IoT
This dataset comprises a range of features, including time slots, device IDs, geographic coordinates (x, y), energy consumption, uplink history, emergency status, QoS pool identifiers, data flags, resource IDs, and data sizes. The device locations are modeled using a Poisson distribution with a spread of \(100\) meters within a \(500 \times 500\) meter area. The uplink history, QoS pool assignments, and data flags are derived from the probabilities of data availability and priority values.
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This paper presents an innovative Internet of Things (IoT) system that integrates gas sensors and a custom Convolutional Neural Network (CNN) to classify the freshness and species of beef and mutton in real time. The CNN, trained on 9,928 images, achieved 99% accuracy, outperforming models like ResNet-50, SVM, and KNN. The system uses three gas sensors (MQ135, MQ4, MQ136) to detect gases such as ammonia, methane, and hydrogen sulfide, which indicate meat spoilage.
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The TiHAN-V2X Dataset was collected in Hyderabad, India, across various Vehicle-to-Everything (V2X) communication types, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Infrastructure-to-Vehicle (I2V), and Vehicle-to-Cloud (V2C). The dataset offers comprehensive data for evaluating communication performance under different environmental and road conditions, including urban, rural, and highway scenarios.
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This is an IMU dataset of human arm motions.
Users wear a smart watch that is equipped with IMU sensors.
A VR controller is attached to a smart watch to provide orientation and location ground truth.
The orientation and location ground truth is accurately calibrated using techniques proposed in 'Real-time tracking of smartwatch orientation and location by multitask learning'.
This dataset contains magnetic distortion feature in many data traces, which means the magnetic field does not always point to the same direction (i.e., north) in global reference.
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The Unified Multimodal Network Intrusion Detection System (UM-NIDS) dataset is a comprehensive, standardized dataset that integrates network flow data, packet payload information, and contextual features, making it highly suitable for machine learning-based intrusion detection models. This dataset addresses key limitations in existing NIDS datasets, such as inconsistent feature sets and the lack of payload or time-window-based contextual features.
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This dataset contains LoRa physical layer signals collected from 60 LoRa devices and six SDRs (PLUTO-SDR, USRP B200 mini, USRP B210, USRP N210, RTL-SDR). It is intended for use by researchers in the development of a federated RFFI system, whereby the signals collected from different receivers and locations can be employed for evaluation purposes.
More details can be found at https://github.com/gxhen/federatedRFFI
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This dataset mentioned in the article "Environment Independent Gait Recognition Based on Wi-Fi Signals". This dataset was collected using a pair of Wi-Fi transceivers gathering channel state information of human walking, with the transmitter featuring an omnidirectional antenna and the receiver having three omnidirectional antennas. Data was collected in four indoor environments, where eight users walked along 24 directions. For specific environments and directions arrangements, please refer to the article. Each user walked ten times in each direction.
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The data used in this work is collected using the AirBox Sense system developed to detect six air pollutants, ambient temperature, and ambient relative humidity. The pollutants are Nitrogen Dioxide (NO2), surface Ozone (O3), Carbon Monoxide (CO), Sulphur Dioxide (SO2), Particulate Matter (PM2.5, and PM10). The sensors monitor these pollutants in real-time and store them in a cloud-based platform using a cellular module. Data are collected every 20 seconds, producing 4320 readings each day.
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This dataset presents real-world VPN encrypted traffic flows captured from five applications that belong to four service categories, which reflect typical usage patterns encountered by everyday users.
Our methodology utilized a set of automatic scripts to simulate real-world user interactions for these applications, to achieve a low level of noise and irrelevant network traffic.
The dataset consists of flow data collected from four service categories:
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Data Collection Period: Both datasets cover the period from July 1, 2022, to July 31, 2023. This one-year span captures a full cycle of seasonal variations, which are critical for understanding and forecasting air quality trends.
Data Characteristics
- Temporal Resolution: The data is recorded at 15-minute intervals, offering detailed temporal resolution.
- Missing Data: Both datasets contain missing values due to sensor malfunctions or communication issues. These missing values were handled using imputation techniques as part of the preprocessing phase.
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