IoT
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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441 Views
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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69 Views
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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210 Views
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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263 Views
NIMS BENIGN DATASET 2024-2 dataset comprises data captured from Consumer IoT devices, depicting three primary real-life states (Power-up, Idle, and Active) experienced by everyday users. Our setup focuses on capturing realistic data through these states, providing a comprehensive understanding of Consumer IoT devices.
The dataset comprises of nine popular IoT devices namely
Amcrest Camera
Smarter Coffeemaker
Ring Doorbell
Amazon Echodot
Google Nestcam
Google Nestmini
Kasa Powerstrip
Samsung 32 inch Smart Television (TV)
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54 Views
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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319 Views
Smart grids today are characterized by the integration of distributed energy resources, including renewable energy sources, traditional power sources, and storage systems. These components typically employ various control technologies that interface with generators through smart inverters, making them susceptible to numerous cyber threats. To address this vulnerability, there is a crucial need for datasets that document attacks on these systems, enabling risk evaluation and the development of effective monitoring algorithms. This dataset
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312 Views
This dataset is designed for the reconstruction of images of underground potato tubers using received signal strength (RSS) measurements collected by a ZigBee wireless sensor network. It includes RSS data from sensing areas of various sizes, environments with different layouts, and soils with varying moisture levels. The measurements were obtained from 9 potato tubers of differing sizes and shapes, which were buried in two distinct positions within the sensing area.
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188 Views
The security of Internet of Things (IoT) networks has become a major concern in recent years, as the number of connected objects continues to grow, thereby opening up more potential for malicious attacks. Supervised Machine Learning (ML) algorithms, which require a labeled dataset for training, are increasingly employed to detect attacks in IoT networks. However, existing datasets focus only on specific types of attacks, resulting in ML-based solutions that struggle to generalize effectively.
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266 Views
The "Aggressive Driving Behavior IoT Data" dataset captures real-time driving behaviour through a network of Internet of Things (IoT) devices, specifically designed to monitor and analyze aggressive driving patterns. This dataset contains comprehensive recordings from various sensors embedded in vehicles, including GPS, accelerometer, gyroscope, and onboard diagnostics (OBD) systems. The data points collected provide detailed insights into vehicle speed, acceleration, braking, steering patterns, and environmental conditions over time.
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926 Views