ScienceDex guides
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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.