This dataset includes cyclist stress levels and a range of sensor readings capturing environmental conditions from both real-world traffic and a bike simulator environment. Data was collected using a smartphone-based sensor system, which recorded accelerometer, gyroscope, GPS, ambient light, and microphone data. Cyclists verbally reported their perceived stress levels at regular 5-second intervals, creating a labeled dataset for analyzing cyclist stress.
This dataset contains 535 recordings of heart and lung sounds captured using a digital stethoscope from a clinical manikin, including both individual and mixed recordings of heart and lung sounds; 50 heart sounds, 50 lung sounds, and 145 mixed sounds. For each mixed sound, the corresponding source heart sound (145 recordings) and source lung sound (145 recordings) were also recorded. It includes recordings from different anatomical chest locations, with normal and abnormal sounds.
This dataset supports the numerical experiments conducted in the paper "Optimization of Energy-Constrained Computing Task Assignment and Resource Allocation Problem." It provides simulation data for the optimization of energy-constrained computing task assignment and resource allocation in end-edge-cloud collaborative systems.
This dataset contains IoT-based soil data collected from various districts of Bangladesh, aimed at assessing soil quality for crop recommendation purposes. The data includes key soil parameters, such as nitrogen, phosphorus, potassium levels, soil conductivity, pH, humidity, and temperature, measured over multiple time intervals. The dataset includes a Soil Quality Index (SQI) and fuzzy classification categories, allowing for an in-depth soil fertility analysis.
This dataset, denoted as 𝑾_data, represents a synthetic yet structurally authentic warehouse management dataset comprising 4,132 records and 11 well-defined attributes. It was generated using the Gretel.ai platform, following the structural standards provided by the TI Supply Chain API–Storage Locations specification. The dataset encapsulates essential operational and spatial parameters of warehouses, including unique identifiers, geospatial coordinates, storage capacities, and categorical capacity statuses.
This dataset provides high-grade Received Signal Strength Indicator (RSSI) data collected from a set of experiments meant to estimate the number of drones present in a closed indoor space. The experiments are conducted varying the number of drones from one to seven, where all the variations in RSSI signal data are captured using a 5G transceiver setup established using Ettus E312 software-defined radio. There are seven files in the database, with a minimum of about 270 million samples.
The main goal of this research is to propose a realistic benchmark dataset to enable the development and evaluation of Internet of Medical Things (IoMT) security solutions. To accomplish this, 18 attacks were executed against an IoMT testbed composed of 40 IoMT devices (25 real devices and 15 simulated devices), considering the plurality of protocols used in healthcare (e.g., Wi-Fi, MQTT and Bluetooth).
This dataset contains user reviews about multiple consumer IoT devices.
Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response
This data provides a comprehensive collection of air quality and meteorological data from several large cities in India. With 1,410 records, it includes key characteristics like the Air Quality Index (AQI) according to both U.S. and China standards, temperature, atmospheric pressure, humidity, wind speed, wind direction, and timestamps.
